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CIRAD Madagascar organic resources ASD vis-NIR, native and XDS-standardized

cirad · NIR

CIRAD Madagascar organic resources ASD vis-NIR, native and XDS-standardized. Origin database composed from 2 standardized v2.0 leaves: 2 spectral source(s), 20 declared target(s). Auto-generated from dataset_card.json (verify before publication).

nirv2cirad
Original source available without a Dataverse token. The catalog uses the private tier for restricted or uncleared redistribution rights. You can obtain original resources from the source; their license still applies. Raw retrieval and automatic assembly are separate; use the loading example below.
1,000
samples
2,100
wavelengths
2
sources
20
targets
52
metadata
NIR
family

Provenance & citation

Where the original data and publications come from. Follow these links to obtain the source and cite its authors. Reading guide

ContributorSpectroscopic VIS-NIR analysis of organic resources from Madagascar
Conversion statuspartial
Origin · dataverse [open]10.18167/DVN1/SZSTBD — CIRAD Dataverse
Origin · dataverse [open]10.18167/DVN1/YRMQ52 — CIRAD Dataverse

Governance & integrity

Source license, redistribution conditions, content version and identity hashes. Redistribution tier and original-source availability are separate. Reading guide

Tierprivate
LicenseLicenseRef-not-cleared
Permitted useResearch and benchmarking; private use only.
Access policyManual download / private-use-only per source.
RedistributionCIRAD Dataverse API reports CC BY-NC-SA 4.0.
Content version1.1.0
Schema / protocol2.0
Content hash11ebd414d317c46e…
Processing hashbe169c9d5c8ebc7b…
Metadata hashaceaad661a34e029…

Dataset property explorer

Start with the profile axes to locate unusual variation, then inspect the measured values and evidence below. Scores are descriptive heuristics. Reading guide

Mean available profile score0.63
Highest axisLocal artifacts · 1.00
Diagnostics8
Sources profiled2

How to read the profile

The center is 0 and the outer ring is 1. Larger values indicate stronger anomaly or heterogeneity signals on that axis. Compare individual axes using the table; this is not an overall quality grade. Missing axes are left blank, and partial profiles have no filled polygon.

CIRAD Madagascar organic resources ASD vis-NIR, native and XDS-standardized property profile0.250.50.751integritynoiseartefactsbaselinePCA outliersreferencerepeatabilitystructureCIRAD Madagascar organic resources ASD vis-NIR, native and XDS-standardized — integrity: 0.00CIRAD Madagascar organic resources ASD vis-NIR, native and XDS-standardized — noise: 0.00CIRAD Madagascar organic resources ASD vis-NIR, native and XDS-standardized — artefacts: 1.00CIRAD Madagascar organic resources ASD vis-NIR, native and XDS-standardized — baseline: 0.61CIRAD Madagascar organic resources ASD vis-NIR, native and XDS-standardized — PCA outliers: 0.81CIRAD Madagascar organic resources ASD vis-NIR, native and XDS-standardized — reference: 1.00CIRAD Madagascar organic resources ASD vis-NIR, native and XDS-standardized — structure: 1.00CIRAD Madagasca…0 center · 1 outer ring · outward = stronger anomaly / heterogeneity signal

Profile axes

Dimensionless scores from 0 to 1 summarize each property; larger values indicate stronger signals. Unavailable axes are not zero.

Integrity
?Combines missing cells, zero columns and infinite values. Higher values call for checking data completeness.
0.00
Noise
?Combines roughness and its signal-to-roughness proxy. Real narrow bands may also contribute.
0.00
Local artifacts
?Combines spike, jump and clipping heuristics. Inspect flagged spectral regions before attributing a cause.
1.00
Baseline/shape
?Summarizes baseline slope and excess edge variation relative to signal scale.
0.61
PCA outliers
?Combines relative PCA residual and score-distance tails. Valid rare spectra can produce a high value.
0.81
Distance to average spectrum
?Dispersion around this dataset's mean spectrum, using distance, shape and shift summaries.
1.00
Repeatability
?Dispersion among observations sharing an ID. Interpretation depends on the documented meaning of that ID.
unavailable
Multi-regime structure
?Variation in local density in PCA space. May reflect legitimate batches or subpopulations.
1.00

Possible explanations to investigate

Bars rank heuristic signatures from 0 to 1. They do not estimate the probability of a physical fault. On narrow screens, scroll the chart horizontally. Full labels, scores, evidence coverage, possible causes and required external checks are in the table below.

Diagnostic hypotheses00.250.50.751hypothesis scoreSignature compatible with a spectral spliceSignature compatible with a spectral splice: 0.900.90Offset compatible with a different white referenceOffset compatible with a different white reference: 0.700.70Signature compatible with a population shiftSignature compatible with a population shift: 0.650.65Signature compatible with a different backgroundSignature compatible with a different background: 0.640.64Signature compatible with clippingSignature compatible with clipping: 0.410.41High spectral roughness signatureHigh spectral roughness signature: 0.400.40Flat spectrum/weak signalFlat spectrum/weak signal: 0.160.16

Heuristic signature scores are not calibrated probabilities of physical causes: 0.78 does not mean a 78% causal probability. Missing evidence remains unavailable. RMS/SAM describe dispersion around the dataset mean, without an independent external reference.

Diagnostic hypotheses and supporting evidence
DiagnosticScoreHeuristic strengthEvidence coverageSignalsPossible causeRequired external checks
Signature compatible with a spectral spliceX_xds_standardized0.90strong100 %Spike rate 1.00, Jump rate 1.00, RMS/SAM to mean spectrum 1.00Break at detector junctions, local calibration or different probe.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.
Offset compatible with a different white referenceX_xds_standardized0.70medium100 %RMS/SAM to mean spectrum 1.00, local artifacts 1.00, PCA Q 0.81Systematic discrepancy between campaigns, instruments or white reference.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.
Signature compatible with a population shiftX_native0.65medium100 %RMS/SAM to mean spectrum 1.00, PCA structure 1.00, Mahalanobis / T2 0.77Different but physically plausible variety, species, batch or condition.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.
Signature compatible with a different backgroundX_xds_standardized0.64medium100 %RMS/SAM to mean spectrum 1.00, PCA Q 0.81, Mahalanobis / T2 0.77Systematic effect of support, white/black, transflectance or measurement environment.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.
Signature compatible with clippingX_xds_standardized0.41weak100 %Jump rate 1.00, PCA Q 0.81, Baseline/mean/area 0.63Detector saturated or insufficient dynamic range.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.
High spectral roughness signatureX_xds_standardized0.40weak100 %Spike rate 1.00, RMS/SAM to mean spectrum 1.00, PCA Q 0.81Weak signal, detector problem, lamp or unstable acquisition.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.
Flat spectrum/weak signalX_xds_standardized0.16weak100 %PCA Q 0.81Bad contact, missing sample, degraded measurement or very low dynamic range.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.
Weak signatures observedX_native—unknown90 %Weak signatures on available metrics; does not certify either physical validity or the absence of defects.Check the white/black reference actually acquired and its metadata.
Check instrument, probe, geometry, campaign and processing/resampling log.
Check the repeat protocol before interpreting an intra-ID dispersion.

Spectral sources

Each block is a separate instrument or spectral source. Read its signal summary before using PCA to explore variation between observations. Reading guide

Source coverage
SourceObservationsSample units
ASD non-standardized absorbance1,0001,000
ASD standardized on XDS absorbance1,0001,000

1,000 sample units are measured by every source; 0 by a single source. Sources are joined by declared sample identity, never by row position.

ASD non-standardized absorbance

X_native · NIR · ASD

Read the signal and variation summaries for this source. Axis unit: nm; signal representation: absorbance.

Spectral signal summary

The mean and median describe the typical signal at each channel; shaded bands show the 25th–75th and 5th–95th percentiles across observations. A wider band means greater variation, which can be biological or instrumental.

ASD non-standardized absorbance spectra0.00.51.01.501,0002,0003,000q05-q95 envelopeq25-q75 envelopemedian spectrummedianq25–q75q05–q95wavelength / nm400nm — median 1.017 (q25–q75 0.9205–1.204)416nm — median 0.9888 (q25–q75 0.8921–1.191)430nm — median 0.9606 (q25–q75 0.8621–1.171)446nm — median 0.9247 (q25–q75 0.8256–1.135)460nm — median 0.8953 (q25–q75 0.7974–1.1)476nm — median 0.8639 (q25–q75 0.7629–1.062)490nm — median 0.8375 (q25–q75 0.737–1.034)506nm — median 0.8084 (q25–q75 0.707–1.006)520nm — median 0.7821 (q25–q75 0.6849–0.982)536nm — median 0.7554 (q25–q75 0.6578–0.9533)550nm — median 0.7295 (q25–q75 0.635–0.9267)566nm — median 0.704 (q25–q75 0.6124–0.9019)582nm — median 0.6796 (q25–q75 0.5914–0.8771)596nm — median 0.6611 (q25–q75 0.5736–0.8575)612nm — median 0.6364 (q25–q75 0.552–0.8284)626nm — median 0.6151 (q25–q75 0.5333–0.7958)642nm — median 0.5911 (q25–q75 0.511–0.7723)656nm — median 0.5749 (q25–q75 0.4918–0.7659)672nm — median 0.552 (q25–q75 0.4664–0.7474)686nm — median 0.5238 (q25–q75 0.4407–0.6999)702nm — median 0.4974 (q25–q75 0.4151–0.6512)716nm — median 0.4726 (q25–q75 0.3935–0.6193)732nm — median 0.449 (q25–q75 0.369–0.5823)748nm — median 0.4251 (q25–q75 0.3466–0.5517)762nm — median 0.4039 (q25–q75 0.3279–0.5293)778nm — median 0.3813 (q25–q75 0.3075–0.5052)792nm — median 0.3623 (q25–q75 0.29–0.4851)808nm — median 0.3425 (q25–q75 0.2719–0.4611)822nm — median 0.326 (q25–q75 0.2578–0.4411)838nm — median 0.3092 (q25–q75 0.243–0.4214)852nm — median 0.2966 (q25–q75 0.2313–0.404)868nm — median 0.2831 (q25–q75 0.2184–0.3847)882nm — median 0.2714 (q25–q75 0.2081–0.3716)898nm — median 0.2594 (q25–q75 0.1974–0.3563)914nm — median 0.2479 (q25–q75 0.188–0.342)928nm — median 0.2395 (q25–q75 0.1806–0.3306)944nm — median 0.2298 (q25–q75 0.1725–0.3168)958nm — median 0.2232 (q25–q75 0.1669–0.3084)974nm — median 0.2146 (q25–q75 0.1606–0.2997)988nm — median 0.2085 (q25–q75 0.1557–0.2939)1,004nm — median 0.2105 (q25–q75 0.1581–0.2927)1,018nm — median 0.2046 (q25–q75 0.1533–0.2858)1,034nm — median 0.199 (q25–q75 0.1485–0.2785)1,050nm — median 0.1935 (q25–q75 0.1439–0.2722)1,064nm — median 0.1886 (q25–q75 0.1404–0.2659)1,080nm — median 0.1841 (q25–q75 0.1365–0.2599)1,094nm — median 0.1798 (q25–q75 0.1333–0.2544)1,110nm — median 0.1757 (q25–q75 0.1301–0.2486)1,124nm — median 0.173 (q25–q75 0.1281–0.2442)1,140nm — median 0.1706 (q25–q75 0.1275–0.2408)1,154nm — median 0.1695 (q25–q75 0.1276–0.2395)1,170nm — median 0.1681 (q25–q75 0.1283–0.2362)1,184nm — median 0.1669 (q25–q75 0.1285–0.2343)1,200nm — median 0.1649 (q25–q75 0.1278–0.23)1,216nm — median 0.1616 (q25–q75 0.1256–0.2268)1,230nm — median 0.1584 (q25–q75 0.1223–0.2236)1,246nm — median 0.1552 (q25–q75 0.119–0.2204)1,260nm — median 0.1529 (q25–q75 0.1167–0.218)1,276nm — median 0.1508 (q25–q75 0.1153–0.2159)1,290nm — median 0.149 (q25–q75 0.114–0.214)1,306nm — median 0.1474 (q25–q75 0.1133–0.2122)1,320nm — median 0.148 (q25–q75 0.1142–0.2117)1,336nm — median 0.1495 (q25–q75 0.1172–0.2116)1,350nm — median 0.1525 (q25–q75 0.1229–0.2135)1,366nm — median 0.1565 (q25–q75 0.1289–0.2168)1,382nm — median 0.1637 (q25–q75 0.1378–0.2227)1,396nm — median 0.1808 (q25–q75 0.1539–0.2332)1,412nm — median 0.2081 (q25–q75 0.1801–0.2564)1,426nm — median 0.2244 (q25–q75 0.1955–0.2691)1,442nm — median 0.2333 (q25–q75 0.2042–0.2787)1,456nm — median 0.2363 (q25–q75 0.2065–0.2824)1,472nm — median 0.2361 (q25–q75 0.2063–0.2825)1,486nm — median 0.2331 (q25–q75 0.2044–0.2788)1,502nm — median 0.2287 (q25–q75 0.2004–0.27)1,516nm — median 0.2246 (q25–q75 0.1962–0.2637)1,532nm — median 0.2195 (q25–q75 0.1922–0.2581)1,548nm — median 0.2168 (q25–q75 0.1897–0.2555)1,562nm — median 0.2158 (q25–q75 0.1895–0.2543)1,578nm — median 0.2145 (q25–q75 0.1884–0.2536)1,592nm — median 0.2125 (q25–q75 0.1865–0.2505)1,608nm — median 0.2073 (q25–q75 0.1831–0.248)1,622nm — median 0.2038 (q25–q75 0.1804–0.2444)1,638nm — median 0.2023 (q25–q75 0.1787–0.2428)1,652nm — median 0.2022 (q25–q75 0.1789–0.2429)1,668nm — median 0.2055 (q25–q75 0.1826–0.2477)1,682nm — median 0.2119 (q25–q75 0.1882–0.253)1,698nm — median 0.2192 (q25–q75 0.1944–0.2615)1,714nm — median 0.2252 (q25–q75 0.2006–0.2688)1,728nm — median 0.2304 (q25–q75 0.2054–0.2745)1,744nm — median 0.2299 (q25–q75 0.205–0.2752)1,758nm — median 0.2323 (q25–q75 0.2067–0.2769)1,774nm — median 0.2329 (q25–q75 0.2069–0.2776)1,788nm — median 0.233 (q25–q75 0.2067–0.2771)1,804nm — median 0.2322 (q25–q75 0.2056–0.2769)1,818nm — median 0.2309 (q25–q75 0.2042–0.2754)1,834nm — median 0.231 (q25–q75 0.2034–0.2747)1,848nm — median 0.2294 (q25–q75 0.202–0.2739)1,864nm — median 0.235 (q25–q75 0.207–0.2791)1,880nm — median 0.2595 (q25–q75 0.2297–0.3074)1,894nm — median 0.3112 (q25–q75 0.2812–0.3663)1,910nm — median 0.3816 (q25–q75 0.3512–0.441)1,924nm — median 0.4101 (q25–q75 0.3792–0.473)1,940nm — median 0.4106 (q25–q75 0.3782–0.4752)1,954nm — median 0.3957 (q25–q75 0.3642–0.4593)1,970nm — median 0.3766 (q25–q75 0.3465–0.4385)1,984nm — median 0.3607 (q25–q75 0.331–0.4214)2,000nm — median 0.3445 (q25–q75 0.314–0.4025)2,016nm — median 0.3348 (q25–q75 0.3048–0.393)2,030nm — median 0.3365 (q25–q75 0.3035–0.3939)2,046nm — median 0.3445 (q25–q75 0.3085–0.4016)2,060nm — median 0.3506 (q25–q75 0.3133–0.4061)2,076nm — median 0.3512 (q25–q75 0.3144–0.4097)2,090nm — median 0.3516 (q25–q75 0.3131–0.4089)2,106nm — median 0.3525 (q25–q75 0.3125–0.41)2,120nm — median 0.3521 (q25–q75 0.3107–0.4109)2,136nm — median 0.3518 (q25–q75 0.311–0.411)2,150nm — median 0.3537 (q25–q75 0.3145–0.4113)2,166nm — median 0.3575 (q25–q75 0.3181–0.4136)2,182nm — median 0.3567 (q25–q75 0.3182–0.41)2,196nm — median 0.354 (q25–q75 0.3177–0.4054)2,212nm — median 0.3518 (q25–q75 0.3167–0.4018)2,226nm — median 0.3467 (q25–q75 0.3118–0.3971)2,242nm — median 0.357 (q25–q75 0.3234–0.4082)2,256nm — median 0.3762 (q25–q75 0.3418–0.4273)2,272nm — median 0.3943 (q25–q75 0.3579–0.4472)2,286nm — median 0.399 (q25–q75 0.3627–0.4536)2,302nm — median 0.4093 (q25–q75 0.3701–0.4604)2,316nm — median 0.414 (q25–q75 0.3751–0.4653)2,332nm — median 0.4111 (q25–q75 0.3733–0.4648)2,348nm — median 0.4157 (q25–q75 0.3768–0.4688)2,362nm — median 0.413 (q25–q75 0.3757–0.4667)2,378nm — median 0.4151 (q25–q75 0.3776–0.4673)2,392nm — median 0.4193 (q25–q75 0.3812–0.4708)2,408nm — median 0.427 (q25–q75 0.3875–0.4799)2,422nm — median 0.44 (q25–q75 0.3998–0.494)2,438nm — median 0.4612 (q25–q75 0.4192–0.5159)2,452nm — median 0.4808 (q25–q75 0.4385–0.5373)2,468nm — median 0.5048 (q25–q75 0.46–0.5623)2,482nm — median 0.5192 (q25–q75 0.4735–0.5776)2,498nm — median 0.5256 (q25–q75 0.4802–0.5862)

Sampling

How many observations and spectral channels were exported, and how the wavelength axis is spaced. Reading guide

Wavelengths
?Number of spectral channels. A channel can represent wavelength, wavenumber or another feature axis.
1,050
Axis range
?Lowest and highest recorded spectral-axis coordinates, in the stated unit.
400–2,498 nm
Mean spacing
?Average difference between adjacent spectral-axis coordinates; compare only within the same unit.
2 nm
Grid
?Uniform means adjacent axis spacings are approximately equal. An irregular grid may need care when applying derivatives.
uniform
Observations
?Exported spectral rows in this source, including repeated measurements when present.
1,000

Signal & quality

Signal level and roughness summaries. These depend on the signal representation and are not instrument acceptance tests. Reading guide

Value range
?Minimum and maximum recorded spectral values, in the original signal representation.
0.0408 – 1.51
Mean range
?Minimum and maximum of the mean spectrum across spectral channels.
0.21 – 1.04
Mean level
?Average signal over observations and channels. The historical key mean_reflectance may also store other signal representations.
0.3934
Area
?Trapezoidal integral of the mean spectrum over the sorted recorded axis; its unit is signal multiplied by axis unit.
824.5
PTP
?Peak-to-peak: maximum minus minimum of the mean spectrum. Low values indicate little variation along the mean spectrum.
0.8334
Noise RMS
?Historical roughness sigma proxy from robust second differences. Narrow genuine bands can also raise it; this is not repeat-measured instrument noise.
6.023e-05
Roughness S/N proxy
?Average absolute signal divided by the roughness proxy. Higher means smoother signal relative to its amplitude, not certified instrument SNR.
6.5e+03
Roughness S/N proxy dB
?20 log10 of the roughness signal-to-noise proxy. Units are decibels; compare only compatible signal representations.
8e+01 dB
Dynamic range
?Maximum minus minimum of the mean spectrum. Its magnitude depends on the signal representation.
0.833
Smoothness
?Mean per-spectrum RMS of second differences, in signal units. Smaller values indicate smoother spectra under this protocol.
0.001071
Saturated
?Fraction at the observed global maximum; a heuristic indicator, not a comparison with known detector limits.
0.0%
X-outliers
?Number flagged by the qualification outlier method. An unusual spectrum can be valid and should not be discarded automatically.
498

Integrity & artefacts

Missing values, narrow spikes and abrupt jumps to investigate in the original spectra. Reading guide

NaN ratio
?Missing spectral cells divided by all spectral cells, displayed as a percentage.
0.00%
Inf count
?Number of positive or negative infinite spectral values; investigate invalid calculations or export.
0
Zero ratio
?Exactly zero spectral cells divided by all cells. Zero can be a valid measurement and is not automatically missing.
0.00%
Spike count
?Local narrow peaks flagged by the second-difference heuristic; real sharp spectral bands may also be flagged.
43,243
Spike rate
?Fraction of tested second-difference positions flagged as spikes, displayed as a percentage.
4.13%
Jump count
?Adjacent-channel changes flagged by the first-difference heuristic, potentially including detector joins.
7,718
Jump rate
?Fraction of tested adjacent-channel differences flagged as jumps, displayed as a percentage.
0.74%
Clip fraction
?Fraction of finite cells near the observed global minimum or maximum; inspect detector limits before concluding saturation.
0.00%

Shape & reference

Variation relative to this dataset's mean spectrum; the mean is not an independently measured calibration reference. Reading guide

Baseline slope
?Linear slope fitted to the mean spectrum over an axis normalized to 0–1. It describes total baseline change, not change per physical axis unit.
-0.24326
Curvature RMS
?Median per-spectrum RMS of second differences, summarizing curvature and high-frequency variation.
0.0008837
D1 RMS
?Median per-spectrum RMS of adjacent-channel first differences; reflects both real spectral structure and artefacts.
0.0021995
RMS to mean
?Root mean square distance to this dataset's mean spectrum, in signal units.
0.095029
RMS p95
?95th percentile of distances to the mean spectrum: 95% of observations have a distance at or below this value.
0.34203
SAM to mean
?Spectral Angle Mapper: angle to the mean spectrum, in radians. Smaller angles indicate more similar shapes.
0.15501
SAM p95
?95th percentile of spectral angles to the dataset mean, in radians. It is less sensitive to overall amplitude than RMS.
0.34336
Affine offset p95
?95th percentile of absolute fitted offsets when matching each spectrum to the dataset mean.
0.46644
Affine gain p95 Δ
?95th percentile of the absolute gain deviation from 1 in the fit to the mean spectrum.
0.52421
Affine residual p95
?95th percentile of residual RMS after gain and offset correction to the mean spectrum.
0.091259
Xcorr lag p95
?95th percentile of the absolute cross-correlation shift relative to the mean spectrum, in spectral channels.
0

Outliers & repeatability

Unusual spectra and within-ID dispersion. Repeated IDs do not by themselves establish technical repetitions. Reading guide

PCA Q p95/median
?Q (squared prediction error) is variation left outside the retained PCA subspace. The displayed ratio compares its 95th percentile with its median.
6.4
Hotelling T2 p95/median
?Hotelling T² measures distance within the retained PCA subspace, scaled by component variance. Ratio of the 95th percentile to the median.
6.2
Mahalanobis H p95/median
?Distance from the PCA-score center after variance scaling. Ratio of the 95th percentile to the median; large values identify relatively extreme spectra.
2.5
Repeat groups
?Number of IDs with several observations. Source documentation is needed to decide whether these are technical repeats, positions or time points.
0

Dimensionality (PCA)

Principal component analysis (PCA) compresses correlated wavelengths into components. This describes spectral variation, without using prediction targets. Reading guide

Effective rank
?Entropy-based summary of the variance distribution across components. Larger values indicate variation spread across more dimensions.
1.9
PCs → 95% var
?Number of principal components needed to explain 95% of spectral variance, when reached by the computed projection.
2
PCs → 99% var
?Number of principal components needed to explain 99% of spectral variance, when reached by the computed projection.
3
Top-10 cum. var
?Fraction of spectral variance explained by up to the first ten computed principal components, displayed as a percentage.
100.0%
Computed metric scores 30highest alert 1.00

Value is the measured quantity; alert score is its heuristic mapping to 0–1. Levels are weak below 0.42, medium from 0.42, and strong from 0.72. These are display bands, not calibrated acceptance thresholds. A dash means unavailable, never zero. Possible causes are suggestions to investigate. Use each metric’s ? control for its meaning and scoring: hover for a hint, or expand it with a tap, Enter or Space.

FamilyComputed metricValueAlert scoreHeuristic levelDescriptive interpretationPossible causes to verifyCalculation / scoring
Data integrityInvalid/masked cell count (owner report)
?Count of cells reported unavailable by the native owner under its null or validity mask. This count is distinct from the historical NaN ratio; consult the recorded population and denominator. Scoring: No alert mapping is defined for this metric; an unavailable score does not mean a zero measured value. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
integrity.missing_cells		——not calculableNot enough information to score this metric on this dataset.Missing data or validity mask; not evidence of an acquisition causeNative Preflight count of cells unavailable under x/null and valid_mask; denominator stays in native_metric.denominators.n_cells. Not legacy NaN ratio.
Data integrityNaN ratio
?Missing spectral cells divided by all spectral cells, displayed as a percentage. Scoring: alert = min(1, nan_ratio / 0.05). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
integrity.nan_ratio		0%0.00weakFull spectrumAcquisition/export errorcount(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Data integrityInf count
?Number of positive or negative infinite spectral values; investigate invalid calculations or export. Scoring: alert = min(1, inf_count / 1). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
integrity.inf_count		00.00weakNormalInvalid calculationscount(isinf(X))alert = min(1, inf_count / 1)
Data integrityZero ratio
?Exactly zero spectral cells divided by all cells. Zero can be a valid measurement and is not automatically missing. Scoring: alert = min(1, zero_ratio / 0.05). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
integrity.zero_ratio		0%0.00weakNormalExport, saturationcount(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Overall amplitudeMean reflectance
?Average signal over observations and channels. The historical key mean_reflectance may also store other signal representations. Scoring: alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
amplitude.mean_reflectance		0.393360.58mediumAtypical value: Too light / background visible or Too darkBackground, geometrymean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Overall amplitudeArea under curve
?Trapezoidal integral of the mean spectrum over the sorted recorded axis; its unit is signal multiplied by axis unit. Scoring: alert reuses baseline/shape drift because area scale depends on axis and units. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
amplitude.area_under_curve		824.460.58mediumAtypical value: Illumination difference or NormalProbe distancetrapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Overall amplitudePeak-to-peak (PTP)
?Peak-to-peak: maximum minus minimum of the mean spectrum. Low values indicate little variation along the mean spectrum. Scoring: alert increases when dynamic range is abnormally flat. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
amplitude.peak_to_peak		0.833350.00weakStrong variabilitySaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Overall amplitudeVariance
?Variance of all finite spectral cells, in squared signal units. It combines variation across observations and channels; a small value can describe a flat signal. Scoring: alert increases when variance/dynamic range is abnormally flat. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
amplitude.variance		0.0576980.00weakNormal or heterogeneousBad contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
NoiseRoughness sigma proxy
?Historical roughness sigma proxy from robust second differences. Narrow genuine bands can also raise it; this is not repeat-measured instrument noise. Scoring: alert = noise_rms / signal_scale, saturated at 5%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
noise.roughness_sigma_proxy		6.023e-050.00weakStableLamp, detectormedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
NoiseRoughness S/N proxy
?Average absolute signal divided by the roughness proxy. Higher means smoother signal relative to its amplitude, not certified instrument SNR. Scoring: alert decreases with SNR dB; >=40 dB is treated as low alert. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
noise.roughness_signal_to_noise_proxy		6530.90.00weakGood signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
NoiseBandwise roughness S/N proxy
?Minimum channel-wise ratio of absolute mean signal to local second-difference roughness. A smaller ratio identifies a weak or rough spectral region; genuine narrow bands and near-zero signal can also lower it. This is not certified instrument SNR. Scoring: alert decreases with worst-band SNR dB; >=35 dB is treated as low alert. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
noise.bandwise_roughness_signal_to_noise_proxy_min		272.630.00weakReliable zoneDetectormin(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Local artifactsSpike count
?Local narrow peaks flagged by the second-difference heuristic; real sharp spectral bands may also be flagged. Scoring: alert follows spike_rate, saturated at 1%. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.spike_count		43,2431.00strongArtifactsCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Local artifactsSpike rate
?Fraction of tested second-difference positions flagged as spikes, displayed as a percentage. Scoring: alert = min(1, spike_rate / 0.01). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.spike_rate		4.13%1.00strongSuspicious spectrumInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Local artifactsJump count
?Adjacent-channel changes flagged by the first-difference heuristic, potentially including detector joins. Scoring: alert follows jump_rate, saturated at 1%. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.jump_count		7,7180.74strongDetector connectionSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Local artifactsJump rate
?Fraction of tested adjacent-channel differences flagged as jumps, displayed as a percentage. Scoring: alert = min(1, jump_rate / 0.01). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.jump_rate		0.736%0.74strongSpectral problemCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Local artifactsClip fraction
?Fraction of finite cells near the observed global minimum or maximum; inspect detector limits before concluding saturation. Scoring: alert = min(1, clip_fraction / 0.01). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.clip_fraction		0.00019%0.00weakNormalDetector saturatedfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Spectral formBaseline slope
?Linear slope fitted to the mean spectrum over an axis normalized to 0–1. It describes total baseline change, not change per physical axis unit. Scoring: alert = abs(slope / signal_scale), saturated at 0.5. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
shape.baseline_slope		-0.243260.58mediumDriftIlluminationlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Spectral formCurvature RMS
?Median per-spectrum RMS of second differences, summarizing curvature and high-frequency variation. Scoring: alert = curvature_rms / signal_scale, saturated at 1%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
shape.curvature_rms		0.00088370.11weakSmoothBackground, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Spectral formD1 RMS
?Median per-spectrum RMS of adjacent-channel first differences; reflects both real spectral structure and artefacts. Scoring: alert = d1_rms / signal_scale, saturated at 5%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
shape.d1_rms		0.00219950.05weakFlatBiology or artifactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Multivariate outliersPCA Q (SPE)
?Q (squared prediction error) is variation left outside the retained PCA subspace. The displayed ratio compares its 95th percentile with its median. Scoring: alert = min(1, pca_q_ratio / 8). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
outliers.pca_q_ratio		6.38690.80strongAtypical spectrumArtifact, mixturep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Multivariate outliersHotelling T²
?Hotelling T² measures distance within the retained PCA subspace, scaled by component variance. Ratio of the 95th percentile to the median. Scoring: alert = min(1, hotelling_t2_ratio / 8). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
outliers.hotelling_t2_ratio		6.17810.77strongExtreme but consistentNatural variabilityp95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Multivariate outliersMahalanobis H
?Distance from the PCA-score center after variance scaling. Ratio of the 95th percentile to the median; large values identify relatively extreme spectra. Scoring: alert = min(1, mahalanobis_h_ratio / 4). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
outliers.mahalanobis_h_ratio		2.48560.62mediumGlobal outlierDifferent domainp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparison to average spectrumRMS to mean spectrum
?95th percentile of distances to the mean spectrum: 95% of observations have a distance at or below this value. Scoring: alert = RMS_p95 / signal_scale, saturated at 25%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
reference.rms_to_mean_spectrum_p95		0.342031.00strongDifferent spectrumDomain shiftp95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparison to average spectrumSpectral Angle Mapper (SAM)
?95th percentile of spectral angles to the dataset mean, in radians. It is less sensitive to overall amplitude than RMS. Scoring: alert = min(1, SAM_p95 / 0.35 rad). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
reference.sam_to_mean_spectrum_p95		0.343360.98strongDifferent shapeBackground, geometryp95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RepeatabilityRMS intra-ID
?Within-ID root mean square deviation from the group's mean spectrum, in signal units. Repeated IDs require documentation before this spread can be called technical repeatability. Scoring: alert = RMS_intra_ID / signal_scale, saturated at 10%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
repeatability.rms_intra_id		——not calculableNot enough information to score this metric on this dataset.Positioningmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RepeatabilitySAM intra-ID
?Within-ID spectral angle to the group's mean spectrum, in radians. Repeated IDs require documentation before this spread can be called technical repeatability. Scoring: alert = min(1, SAM_intra_ID / 0.15 rad). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
repeatability.sam_intra_id		——not calculableNot enough information to score this metric on this dataset.Acquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RepeatabilityCV intra-ID
?Within-ID coefficient of variation: standard deviation relative to absolute mean; unstable near zero signal. Repeated IDs require documentation before this spread can be called technical repeatability. Scoring: alert = min(1, CV_intra_ID / 0.25). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
repeatability.cv_intra_id		——not calculableNot enough information to score this metric on this dataset.Operatormedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure of the datasetPCA score density
?Inverse median distance to nearby observations in retained PCA-score space. Larger raw values mean more closely spaced observations; scale depends on the PCA representation. The alert uses density variation, so raw density alone does not establish an anomaly. Scoring: alert follows density_cv/profile structure complexity, not raw density alone. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
structure.pca_score_density		2.4231.00strongSubpopulationsDifferent batches1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure of the datasetkNN distance ratio (not LOF)
?95th percentile of mean k-nearest-neighbor distance divided by the population median of that distance, in PCA-score space. Values above 1 indicate an upper tail with more isolated observations; valid rare spectra can also be isolated. This distance ratio is not the Local Outlier Factor algorithm. Scoring: alert = min(1, max(0, kNN_distance_ratio_p95 - 1) / 2). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
structure.knn_distance_ratio_p95		3.21391.00strongIsolated spectrumRare casesp95(mean kNN distance / median population mean kNN distance) in PCA-score space; not LOFalert = min(1, max(0, kNN_distance_ratio_p95 - 1) / 2)
Structure of the datasetIsolation Forest score
?95th percentile of the negated Isolation Forest score in retained PCA-score space. Larger raw scores indicate easier isolation under that model; the scale is implementation-dependent. The displayed alert reuses structure complexity rather than directly mapping this raw score. Scoring: alert follows structure complexity; raw score is implementation-dependent. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
structure.isolation_forest_score_p95		0.589911.00strongAtypical spectrumVarious causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent

PCA score plot — explore spectral variation

Source ASD non-standardized absorbance: 800 of 1,000 source observations are plotted. PC1 captures 79.2% and PC2 15.5% of the reported spectral variance: 94.7% together; 5.3% remains outside this view.

Principal component analysis (PCA) summarizes spectral differences without using targets. The horizontal PC1 axis captures the largest variation; vertical PC2 captures the next largest direction of variation. Each dot is one observation; repeated measurements need not be distinct samples. Scores are projection coordinates, not wavelengths or target values. Nearby dots have similar projections; separate groups or isolated dots are leads to inspect in the original spectra and acquisition metadata. Overlapping dots can differ in omitted dimensions. The dots have no class or batch labels, so this view cannot identify groups, confirm defective observations, explain a chemical cause or measure prediction accuracy.

ASD non-standardized absorbance PCA score plot-30-20-10010-5.0-2.50.02.55.0PC1 2.534 · PC2 0.1465PC1 0.1044 · PC2 2.502PC1 -0.7977 · PC2 2.808PC1 1.328 · PC2 1.293PC1 1.647 · PC2 -0.8553PC1 2.652 · PC2 -0.987PC1 2.861 · PC2 -0.9966PC1 1.292 · PC2 -0.06671PC1 1.735 · PC2 -0.1708PC1 1.722 · PC2 -0.243PC1 1.486 · PC2 -0.5849PC1 1.207 · PC2 0.1349PC1 2.104 · PC2 -0.2191PC1 0.1691 · PC2 0.3571PC1 1.226 · PC2 -0.3156PC1 0.1508 · PC2 0.608PC1 0.8107 · PC2 0.6025PC1 1.957 · PC2 -0.2007PC1 0.06986 · PC2 -0.09172PC1 1.703 · PC2 -0.0677PC1 0.4454 · PC2 0.1058PC1 -1.826 · PC2 -1.52PC1 1.222 · PC2 -0.08965PC1 1.678 · PC2 -0.1597PC1 1.939 · PC2 -0.7093PC1 0.7542 · PC2 0.1046PC1 0.9274 · PC2 -0.3139PC1 1.922 · PC2 -0.2767PC1 0.1307 · PC2 -1.276PC1 2.002 · PC2 -0.9447PC1 1.794 · PC2 0.003428PC1 1.763 · PC2 0.4528PC1 1.364 · PC2 0.3913PC1 0.6321 · PC2 -0.5307PC1 0.7556 · PC2 -0.4762PC1 1.534 · PC2 -0.6733PC1 -1.035 · PC2 0.2111PC1 2.928 · PC2 -1.807PC1 0.4597 · PC2 -1.279PC1 0.7418 · PC2 -0.5474PC1 2.601 · PC2 -1.132PC1 2.427 · PC2 -0.9099PC1 2.512 · PC2 -0.3197PC1 0.06519 · PC2 -1.147PC1 0.5873 · PC2 -1.072PC1 -0.2914 · PC2 -0.4376PC1 1.372 · PC2 -0.7347PC1 1.531 · PC2 -1.251PC1 -0.373 · PC2 -1.608PC1 1.835 · PC2 -0.9142PC1 0.5704 · PC2 -1.125PC1 1.924 · PC2 -1.488PC1 1.881 · PC2 -0.9792PC1 3.577 · PC2 -2.521PC1 0.2606 · PC2 -2.23PC1 3.134 · PC2 -1.591PC1 -12.4 · PC2 -4.738PC1 0.9288 · PC2 -1.624PC1 -0.3032 · PC2 -0.7946PC1 1.014 · PC2 0.1707PC1 0.1743 · PC2 -0.9768PC1 0.6953 · PC2 -1.564PC1 1.264 · PC2 -1.144PC1 -5.629 · PC2 -2.645PC1 1.465 · PC2 -1.508PC1 1.668 · PC2 -1.726PC1 -1.578 · PC2 -1.46PC1 1.33 · PC2 -0.7098PC1 2.509 · PC2 -0.1686PC1 0.08264 · PC2 -1.414PC1 2.899 · PC2 -1.313PC1 2.881 · PC2 -1.094PC1 -14.24 · PC2 -2.176PC1 -14.45 · PC2 -2.555PC1 1.908 · PC2 -1.571PC1 2.253 · PC2 -0.7884PC1 -0.4317 · PC2 -1.999PC1 3.31 · PC2 -1.611PC1 0.2711 · PC2 -0.45PC1 2.154 · PC2 -0.5971PC1 2.295 · PC2 -0.8013PC1 -0.08484 · PC2 -0.3165PC1 0.139 · PC2 0.3449PC1 1.782 · PC2 0.0267PC1 0.3522 · PC2 0.03247PC1 -1.231 · PC2 -0.1864PC1 1.276 · PC2 -0.1479PC1 -0.5849 · PC2 0.05785PC1 -0.3929 · PC2 -0.537PC1 1.089 · PC2 -0.3571PC1 0.2548 · PC2 -0.7855PC1 -0.3613 · PC2 -0.1439PC1 1.225 · PC2 -0.7354PC1 1.358 · PC2 -0.7056PC1 1.267 · PC2 -0.6858PC1 2.077 · PC2 -0.5861PC1 2.017 · PC2 -0.3743PC1 1.021 · PC2 -1.981PC1 0.5477 · PC2 -0.1695PC1 1.775 · PC2 -0.6561PC1 0.615 · PC2 -0.4139PC1 0.9086 · PC2 -0.1005PC1 0.4572 · PC2 -0.1338PC1 0.4546 · PC2 -0.4769PC1 2.016 · PC2 -0.3672PC1 2.27 · PC2 -1.505PC1 3.113 · PC2 -1.58PC1 3.058 · PC2 -1.934PC1 3.264 · PC2 -1.553PC1 1.588 · PC2 -0.4561PC1 2.419 · PC2 -1.558PC1 1.438 · PC2 -0.7126PC1 2.125 · PC2 -1.074PC1 3.604 · PC2 -2.178PC1 3.847 · PC2 -1.857PC1 3.832 · PC2 -1.938PC1 1.977 · PC2 -0.6661PC1 1.234 · PC2 0.02881PC1 0.5008 · PC2 1.918PC1 0.7344 · PC2 0.5858PC1 -0.421 · PC2 0.6543PC1 0.3141 · PC2 0.68PC1 0.6728 · PC2 2.351PC1 1.297 · PC2 1.52PC1 0.109 · PC2 2.495PC1 -0.6619 · PC2 2.827PC1 1.516 · PC2 1.311PC1 2.356 · PC2 0.002413PC1 0.08581 · PC2 1.504PC1 -0.5436 · PC2 2.013PC1 -1.37 · PC2 2.262PC1 0.9615 · PC2 0.1852PC1 1.511 · PC2 0.07953PC1 0.3457 · PC2 1.049PC1 0.8075 · PC2 0.353PC1 0.5968 · PC2 0.909PC1 1.101 · PC2 0.6677PC1 1.56 · PC2 -0.2122PC1 1.182 · PC2 0.2197PC1 0.4757 · PC2 0.9151PC1 2.543 · PC2 -1.524PC1 3.185 · PC2 -0.8744PC1 2.385 · PC2 -0.245PC1 3.127 · PC2 -0.703PC1 3.531 · PC2 -0.863PC1 2.536 · PC2 -1.064PC1 2.064 · PC2 -0.3208PC1 3.59 · PC2 -2.057PC1 3.001 · PC2 -1.585PC1 3.264 · PC2 -1.785PC1 0.8636 · PC2 1.697PC1 1.687 · PC2 0.6226PC1 0.802 · PC2 1.795PC1 2.363 · PC2 -0.09582PC1 2.142 · PC2 -0.4394PC1 2.077 · PC2 0.2998PC1 1.95 · PC2 0.1757PC1 2.169 · PC2 0.2427PC1 1.927 · PC2 0.2466PC1 2.761 · PC2 0.01092PC1 2.694 · PC2 0.04504PC1 2.546 · PC2 0.2453PC1 1.759 · PC2 0.4605PC1 1.904 · PC2 0.2937PC1 1.662 · PC2 0.7212PC1 2.208 · PC2 0.6051PC1 3.055 · PC2 -0.4096PC1 2.319 · PC2 0.2332PC1 2.479 · PC2 0.0717PC1 1.93 · PC2 0.06251PC1 1.84 · PC2 0.05025PC1 1.873 · PC2 0.1151PC1 -0.5859 · PC2 0.5793PC1 0.1267 · PC2 1.153PC1 -0.4317 · PC2 1.953PC1 -2.469 · PC2 3.056PC1 -0.7201 · PC2 1.506PC1 -1.361 · PC2 1.923PC1 -2.196 · PC2 2.253PC1 -2.567 · PC2 4.012PC1 -1.47 · PC2 1.413PC1 -0.8604 · PC2 1.518PC1 -0.9837 · PC2 1.033PC1 0.1276 · PC2 0.9295PC1 -0.7852 · PC2 0.8666PC1 1.809 · PC2 -0.05461PC1 -0.2448 · PC2 0.39PC1 1.183 · PC2 -0.3362PC1 -2.069 · PC2 2.087PC1 2.303 · PC2 -0.2678PC1 -1.051 · PC2 1.003PC1 0.5343 · PC2 0.6285PC1 0.05229 · PC2 0.8774PC1 3.606 · PC2 -1.475PC1 3.942 · PC2 -1.548PC1 2.139 · PC2 0.4415PC1 1.194 · PC2 0.05139PC1 1.941 · PC2 -1.9PC1 3.272 · PC2 -1.497PC1 3.288 · PC2 -1.408PC1 1.909 · PC2 0.2477PC1 1.596 · PC2 0.3128PC1 2.944 · PC2 -1.515PC1 2.307 · PC2 -0.501PC1 -0.5979 · PC2 -0.7805PC1 -0.2764 · PC2 -1.974PC1 -1.966 · PC2 2.556PC1 -2.62 · PC2 1.853PC1 -1.833 · PC2 -2.79PC1 2.59 · PC2 -2.618PC1 -10.78 · PC2 -0.2293PC1 -2.217 · PC2 -0.5911PC1 1.584 · PC2 0.06164PC1 -1.812 · PC2 2.151PC1 1.008 · PC2 -1.039PC1 -12.78 · PC2 -3.428PC1 -6.98 · PC2 -1.344PC1 -0.568 · PC2 0.9818PC1 1.866 · PC2 -1.258PC1 1.716 · PC2 -1.499PC1 1.988 · PC2 -1.366PC1 1.981 · PC2 -1.367PC1 0.9419 · PC2 -1.416PC1 2.669 · PC2 -1.218PC1 2.364 · PC2 -0.9427PC1 2.768 · PC2 -1.58PC1 2.831 · PC2 -1.27PC1 1.785 · PC2 -0.8858PC1 2.952 · PC2 -1.336PC1 2.899 · PC2 -1.16PC1 2.071 · PC2 -0.533PC1 2.39 · PC2 -0.2904PC1 2.717 · PC2 -0.5733PC1 1.754 · PC2 -0.6407PC1 1.759 · PC2 -0.5715PC1 2.547 · PC2 -0.06571PC1 0.001849 · PC2 1.368PC1 1.321 · PC2 -0.2282PC1 2.471 · PC2 -0.1692PC1 2.787 · PC2 -0.5654PC1 2.987 · PC2 -1.211PC1 2.745 · PC2 -0.7347PC1 1.947 · PC2 -0.2853PC1 3.685 · PC2 -1.646PC1 4.036 · PC2 -1.654PC1 3.381 · PC2 -1.496PC1 3.354 · PC2 -1.957PC1 3.585 · PC2 -1.466PC1 3.504 · PC2 -1.827PC1 3.438 · PC2 -1.426PC1 2.894 · PC2 -1.685PC1 3.245 · PC2 -2.084PC1 1.748 · PC2 -0.6253PC1 2.765 · PC2 -1.522PC1 2.49 · PC2 -0.6465PC1 2.961 · PC2 -1.295PC1 2.769 · PC2 -1.16PC1 2.961 · PC2 -1.268PC1 2.989 · PC2 -1.355PC1 3.252 · PC2 -1.129PC1 2.788 · PC2 -1.296PC1 2.936 · PC2 -1.363PC1 3.028 · PC2 -1.415PC1 2.81 · PC2 -1.239PC1 2.863 · PC2 -1.369PC1 2.734 · PC2 -1.405PC1 -3.314 · PC2 -1.54PC1 -2.405 · PC2 1.9PC1 -2.868 · PC2 -3.016PC1 -0.4277 · PC2 0.3041PC1 -0.04104 · PC2 0.1356PC1 -0.3448 · PC2 1.581PC1 -9.17 · PC2 -2.263PC1 2.157 · PC2 -1.418PC1 2.315 · PC2 -1.081PC1 1.807 · PC2 -1.174PC1 2.897 · PC2 -1.069PC1 1.94 · PC2 3.635PC1 -4.939 · PC2 1.719PC1 -2.016 · PC2 0.234PC1 -4.013 · PC2 0.4213PC1 -3.024 · PC2 0.0999PC1 -3.466 · PC2 0.7395PC1 -3.445 · PC2 1.329PC1 -8.018 · PC2 -1.494PC1 -2.521 · PC2 -0.7824PC1 -3.755 · PC2 1.35PC1 -4.756 · PC2 0.3524PC1 -4.387 · PC2 -0.3108PC1 -2.007 · PC2 0.9359PC1 -4.775 · PC2 -1.391PC1 -1.491 · PC2 2.773PC1 1.868 · PC2 -2.206PC1 -1.573 · PC2 -3.621PC1 1.917 · PC2 -1.78PC1 8 · PC2 -3.535PC1 -2.072 · PC2 3.501PC1 -0.8061 · PC2 1.346PC1 0.7557 · PC2 2.419PC1 -1.541 · PC2 -0.3627PC1 -10.48 · PC2 1.097PC1 2.693 · PC2 -1.12PC1 1.398 · PC2 -0.6267PC1 4.327 · PC2 0.967PC1 1.197 · PC2 -3.664PC1 2.285 · PC2 -0.01345PC1 3.285 · PC2 3.407PC1 3.327 · PC2 3.548PC1 2.823 · PC2 3.887PC1 3.481 · PC2 3.563PC1 2.988 · PC2 3.874PC1 2.686 · PC2 3.76PC1 2.573 · PC2 3.777PC1 2.715 · PC2 3.32PC1 2.492 · PC2 3.405PC1 -0.5514 · PC2 2.613PC1 0.6933 · PC2 1.962PC1 -0.2265 · PC2 2.034PC1 0.439 · PC2 0.5351PC1 0.7533 · PC2 0.4279PC1 0.1437 · PC2 0.5116PC1 -9.715 · PC2 1.04PC1 -11.65 · PC2 1.001PC1 -11.03 · PC2 0.9255PC1 -10.32 · PC2 1.203PC1 -10.78 · PC2 1.72PC1 -10.26 · PC2 1.139PC1 -9.761 · PC2 1.275PC1 -10.35 · PC2 1.249PC1 -9.571 · PC2 1.288PC1 -9.589 · PC2 1.406PC1 -0.9954 · PC2 -1.706PC1 -0.7593 · PC2 -2.206PC1 -0.8136 · PC2 -2.068PC1 -4.066 · PC2 -1.406PC1 -1.674 · PC2 -1.391PC1 -0.7726 · PC2 -1.973PC1 -0.8763 · PC2 -1.849PC1 -2.089 · PC2 -1.331PC1 -4.154 · PC2 -1.38PC1 -1.652 · PC2 1.014PC1 0.8676 · PC2 -0.1558PC1 0.6097 · PC2 -0.06811PC1 0.6358 · PC2 -0.5139PC1 0.2804 · PC2 -2.267PC1 3.767 · PC2 -1.5PC1 0.7793 · PC2 -1.871PC1 1.997 · PC2 -2.038PC1 1.021 · PC2 -1.887PC1 1.649 · PC2 -1.679PC1 3.74 · PC2 -1.835PC1 4.163 · PC2 -1.927PC1 1.647 · PC2 -0.7471PC1 2.077 · PC2 -0.3645PC1 3.09 · PC2 -1.316PC1 1.394 · PC2 -0.4915PC1 1.717 · PC2 -0.676PC1 1.639 · PC2 -0.6306PC1 1.502 · PC2 -0.4616PC1 2.607 · PC2 -0.6691PC1 2.306 · PC2 -0.6913PC1 2.351 · PC2 -0.7125PC1 3.699 · PC2 -1.684PC1 3.122 · PC2 -2.145PC1 3.757 · PC2 -2.299PC1 3.549 · PC2 -2.006PC1 3.588 · PC2 -1.901PC1 4.181 · PC2 -2.29PC1 3.163 · PC2 -1.467PC1 5.514 · PC2 -2.051PC1 3.117 · PC2 -1.494PC1 2.87 · PC2 -1.064PC1 2.885 · PC2 -1.386PC1 1.758 · PC2 -0.8947PC1 3.236 · PC2 -1.177PC1 3.364 · PC2 -1.725PC1 3.273 · PC2 -1.725PC1 3.767 · PC2 -1.951PC1 3.81 · PC2 -1.805PC1 2.851 · PC2 -1.194PC1 2.134 · PC2 -0.4852PC1 2.961 · PC2 -1.178PC1 2.566 · PC2 -1.268PC1 2.92 · PC2 -1.175PC1 2.977 · PC2 -1.217PC1 2.565 · PC2 -1.297PC1 2.103 · PC2 -1.216PC1 3.867 · PC2 -1.609PC1 3.448 · PC2 -1.228PC1 3.345 · PC2 -1.273PC1 3.294 · PC2 -1.362PC1 3.085 · PC2 -1.38PC1 3.28 · PC2 -1.394PC1 4.201 · PC2 -1.375PC1 1.643 · PC2 0.7119PC1 1.328 · PC2 0.7032PC1 2.649 · PC2 0.2915PC1 1.794 · PC2 0.7398PC1 1.284 · PC2 1.007PC1 1.272 · PC2 1.142PC1 1.994 · PC2 0.2808PC1 1.808 · PC2 0.3014PC1 1.39 · PC2 0.9192PC1 1.064 · PC2 1.042PC1 2.975 · PC2 -1.376PC1 2.331 · PC2 -1.23PC1 3.096 · PC2 -1.411PC1 1.242 · PC2 -0.3832PC1 1.739 · PC2 -0.3183PC1 2.162 · PC2 -0.427PC1 3.117 · PC2 -1.452PC1 2.69 · PC2 -1.345PC1 2.989 · PC2 -1.104PC1 3.983 · PC2 -1.504PC1 3.474 · PC2 -0.6036PC1 3.553 · PC2 -0.9052PC1 5.775 · PC2 -2.17PC1 3.889 · PC2 -1.472PC1 4.396 · PC2 -1.509PC1 3.559 · PC2 -0.9038PC1 2.888 · PC2 -0.4203PC1 2.927 · PC2 -0.6984PC1 2.852 · PC2 -0.6808PC1 3.551 · PC2 -1.375PC1 4.031 · PC2 -1.643PC1 1.151 · PC2 0.3284PC1 4.132 · PC2 -1.728PC1 2.378 · PC2 0.1365PC1 0.3389 · PC2 -0.6811PC1 2.926 · PC2 -0.4707PC1 2.731 · PC2 -0.3797PC1 2.811 · PC2 -1.05PC1 1.787 · PC2 -0.6028PC1 2.862 · PC2 -0.2709PC1 2.295 · PC2 -1.005PC1 2.516 · PC2 -0.9123PC1 2.272 · PC2 -0.9864PC1 2.276 · PC2 -0.4148PC1 2.572 · PC2 -0.967PC1 1.016 · PC2 -0.4322PC1 4.028 · PC2 -1.373PC1 2.82 · PC2 -0.4363PC1 2.566 · PC2 -0.7267PC1 2.529 · PC2 -0.5555PC1 3.367 · PC2 -1.471PC1 2.141 · PC2 0.05442PC1 3.386 · PC2 -1.361PC1 2.02 · PC2 -0.3076PC1 3.043 · PC2 -0.9441PC1 2.132 · PC2 -0.3601PC1 2.367 · PC2 -0.2257PC1 1.106 · PC2 0.5706PC1 0.8871 · PC2 0.0203PC1 0.6417 · PC2 1.308PC1 0.6997 · PC2 -0.4048PC1 3.246 · PC2 -1.569PC1 2.611 · PC2 -1.564PC1 1.904 · PC2 -1.017PC1 1.948 · PC2 -0.4676PC1 -1.847 · PC2 4.435PC1 -0.9712 · PC2 3.644PC1 -0.1348 · PC2 2.809PC1 0.3752 · PC2 3.089PC1 -0.4166 · PC2 3.118PC1 -0.8645 · PC2 3.689PC1 0.3814 · PC2 2.872PC1 -0.6322 · PC2 2.51PC1 -0.9718 · PC2 4.118PC1 -2.658 · PC2 4.98PC1 0.4976 · PC2 2.196PC1 0.2345 · PC2 2.944PC1 -1.118 · PC2 4.022PC1 -1.391 · PC2 3.523PC1 0.04745 · PC2 3PC1 -1.2 · PC2 4.879PC1 -0.3469 · PC2 4.449PC1 -0.7208 · PC2 3.922PC1 -1.1 · PC2 4.053PC1 0.2241 · PC2 2.994PC1 0.7159 · PC2 2.462PC1 -0.5331 · PC2 2.833PC1 -0.1499 · PC2 2.532PC1 -0.3542 · PC2 3.287PC1 -2.12 · PC2 2.857PC1 -0.7297 · PC2 2.793PC1 -1.262 · PC2 2.948PC1 -0.06911 · PC2 2.56PC1 0.434 · PC2 2.507PC1 -0.001433 · PC2 2.418PC1 -0.2164 · PC2 2.403PC1 0.3343 · PC2 2.641PC1 1.076 · PC2 3.222PC1 -1.503 · PC2 1.93PC1 -0.1211 · PC2 2.379PC1 0.66 · PC2 3.016PC1 0.4242 · PC2 2.607PC1 0.1687 · PC2 3.231PC1 -0.02318 · PC2 2.557PC1 0.3747 · PC2 3.058PC1 -0.1533 · PC2 2.486PC1 -1.23 · PC2 2.656PC1 2.324 · PC2 -0.8829PC1 -0.5131 · PC2 0.2029PC1 2.286 · PC2 -0.9297PC1 1.993 · PC2 -1.087PC1 0.7664 · PC2 -0.1744PC1 2.167 · PC2 -1.215PC1 2.633 · PC2 -1.354PC1 2.84 · PC2 -2.979PC1 3.703 · PC2 -2.838PC1 4.024 · PC2 -3.094PC1 2.058 · PC2 -1.208PC1 3.276 · PC2 -1.139PC1 2.555 · PC2 -1.129PC1 1.349 · PC2 -0.5541PC1 2.123 · PC2 -1.728PC1 2.09 · PC2 -0.748PC1 2.276 · PC2 -1.298PC1 2.138 · PC2 -1.319PC1 2.807 · PC2 -1.819PC1 0.2549 · PC2 -1.664PC1 0.2606 · PC2 -1.033PC1 0.9807 · PC2 -1.162PC1 0.8754 · PC2 -0.01754PC1 1.383 · PC2 -0.7289PC1 0.9491 · PC2 -0.8092PC1 1.966 · PC2 -1.607PC1 1.875 · PC2 -1.909PC1 2.374 · PC2 -1.954PC1 1.317 · PC2 -2PC1 1.4 · PC2 -1.532PC1 1.207 · PC2 -1.387PC1 1.212 · PC2 -1.495PC1 1.964 · PC2 -0.7066PC1 0.9067 · PC2 -0.1607PC1 -12.82 · PC2 -3.494PC1 -17.04 · PC2 -3.743PC1 -13.1 · PC2 -3.192PC1 -10.78 · PC2 -2.614PC1 -13.91 · PC2 -3.107PC1 -13.53 · PC2 -3.43PC1 -13.97 · PC2 -3.269PC1 -14.2 · PC2 -2.331PC1 -14.16 · PC2 -3.062PC1 -13.43 · PC2 -2.907PC1 -11.36 · PC2 -2.503PC1 -11.81 · PC2 -2.288PC1 -13.63 · PC2 -1.78PC1 -14.71 · PC2 -2.708PC1 -12.23 · PC2 -2.774PC1 -11.98 · PC2 -1.298PC1 -11.17 · PC2 -0.1953PC1 -13.77 · PC2 -1.214PC1 -11.57 · PC2 -3.143PC1 -11.77 · PC2 -1.162PC1 -15.94 · PC2 -1.697PC1 -15.18 · PC2 -2.503PC1 -11.13 · PC2 0.1595PC1 -21.38 · PC2 -3.897PC1 -16.48 · PC2 -2.203PC1 -9.593 · PC2 -3.208PC1 -10.45 · PC2 -0.5351PC1 -11.33 · PC2 -0.894PC1 -12.81 · PC2 -2.145PC1 -11.28 · PC2 -0.4509PC1 -13.44 · PC2 -1.05PC1 -14.18 · PC2 -1.626PC1 -17.09 · PC2 -2.083PC1 -16.19 · PC2 -2.138PC1 -17.1 · PC2 -0.7518PC1 3.531 · PC2 -0.9342PC1 3.281 · PC2 -0.53PC1 2.577 · PC2 -0.5481PC1 2.751 · PC2 -1.318PC1 2.531 · PC2 -1.49PC1 1.636 · PC2 -0.5607PC1 2.715 · PC2 -3.151PC1 3.153 · PC2 -1.853PC1 4.208 · PC2 -2.463PC1 -0.6639 · PC2 0.1046PC1 2.019 · PC2 -0.8911PC1 1.227 · PC2 -0.8569PC1 -0.08967 · PC2 0.623PC1 1.537 · PC2 -1.35PC1 1.773 · PC2 -0.728PC1 2.267 · PC2 -1.284PC1 1.191 · PC2 -1.828PC1 0.2136 · PC2 -0.9418PC1 2.772 · PC2 -1.009PC1 1.034 · PC2 -0.9076PC1 1.044 · PC2 -0.1728PC1 1.284 · PC2 -0.7089PC1 0.2237 · PC2 -0.872PC1 2.212 · PC2 -0.6953PC1 2.227 · PC2 -1.715PC1 2.82 · PC2 -1.871PC1 2.58 · PC2 -1.961PC1 -1.074 · PC2 -1.371PC1 0.08452 · PC2 -1.415PC1 2.292 · PC2 -1.039PC1 2.243 · PC2 -1.407PC1 1.44 · PC2 -0.81PC1 -0.6048 · PC2 0.4277PC1 4.907 · PC2 -3.898PC1 4.053 · PC2 -3.283PC1 3.947 · PC2 -3.61PC1 3.225 · PC2 -2.106PC1 2.099 · PC2 -1.033PC1 3.389 · PC2 -2.798PC1 3.162 · PC2 -2.623PC1 4.639 · PC2 -3.036PC1 3.391 · PC2 -3.006PC1 2.986 · PC2 -1.548PC1 3.696 · PC2 -3.284PC1 2.729 · PC2 -0.5557PC1 2.189 · PC2 -0.5915PC1 2.412 · PC2 -1.703PC1 3.654 · PC2 -1.997PC1 2.501 · PC2 -1.634PC1 0.6816 · PC2 -0.8822PC1 1.662 · PC2 -0.5903PC1 0.9705 · PC2 -0.3081PC1 2.896 · PC2 -1.23PC1 2.168 · PC2 -1.751PC1 2.853 · PC2 -1.874PC1 -0.6793 · PC2 -1.883PC1 2.678 · PC2 -1.272PC1 2.622 · PC2 -1.958PC1 3.069 · PC2 -1.454PC1 2.5 · PC2 -1.789PC1 0.1417 · PC2 0.6903PC1 1.853 · PC2 -0.2733PC1 2.525 · PC2 -0.4219PC1 3.305 · PC2 -1.457PC1 3.438 · PC2 -1.666PC1 3.105 · PC2 -1.596PC1 3.087 · PC2 -3.589PC1 4.161 · PC2 -2.963PC1 2.565 · PC2 -0.8493PC1 2.059 · PC2 -2.903PC1 2.771 · PC2 -1.378PC1 2.636 · PC2 -0.9811PC1 1.175 · PC2 0.4514PC1 2.774 · PC2 -1.663PC1 2.945 · PC2 -1.823PC1 2.759 · PC2 -1.406PC1 2.731 · PC2 -1.166PC1 2.856 · PC2 -2.093PC1 0.4658 · PC2 -1.709PC1 0.5262 · PC2 -1.415PC1 1.67 · PC2 -1.788PC1 2.508 · PC2 -1.798PC1 0.375 · PC2 0.4017PC1 2.161 · PC2 -0.7377PC1 2.013 · PC2 -0.6081PC1 2.672 · PC2 -1.697PC1 3.416 · PC2 -1.953PC1 1.33 · PC2 -1.005PC1 0.4344 · PC2 -0.8245PC1 2.049 · PC2 -1.133PC1 2.917 · PC2 -1.258PC1 2.197 · PC2 -1.261PC1 2.841 · PC2 -1.035PC1 2.181 · PC2 -0.6644PC1 2.798 · PC2 -1.478PC1 2.389 · PC2 -0.978PC1 -8.29 · PC2 0.8654PC1 -4.076 · PC2 -0.9064PC1 -11.12 · PC2 -1.371PC1 -10.29 · PC2 -0.6092PC1 -10.11 · PC2 -0.3673PC1 -9.082 · PC2 -0.7768PC1 -10.45 · PC2 -1.082PC1 -9.506 · PC2 -0.7118PC1 -9.938 · PC2 -1.032PC1 -8.363 · PC2 -0.8968PC1 -9.818 · PC2 -0.331PC1 -9.953 · PC2 -0.4926PC1 -10.27 · PC2 -0.6295PC1 -9.489 · PC2 -0.3819PC1 -8.815 · PC2 -0.3477PC1 -9.722 · PC2 -0.2091PC1 -8.6 · PC2 -0.7229PC1 -16.69 · PC2 -3.57PC1 -1.4 · PC2 1.831PC1 -0.4065 · PC2 1.92PC1 -3.23 · PC2 2.333PC1 -2.617 · PC2 2.303PC1 -3.606 · PC2 2.283PC1 -0.8926 · PC2 2.057PC1 -0.8302 · PC2 1.721PC1 -1.124 · PC2 2.145PC1 -6.505 · PC2 -1.431PC1 -1.156 · PC2 2.461PC1 -0.4036 · PC2 3.015PC1 -1.239 · PC2 3.038PC1 -0.2289 · PC2 3.281PC1 -1.284 · PC2 3.387PC1 -1.154 · PC2 2.959PC1 -0.7699 · PC2 3.407PC1 -6.329 · PC2 2.953PC1 -1.009 · PC2 3.01PC1 -1.066 · PC2 2.954PC1 -2.387 · PC2 2.974PC1 -1.033 · PC2 3.067PC1 -2.581 · PC2 3.21PC1 -0.6361 · PC2 3.032PC1 -1.487 · PC2 3.07PC1 -3.729 · PC2 1.724PC1 -3.62 · PC2 1.105PC1 -2.848 · PC2 1.382PC1 -3.228 · PC2 1.604PC1 -4.62 · PC2 1.224PC1 -2.707 · PC2 1.702PC1 -3.154 · PC2 2.668PC1 -0.2379 · PC2 2.856PC1 -2.193 · PC2 2.866PC1 -0.3059 · PC2 2.415PC1 -0.8917 · PC2 2.8PC1 0.6687 · PC2 2.885PC1 0.01793 · PC2 3.361PC1 -1.468 · PC2 2.996PC1 -0.5542 · PC2 2.529PC1 -0.02136 · PC2 3.043PC1 0.2332 · PC2 2.968PC1 2.044 · PC2 2.573PC1 1.727 · PC2 3.056PC1 -0.4818 · PC2 2.133PC1 1.807 · PC2 2.714PC1 -0.9446 · PC2 2.874PC1 1.715 · PC2 2.647PC1 0.9447 · PC2 2.495PC1 1.158 · PC2 2.544PC1 1.522 · PC2 2.72PC1 1.583 · PC2 2.583PC1 1.141 · PC2 2.4PC1 1.594 · PC2 2.518PC1 1.314 · PC2 2.338PC1 1.087 · PC2 2.522PC1 -0.03788 · PC2 2.36PC1 0.3312 · PC2 2.563PC1 0.1496 · PC2 2.459PC1 0.5052 · PC2 2.77PC1 -0.9147 · PC2 2.762PC1 -0.3552 · PC2 3.306PC1 -1.641 · PC2 3.914PC1 -2.273 · PC2 3.696PC1 -1.99 · PC2 2.888PC1 -5.235 · PC2 1.756PC1 -1.332 · PC2 3.1PC1 -2.485 · PC2 4.006PC1 -3.143 · PC2 2.887PC1 -1.695 · PC2 3.044PC1 -3.236 · PC2 2.936PC1 -2.516 · PC2 2.681PC1 -1.015 · PC2 3.125PC1 -1.183 · PC2 2.926PC1 -2.429 · PC2 2.229PC1 0.1029 · PC2 3.321PC1 -1.498 · PC2 3.721PC1 -0.07553 · PC2 3.008PC1 -4.225 · PC2 2.124PC1 -0.1759 · PC2 3.171PC1 -1.574 · PC2 3.562PC1 -1.717 · PC2 2.824PC1 -3.038 · PC2 2.372PC1 1.049 · PC2 3.251PC1 -3.065 · PC2 2.423PC1 0.00642 · PC2 3.27PC1 -1.239 · PC2 3.393PC1 -3.334 · PC2 2.637PC1 0.3639 · PC2 3.407PC1 0.6311 · PC2 3.112PC1 1.681 · PC2 3.187PC1 0.7146 · PC2 3.078PC1 1.426 · PC2 2.619PC1 1.441 · PC2 3.088PC1 1.541 · PC2 2.77PC1 1.346 · PC2 2.667PC1 1.474 · PC2 2.721PC1 1.485 · PC2 3.039PC1 1.78 · PC2 2.941PC1 1.701 · PC2 2.795PC1 1.357 · PC2 3.143PC1 1.245 · PC2 3.09PC1 1.756 · PC2 2.65PC1 0.9864 · PC2 3.279PC1 1.872 · PC2 2.701PC1 1.234 · PC2 3.19PC1 1.797 · PC2 2.642PC1 1.385 · PC2 3.179PC1 1.787 · PC2 2.823PC1 1.76 · PC2 3.063PC1 1.61 · PC2 2.948PC1 1.868 · PC2 2.881PC1 2.164 · PC2 2.621PC1 2.268 · PC2 2.676PC1 1.812 · PC2 2.74PC1 1.915 · PC2 2.761PC1 1.949 · PC2 2.749PC1 (79.2%)PC2 (15.5%)800 scores
View plotted PCA coordinates

Point numbers identify rows in this displayed coordinate list, not original observation or sample IDs. Non-integer coordinates use up to five significant digits. Original IDs, component loadings, preprocessing details and the observation selection method are not supplied with this plot.

Plotted observation positions (not original sample IDs)
PointPC1 scorePC2 score
12.53370.14645
20.104392.5017
3-0.797682.8081
41.32841.2931
51.6467-0.85531
62.6524-0.98698
72.8608-0.99656
81.2915-0.066706
91.7353-0.17079
101.7222-0.24303
111.4859-0.58493
121.20660.13485
132.1045-0.21906
140.169080.35714
151.2255-0.31562
160.150770.608
170.810720.60249
181.9573-0.20069
190.069863-0.091716
201.7031-0.067699
210.445380.10575
22-1.8264-1.5204
231.2225-0.089655
241.6784-0.15975
251.9392-0.7093
260.754190.10464
270.92743-0.31393
281.9223-0.27674
290.13069-1.2763
302.0022-0.9447
311.79430.0034284
321.76250.45275
331.36350.39134
340.63206-0.53066
350.75556-0.47622
361.5337-0.67328
37-1.03480.21113
382.9277-1.8073
390.45974-1.279
400.74182-0.54737
412.6007-1.1317
422.4274-0.90987
432.5123-0.31969
440.065191-1.1468
450.58728-1.0725
46-0.29136-0.43757
471.3719-0.73467
481.5305-1.2514
49-0.373-1.6081
501.8354-0.91424
510.57043-1.1249
521.9236-1.4883
531.8813-0.97923
543.5774-2.5207
550.26061-2.2296
563.134-1.591
57-12.398-4.738
580.92877-1.6238
59-0.30323-0.79457
601.01440.17066
610.17432-0.97681
620.69526-1.5635
631.2641-1.1436
64-5.6293-2.6455
651.4649-1.5075
661.6679-1.726
67-1.578-1.46
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7791.3462.667
7801.47392.7209
7811.48543.039
7821.77992.9415
7831.70052.7954
7841.35733.1427
7851.24533.0902
7861.75622.6503
7870.986373.2789
7881.87232.7008
7891.23373.1897
7901.7972.6424
7911.38483.1795
7921.78682.8225
7931.76043.0626
7941.61032.9477
7951.86762.8807
7962.16392.6215
7972.26812.676
7981.81152.7398
7991.91492.7609
8001.94882.7493

PCA explained variance — how many dimensions?

Each bar reports the share of spectral variance explained by a component. Concentration in the first components indicates correlated wavelengths. This is not variance explained in the prediction target. This projection may use a different observation sample from the score plot.

PCA explained variance0%25%50%75%100%PC1: 80.0% (cumulative 80.0%)1PC2: 15.1% (cumulative 95.0%)2PC3: 4.0% (cumulative 99.0%)3PC4: 0.5% (cumulative 99.5%)4PC5: 0.2% (cumulative 99.7%)5PC6: 0.1% (cumulative 99.8%)6PC7: 0.1% (cumulative 99.9%)7PC8: 0.0% (cumulative 99.9%)8PC9: 0.0% (cumulative 99.9%)9PC10: 0.0% (cumulative 100.0%)10cumulative explained variancePC variancecumulativeprincipal component · cumulative (dashed)
View explained variance values
Variance per displayed principal component
ComponentSpectral variance
PC179.96%
PC215.06%
PC33.98%
PC40.53%
PC50.16%
PC60.09%
PC70.09%
PC80.04%
PC90.03%
PC100.02%
X-Y spectral correlation 18

Pearson r measures the linear association between each spectral channel (X) and a numeric target (Y), from −1 to +1. The table reports peak and average absolute correlation and the share of channels with |r| ≥ 0.5. These are in-dataset associations, not validation scores or proof of causation.

Spectral association with TOC

Read Pearson correlation (−1 to +1) along the spectral axis (nm); the solid curve shows |r| and the dashed curve shows signed r.

X_native · TOC spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation01,0002,0003,000|r|signed raxis · Pearson correlation scale

Spectral association with TOM

Read Pearson correlation (−1 to +1) along the spectral axis (nm); the solid curve shows |r| and the dashed curve shows signed r.

X_native · TOM spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation01,0002,0003,000|r|signed raxis · Pearson correlation scale

Spectral association with ASH

Read Pearson correlation (−1 to +1) along the spectral axis (nm); the solid curve shows |r| and the dashed curve shows signed r.

X_native · ASH spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation01,0002,0003,000|r|signed raxis · Pearson correlation scale
Targetmax |r|axis @ maxmean |r||r| ≥ .5
TOC0.5941,1160.42851.7%
TOM0.6071,3140.44652.5%
ASH0.5991,1260.36244.3%
SOL0.492,4980.2040.0%
HEM0.5761,0940.37332.1%
CEL0.6251,3140.47454.1%
LIC0.3075960.1360.0%
TN0.521,1160.33419.6%
totP0.4294020.2210.0%
totK0.1434000.02990.0%
totCa0.6494040.30211.3%
totMg0.3782,1240.2230.0%
Cd0.4681,8520.4070.0%
Cr0.8741,0720.74593.8%
Cu0.5969000.4550.0%
Ni0.8241,0280.66885.0%
Pb0.5569480.41533.4%
Zn0.6439860.54673.0%

ASD standardized on XDS absorbance

X_xds_standardized · NIR · ASD

Read the signal and variation summaries for this source. Axis unit: nm; signal representation: not recorded.

Spectral signal summary

The mean and median describe the typical signal at each channel; shaded bands show the 25th–75th and 5th–95th percentiles across observations. A wider band means greater variation, which can be biological or instrumental.

ASD standardized on XDS absorbance spectra0.00.51.01.501,0002,0003,000q05-q95 envelopeq25-q75 envelopemedian spectrummedianq25–q75q05–q95wavelength / nm400nm — median 0.6048 (q25–q75 0.4911–0.8258)416nm — median 0.806 (q25–q75 0.7075–1.012)430nm — median 0.8488 (q25–q75 0.752–1.055)446nm — median 0.8544 (q25–q75 0.7567–1.062)460nm — median 0.8507 (q25–q75 0.7547–1.051)476nm — median 0.8373 (q25–q75 0.7388–1.03)490nm — median 0.8187 (q25–q75 0.7206–1.011)506nm — median 0.7922 (q25–q75 0.693–0.9853)520nm — median 0.7653 (q25–q75 0.6695–0.962)536nm — median 0.7378 (q25–q75 0.6417–0.9326)550nm — median 0.7107 (q25–q75 0.6175–0.9053)566nm — median 0.6826 (q25–q75 0.592–0.8786)582nm — median 0.6544 (q25–q75 0.567–0.8502)596nm — median 0.6332 (q25–q75 0.5463–0.8281)612nm — median 0.6065 (q25–q75 0.5227–0.7972)626nm — median 0.5839 (q25–q75 0.5026–0.7634)642nm — median 0.5583 (q25–q75 0.4787–0.7384)656nm — median 0.541 (q25–q75 0.4584–0.7308)672nm — median 0.5179 (q25–q75 0.4328–0.7123)686nm — median 0.4902 (q25–q75 0.4074–0.6655)702nm — median 0.4634 (q25–q75 0.3815–0.6167)716nm — median 0.4383 (q25–q75 0.3594–0.5844)732nm — median 0.4137 (q25–q75 0.3341–0.5465)748nm — median 0.389 (q25–q75 0.3108–0.5151)762nm — median 0.3671 (q25–q75 0.2915–0.4919)778nm — median 0.3436 (q25–q75 0.2703–0.4668)792nm — median 0.3239 (q25–q75 0.252–0.4458)808nm — median 0.304 (q25–q75 0.2339–0.4217)822nm — median 0.2879 (q25–q75 0.2202–0.4021)838nm — median 0.2715 (q25–q75 0.2058–0.3829)852nm — median 0.2592 (q25–q75 0.1944–0.3657)868nm — median 0.2458 (q25–q75 0.1816–0.3465)882nm — median 0.2339 (q25–q75 0.1711–0.3333)898nm — median 0.2219 (q25–q75 0.1604–0.3178)914nm — median 0.2103 (q25–q75 0.1509–0.3035)928nm — median 0.2017 (q25–q75 0.1435–0.2919)944nm — median 0.1922 (q25–q75 0.1355–0.2783)958nm — median 0.1856 (q25–q75 0.1299–0.2699)974nm — median 0.1772 (q25–q75 0.1237–0.2613)988nm — median 0.171 (q25–q75 0.1189–0.2555)1,004nm — median 0.1738 (q25–q75 0.1224–0.2544)1,018nm — median 0.1681 (q25–q75 0.1178–0.2477)1,034nm — median 0.1624 (q25–q75 0.1129–0.2405)1,050nm — median 0.157 (q25–q75 0.1083–0.2342)1,064nm — median 0.1522 (q25–q75 0.1049–0.2282)1,080nm — median 0.148 (q25–q75 0.1012–0.2224)1,094nm — median 0.1438 (q25–q75 0.09806–0.2172)1,110nm — median 0.1368 (q25–q75 0.09197–0.2084)1,124nm — median 0.1339 (q25–q75 0.08976–0.2039)1,140nm — median 0.1313 (q25–q75 0.08894–0.2002)1,154nm — median 0.1302 (q25–q75 0.08901–0.199)1,170nm — median 0.1288 (q25–q75 0.08972–0.1957)1,184nm — median 0.1278 (q25–q75 0.09005–0.194)1,200nm — median 0.1259 (q25–q75 0.08949–0.1899)1,216nm — median 0.1225 (q25–q75 0.08713–0.1866)1,230nm — median 0.1192 (q25–q75 0.08364–0.1833)1,246nm — median 0.1158 (q25–q75 0.08021–0.1801)1,260nm — median 0.1137 (q25–q75 0.07813–0.1777)1,276nm — median 0.1118 (q25–q75 0.07684–0.1758)1,290nm — median 0.1101 (q25–q75 0.07568–0.174)1,306nm — median 0.1088 (q25–q75 0.07518–0.1726)1,320nm — median 0.1095 (q25–q75 0.07621–0.1722)1,336nm — median 0.111 (q25–q75 0.07919–0.1722)1,350nm — median 0.1139 (q25–q75 0.08471–0.1739)1,366nm — median 0.118 (q25–q75 0.09077–0.1774)1,382nm — median 0.1247 (q25–q75 0.09911–0.1829)1,396nm — median 0.1413 (q25–q75 0.1148–0.193)1,412nm — median 0.1673 (q25–q75 0.1397–0.2151)1,426nm — median 0.1828 (q25–q75 0.1541–0.2269)1,442nm — median 0.1915 (q25–q75 0.1626–0.2364)1,456nm — median 0.1948 (q25–q75 0.1654–0.2405)1,472nm — median 0.1951 (q25–q75 0.1657–0.241)1,486nm — median 0.1924 (q25–q75 0.164–0.2376)1,502nm — median 0.1883 (q25–q75 0.1604–0.2291)1,516nm — median 0.1843 (q25–q75 0.1563–0.223)1,532nm — median 0.1795 (q25–q75 0.1524–0.2176)1,548nm — median 0.1768 (q25–q75 0.1501–0.2151)1,562nm — median 0.176 (q25–q75 0.15–0.214)1,578nm — median 0.1747 (q25–q75 0.149–0.2134)1,592nm — median 0.1726 (q25–q75 0.1469–0.2101)1,608nm — median 0.1672 (q25–q75 0.1433–0.2075)1,622nm — median 0.1637 (q25–q75 0.1406–0.2038)1,638nm — median 0.162 (q25–q75 0.1387–0.202)1,652nm — median 0.1619 (q25–q75 0.1389–0.2021)1,668nm — median 0.1651 (q25–q75 0.1424–0.2067)1,682nm — median 0.1713 (q25–q75 0.1479–0.2119)1,698nm — median 0.1786 (q25–q75 0.1541–0.2204)1,714nm — median 0.1849 (q25–q75 0.1607–0.228)1,728nm — median 0.1903 (q25–q75 0.1656–0.234)1,744nm — median 0.1889 (q25–q75 0.1643–0.2337)1,758nm — median 0.1915 (q25–q75 0.1661–0.2356)1,774nm — median 0.1917 (q25–q75 0.1659–0.2359)1,788nm — median 0.1919 (q25–q75 0.1659–0.2356)1,804nm — median 0.1911 (q25–q75 0.1648–0.2353)1,818nm — median 0.1898 (q25–q75 0.1634–0.2339)1,834nm — median 0.1896 (q25–q75 0.1622–0.2328)1,848nm — median 0.1883 (q25–q75 0.1612–0.2324)1,864nm — median 0.194 (q25–q75 0.1663–0.2376)1,880nm — median 0.2183 (q25–q75 0.1888–0.2658)1,894nm — median 0.2704 (q25–q75 0.2406–0.325)1,910nm — median 0.3415 (q25–q75 0.3113–0.4003)1,924nm — median 0.3703 (q25–q75 0.3396–0.4325)1,940nm — median 0.3706 (q25–q75 0.3385–0.4349)1,954nm — median 0.3557 (q25–q75 0.3245–0.419)1,970nm — median 0.3374 (q25–q75 0.3075–0.3989)1,984nm — median 0.322 (q25–q75 0.2925–0.3824)2,000nm — median 0.3071 (q25–q75 0.2768–0.3648)2,016nm — median 0.299 (q25–q75 0.2692–0.357)2,030nm — median 0.3022 (q25–q75 0.2694–0.3594)2,046nm — median 0.3119 (q25–q75 0.276–0.3688)2,060nm — median 0.3182 (q25–q75 0.2809–0.3737)2,076nm — median 0.3195 (q25–q75 0.2827–0.378)2,090nm — median 0.3207 (q25–q75 0.2822–0.3781)2,106nm — median 0.3236 (q25–q75 0.2835–0.381)2,120nm — median 0.3247 (q25–q75 0.2834–0.3835)2,136nm — median 0.3255 (q25–q75 0.2847–0.3847)2,150nm — median 0.3256 (q25–q75 0.2863–0.3833)2,166nm — median 0.3253 (q25–q75 0.2858–0.3814)2,182nm — median 0.3206 (q25–q75 0.2822–0.3739)2,196nm — median 0.3143 (q25–q75 0.2779–0.3659)2,212nm — median 0.3104 (q25–q75 0.2753–0.3605)2,226nm — median 0.3046 (q25–q75 0.2696–0.3552)2,242nm — median 0.3148 (q25–q75 0.281–0.3663)2,256nm — median 0.3343 (q25–q75 0.2996–0.3858)2,272nm — median 0.3544 (q25–q75 0.3177–0.4078)2,286nm — median 0.361 (q25–q75 0.3244–0.4161)2,302nm — median 0.3744 (q25–q75 0.335–0.4257)2,316nm — median 0.3805 (q25–q75 0.3414–0.4319)2,332nm — median 0.3773 (q25–q75 0.3393–0.4315)2,348nm — median 0.3857 (q25–q75 0.3467–0.4389)2,362nm — median 0.3826 (q25–q75 0.3451–0.4366)2,378nm — median 0.3859 (q25–q75 0.3483–0.4382)2,392nm — median 0.3896 (q25–q75 0.3513–0.4414)2,408nm — median 0.3964 (q25–q75 0.3566–0.4498)2,422nm — median 0.4093 (q25–q75 0.3686–0.4639)2,438nm — median 0.4289 (q25–q75 0.3859–0.4849)2,452nm — median 0.4488 (q25–q75 0.4058–0.5061)2,468nm — median 0.4736 (q25–q75 0.4282–0.532)2,482nm — median 0.4872 (q25–q75 0.4414–0.5456)2,498nm — median 0.4931 (q25–q75 0.4473–0.5541)

Sampling

How many observations and spectral channels were exported, and how the wavelength axis is spaced. Reading guide

Wavelengths
?Number of spectral channels. A channel can represent wavelength, wavenumber or another feature axis.
1,050
Axis range
?Lowest and highest recorded spectral-axis coordinates, in the stated unit.
400–2,498 nm
Mean spacing
?Average difference between adjacent spectral-axis coordinates; compare only within the same unit.
2 nm
Grid
?Uniform means adjacent axis spacings are approximately equal. An irregular grid may need care when applying derivatives.
uniform
Observations
?Exported spectral rows in this source, including repeated measurements when present.
1,000

Signal & quality

Signal level and roughness summaries. These depend on the signal representation and are not instrument acceptance tests. Reading guide

Value range
?Minimum and maximum recorded spectral values, in the original signal representation.
-0.223 – 1.37
Mean range
?Minimum and maximum of the mean spectrum across spectral channels.
0.171 – 0.888
Mean level
?Average signal over observations and channels. The historical key mean_reflectance may also store other signal representations.
0.3534
Area
?Trapezoidal integral of the mean spectrum over the sorted recorded axis; its unit is signal multiplied by axis unit.
741.1
PTP
?Peak-to-peak: maximum minus minimum of the mean spectrum. Low values indicate little variation along the mean spectrum.
0.7167
Noise RMS
?Historical roughness sigma proxy from robust second differences. Narrow genuine bands can also raise it; this is not repeat-measured instrument noise.
7.8231e-05
Roughness S/N proxy
?Average absolute signal divided by the roughness proxy. Higher means smoother signal relative to its amplitude, not certified instrument SNR.
4.5e+03
Roughness S/N proxy dB
?20 log10 of the roughness signal-to-noise proxy. Units are decibels; compare only compatible signal representations.
7e+01 dB
Dynamic range
?Maximum minus minimum of the mean spectrum. Its magnitude depends on the signal representation.
0.717
Smoothness
?Mean per-spectrum RMS of second differences, in signal units. Smaller values indicate smoother spectra under this protocol.
0.001648
Saturated
?Fraction at the observed global maximum; a heuristic indicator, not a comparison with known detector limits.
0.0%
X-outliers
?Number flagged by the qualification outlier method. An unusual spectrum can be valid and should not be discarded automatically.
496

Integrity & artefacts

Missing values, narrow spikes and abrupt jumps to investigate in the original spectra. Reading guide

NaN ratio
?Missing spectral cells divided by all spectral cells, displayed as a percentage.
0.00%
Inf count
?Number of positive or negative infinite spectral values; investigate invalid calculations or export.
0
Zero ratio
?Exactly zero spectral cells divided by all cells. Zero can be a valid measurement and is not automatically missing.
0.00%
Spike count
?Local narrow peaks flagged by the second-difference heuristic; real sharp spectral bands may also be flagged.
51,433
Spike rate
?Fraction of tested second-difference positions flagged as spikes, displayed as a percentage.
4.91%
Jump count
?Adjacent-channel changes flagged by the first-difference heuristic, potentially including detector joins.
15,509
Jump rate
?Fraction of tested adjacent-channel differences flagged as jumps, displayed as a percentage.
1.48%
Clip fraction
?Fraction of finite cells near the observed global minimum or maximum; inspect detector limits before concluding saturation.
0.00%

Shape & reference

Variation relative to this dataset's mean spectrum; the mean is not an independently measured calibration reference. Reading guide

Baseline slope
?Linear slope fitted to the mean spectrum over an axis normalized to 0–1. It describes total baseline change, not change per physical axis unit.
-0.22685
Curvature RMS
?Median per-spectrum RMS of second differences, summarizing curvature and high-frequency variation.
0.0014715
D1 RMS
?Median per-spectrum RMS of adjacent-channel first differences; reflects both real spectral structure and artefacts.
0.0033212
RMS to mean
?Root mean square distance to this dataset's mean spectrum, in signal units.
0.094718
RMS p95
?95th percentile of distances to the mean spectrum: 95% of observations have a distance at or below this value.
0.3393
SAM to mean
?Spectral Angle Mapper: angle to the mean spectrum, in radians. Smaller angles indicate more similar shapes.
0.17133
SAM p95
?95th percentile of spectral angles to the dataset mean, in radians. It is less sensitive to overall amplitude than RMS.
0.37314
Affine offset p95
?95th percentile of absolute fitted offsets when matching each spectrum to the dataset mean.
0.4507
Affine gain p95 Δ
?95th percentile of the absolute gain deviation from 1 in the fit to the mean spectrum.
0.53536
Affine residual p95
?95th percentile of residual RMS after gain and offset correction to the mean spectrum.
0.090955
Xcorr lag p95
?95th percentile of the absolute cross-correlation shift relative to the mean spectrum, in spectral channels.
5

Outliers & repeatability

Unusual spectra and within-ID dispersion. Repeated IDs do not by themselves establish technical repetitions. Reading guide

PCA Q p95/median
?Q (squared prediction error) is variation left outside the retained PCA subspace. The displayed ratio compares its 95th percentile with its median.
6.5
Hotelling T2 p95/median
?Hotelling T² measures distance within the retained PCA subspace, scaled by component variance. Ratio of the 95th percentile to the median.
6.2
Mahalanobis H p95/median
?Distance from the PCA-score center after variance scaling. Ratio of the 95th percentile to the median; large values identify relatively extreme spectra.
2.5
Repeat groups
?Number of IDs with several observations. Source documentation is needed to decide whether these are technical repeats, positions or time points.
0

Dimensionality (PCA)

Principal component analysis (PCA) compresses correlated wavelengths into components. This describes spectral variation, without using prediction targets. Reading guide

Effective rank
?Entropy-based summary of the variance distribution across components. Larger values indicate variation spread across more dimensions.
1.9
PCs → 95% var
?Number of principal components needed to explain 95% of spectral variance, when reached by the computed projection.
3
PCs → 99% var
?Number of principal components needed to explain 99% of spectral variance, when reached by the computed projection.
4
Top-10 cum. var
?Fraction of spectral variance explained by up to the first ten computed principal components, displayed as a percentage.
100.0%
Computed metric scores 30highest alert 1.00

Value is the measured quantity; alert score is its heuristic mapping to 0–1. Levels are weak below 0.42, medium from 0.42, and strong from 0.72. These are display bands, not calibrated acceptance thresholds. A dash means unavailable, never zero. Possible causes are suggestions to investigate. Use each metric’s ? control for its meaning and scoring: hover for a hint, or expand it with a tap, Enter or Space.

FamilyComputed metricValueAlert scoreHeuristic levelDescriptive interpretationPossible causes to verifyCalculation / scoring
Data integrityInvalid/masked cell count (owner report)
?Count of cells reported unavailable by the native owner under its null or validity mask. This count is distinct from the historical NaN ratio; consult the recorded population and denominator. Scoring: No alert mapping is defined for this metric; an unavailable score does not mean a zero measured value. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
integrity.missing_cells		——not calculableNot enough information to score this metric on this dataset.Missing data or validity mask; not evidence of an acquisition causeNative Preflight count of cells unavailable under x/null and valid_mask; denominator stays in native_metric.denominators.n_cells. Not legacy NaN ratio.
Data integrityNaN ratio
?Missing spectral cells divided by all spectral cells, displayed as a percentage. Scoring: alert = min(1, nan_ratio / 0.05). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
integrity.nan_ratio		0%0.00weakFull spectrumAcquisition/export errorcount(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Data integrityInf count
?Number of positive or negative infinite spectral values; investigate invalid calculations or export. Scoring: alert = min(1, inf_count / 1). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
integrity.inf_count		00.00weakNormalInvalid calculationscount(isinf(X))alert = min(1, inf_count / 1)
Data integrityZero ratio
?Exactly zero spectral cells divided by all cells. Zero can be a valid measurement and is not automatically missing. Scoring: alert = min(1, zero_ratio / 0.05). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
integrity.zero_ratio		0%0.00weakNormalExport, saturationcount(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Overall amplitudeMean reflectance
?Average signal over observations and channels. The historical key mean_reflectance may also store other signal representations. Scoring: alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
amplitude.mean_reflectance		0.353450.63mediumAtypical value: Too light / background visible or Too darkBackground, geometrymean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Overall amplitudeArea under curve
?Trapezoidal integral of the mean spectrum over the sorted recorded axis; its unit is signal multiplied by axis unit. Scoring: alert reuses baseline/shape drift because area scale depends on axis and units. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
amplitude.area_under_curve		741.090.63mediumAtypical value: Illumination difference or NormalProbe distancetrapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Overall amplitudePeak-to-peak (PTP)
?Peak-to-peak: maximum minus minimum of the mean spectrum. Low values indicate little variation along the mean spectrum. Scoring: alert increases when dynamic range is abnormally flat. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
amplitude.peak_to_peak		0.716710.00weakStrong variabilitySaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Overall amplitudeVariance
?Variance of all finite spectral cells, in squared signal units. It combines variation across observations and channels; a small value can describe a flat signal. Scoring: alert increases when variance/dynamic range is abnormally flat. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
amplitude.variance		0.0552630.00weakNormal or heterogeneousBad contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
NoiseRoughness sigma proxy
?Historical roughness sigma proxy from robust second differences. Narrow genuine bands can also raise it; this is not repeat-measured instrument noise. Scoring: alert = noise_rms / signal_scale, saturated at 5%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
noise.roughness_sigma_proxy		7.8231e-050.00weakStableLamp, detectormedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
NoiseRoughness S/N proxy
?Average absolute signal divided by the roughness proxy. Higher means smoother signal relative to its amplitude, not certified instrument SNR. Scoring: alert decreases with SNR dB; >=40 dB is treated as low alert. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
noise.roughness_signal_to_noise_proxy		45180.00weakGood signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
NoiseBandwise roughness S/N proxy
?Minimum channel-wise ratio of absolute mean signal to local second-difference roughness. A smaller ratio identifies a weak or rough spectral region; genuine narrow bands and near-zero signal can also lower it. This is not certified instrument SNR. Scoring: alert decreases with worst-band SNR dB; >=35 dB is treated as low alert. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
noise.bandwise_roughness_signal_to_noise_proxy_min		135.670.00weakReliable zoneDetectormin(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Local artifactsSpike count
?Local narrow peaks flagged by the second-difference heuristic; real sharp spectral bands may also be flagged. Scoring: alert follows spike_rate, saturated at 1%. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.spike_count		51,4331.00strongArtifactsCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Local artifactsSpike rate
?Fraction of tested second-difference positions flagged as spikes, displayed as a percentage. Scoring: alert = min(1, spike_rate / 0.01). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.spike_rate		4.91%1.00strongSuspicious spectrumInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Local artifactsJump count
?Adjacent-channel changes flagged by the first-difference heuristic, potentially including detector joins. Scoring: alert follows jump_rate, saturated at 1%. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.jump_count		15,5091.00strongDetector connectionSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Local artifactsJump rate
?Fraction of tested adjacent-channel differences flagged as jumps, displayed as a percentage. Scoring: alert = min(1, jump_rate / 0.01). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.jump_rate		1.48%1.00strongSpectral problemCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Local artifactsClip fraction
?Fraction of finite cells near the observed global minimum or maximum; inspect detector limits before concluding saturation. Scoring: alert = min(1, clip_fraction / 0.01). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
artefacts.clip_fraction		0.00019%0.00weakNormalDetector saturatedfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Spectral formBaseline slope
?Linear slope fitted to the mean spectrum over an axis normalized to 0–1. It describes total baseline change, not change per physical axis unit. Scoring: alert = abs(slope / signal_scale), saturated at 0.5. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
shape.baseline_slope		-0.226850.63mediumDriftIlluminationlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Spectral formCurvature RMS
?Median per-spectrum RMS of second differences, summarizing curvature and high-frequency variation. Scoring: alert = curvature_rms / signal_scale, saturated at 1%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
shape.curvature_rms		0.00147150.21weakSmoothBackground, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Spectral formD1 RMS
?Median per-spectrum RMS of adjacent-channel first differences; reflects both real spectral structure and artefacts. Scoring: alert = d1_rms / signal_scale, saturated at 5%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
shape.d1_rms		0.00332120.09weakFlatBiology or artifactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Multivariate outliersPCA Q (SPE)
?Q (squared prediction error) is variation left outside the retained PCA subspace. The displayed ratio compares its 95th percentile with its median. Scoring: alert = min(1, pca_q_ratio / 8). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
outliers.pca_q_ratio		6.51150.81strongAtypical spectrumArtifact, mixturep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Multivariate outliersHotelling T²
?Hotelling T² measures distance within the retained PCA subspace, scaled by component variance. Ratio of the 95th percentile to the median. Scoring: alert = min(1, hotelling_t2_ratio / 8). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
outliers.hotelling_t2_ratio		6.17340.77strongExtreme but consistentNatural variabilityp95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Multivariate outliersMahalanobis H
?Distance from the PCA-score center after variance scaling. Ratio of the 95th percentile to the median; large values identify relatively extreme spectra. Scoring: alert = min(1, mahalanobis_h_ratio / 4). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
outliers.mahalanobis_h_ratio		2.48460.62mediumGlobal outlierDifferent domainp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparison to average spectrumRMS to mean spectrum
?95th percentile of distances to the mean spectrum: 95% of observations have a distance at or below this value. Scoring: alert = RMS_p95 / signal_scale, saturated at 25%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
reference.rms_to_mean_spectrum_p95		0.33931.00strongDifferent spectrumDomain shiftp95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparison to average spectrumSpectral Angle Mapper (SAM)
?95th percentile of spectral angles to the dataset mean, in radians. It is less sensitive to overall amplitude than RMS. Scoring: alert = min(1, SAM_p95 / 0.35 rad). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
reference.sam_to_mean_spectrum_p95		0.373141.00strongDifferent shapeBackground, geometryp95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RepeatabilityRMS intra-ID
?Within-ID root mean square deviation from the group's mean spectrum, in signal units. Repeated IDs require documentation before this spread can be called technical repeatability. Scoring: alert = RMS_intra_ID / signal_scale, saturated at 10%. Signal scale is the larger of the absolute mean level and absolute peak-to-peak range, with a floor of 1e-12. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
repeatability.rms_intra_id		——not calculableNot enough information to score this metric on this dataset.Positioningmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RepeatabilitySAM intra-ID
?Within-ID spectral angle to the group's mean spectrum, in radians. Repeated IDs require documentation before this spread can be called technical repeatability. Scoring: alert = min(1, SAM_intra_ID / 0.15 rad). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
repeatability.sam_intra_id		——not calculableNot enough information to score this metric on this dataset.Acquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RepeatabilityCV intra-ID
?Within-ID coefficient of variation: standard deviation relative to absolute mean; unstable near zero signal. Repeated IDs require documentation before this spread can be called technical repeatability. Scoring: alert = min(1, CV_intra_ID / 0.25). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
repeatability.cv_intra_id		——not calculableNot enough information to score this metric on this dataset.Operatormedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure of the datasetPCA score density
?Inverse median distance to nearby observations in retained PCA-score space. Larger raw values mean more closely spaced observations; scale depends on the PCA representation. The alert uses density variation, so raw density alone does not establish an anomaly. Scoring: alert follows density_cv/profile structure complexity, not raw density alone. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
structure.pca_score_density		2.44071.00strongSubpopulationsDifferent batches1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure of the datasetkNN distance ratio (not LOF)
?95th percentile of mean k-nearest-neighbor distance divided by the population median of that distance, in PCA-score space. Values above 1 indicate an upper tail with more isolated observations; valid rare spectra can also be isolated. This distance ratio is not the Local Outlier Factor algorithm. Scoring: alert = min(1, max(0, kNN_distance_ratio_p95 - 1) / 2). Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
structure.knn_distance_ratio_p95		3.22741.00strongIsolated spectrumRare casesp95(mean kNN distance / median population mean kNN distance) in PCA-score space; not LOFalert = min(1, max(0, kNN_distance_ratio_p95 - 1) / 2)
Structure of the datasetIsolation Forest score
?95th percentile of the negated Isolation Forest score in retained PCA-score space. Larger raw scores indicate easier isolation under that model; the scale is implementation-dependent. The displayed alert reuses structure complexity rather than directly mapping this raw score. Scoring: alert follows structure complexity; raw score is implementation-dependent. Higher alert scores indicate stronger heuristic concern, not a fault probability or an acceptance decision. A dash means unavailable.
structure.isolation_forest_score_p95		0.587131.00strongAtypical spectrumVarious causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent

PCA score plot — explore spectral variation

Source ASD standardized on XDS absorbance: 800 of 1,000 source observations are plotted. PC1 captures 78.9% and PC2 15.7% of the reported spectral variance: 94.6% together; 5.4% remains outside this view.

Principal component analysis (PCA) summarizes spectral differences without using targets. The horizontal PC1 axis captures the largest variation; vertical PC2 captures the next largest direction of variation. Each dot is one observation; repeated measurements need not be distinct samples. Scores are projection coordinates, not wavelengths or target values. Nearby dots have similar projections; separate groups or isolated dots are leads to inspect in the original spectra and acquisition metadata. Overlapping dots can differ in omitted dimensions. The dots have no class or batch labels, so this view cannot identify groups, confirm defective observations, explain a chemical cause or measure prediction accuracy.

ASD standardized on XDS absorbance PCA score plot-30-20-10010-5.0-2.50.02.55.0PC1 2.51 · PC2 0.1434PC1 0.1002 · PC2 2.496PC1 -0.7952 · PC2 2.799PC1 1.317 · PC2 1.298PC1 1.625 · PC2 -0.8551PC1 2.626 · PC2 -0.98PC1 2.833 · PC2 -0.989PC1 1.274 · PC2 -0.06921PC1 1.715 · PC2 -0.17PC1 1.704 · PC2 -0.2415PC1 1.47 · PC2 -0.5837PC1 1.207 · PC2 0.1412PC1 2.08 · PC2 -0.2162PC1 0.1763 · PC2 0.361PC1 1.21 · PC2 -0.3149PC1 0.1536 · PC2 0.6082PC1 0.8052 · PC2 0.6011PC1 1.935 · PC2 -0.2003PC1 0.06449 · PC2 -0.09643PC1 1.686 · PC2 -0.06564PC1 0.4385 · PC2 0.1037PC1 -1.802 · PC2 -1.511PC1 1.21 · PC2 -0.09037PC1 1.663 · PC2 -0.1572PC1 1.919 · PC2 -0.708PC1 0.7419 · PC2 0.1023PC1 0.9138 · PC2 -0.3152PC1 1.906 · PC2 -0.2735PC1 0.1259 · PC2 -1.272PC1 1.979 · PC2 -0.9424PC1 1.784 · PC2 0.005285PC1 1.749 · PC2 0.4508PC1 1.351 · PC2 0.3877PC1 0.6227 · PC2 -0.5309PC1 0.7362 · PC2 -0.4847PC1 1.509 · PC2 -0.6725PC1 -1.031 · PC2 0.1973PC1 2.899 · PC2 -1.797PC1 0.4494 · PC2 -1.278PC1 0.7291 · PC2 -0.5545PC1 2.573 · PC2 -1.125PC1 2.399 · PC2 -0.9079PC1 2.488 · PC2 -0.3122PC1 0.06084 · PC2 -1.149PC1 0.5695 · PC2 -1.079PC1 -0.3001 · PC2 -0.4478PC1 1.358 · PC2 -0.723PC1 1.52 · PC2 -1.247PC1 -0.3657 · PC2 -1.603PC1 1.811 · PC2 -0.9135PC1 0.5706 · PC2 -1.122PC1 1.908 · PC2 -1.481PC1 1.857 · PC2 -0.9761PC1 3.555 · PC2 -2.507PC1 0.2653 · PC2 -2.222PC1 3.106 · PC2 -1.578PC1 -12.27 · PC2 -4.725PC1 0.9137 · PC2 -1.624PC1 -0.3045 · PC2 -0.7994PC1 0.9977 · PC2 0.163PC1 0.1697 · PC2 -0.9808PC1 0.6828 · PC2 -1.563PC1 1.248 · PC2 -1.141PC1 -5.569 · PC2 -2.635PC1 1.442 · PC2 -1.505PC1 1.643 · PC2 -1.722PC1 -1.571 · PC2 -1.462PC1 1.316 · PC2 -0.71PC1 2.483 · PC2 -0.1687PC1 0.0918 · PC2 -1.403PC1 2.882 · PC2 -1.301PC1 2.854 · PC2 -1.092PC1 -14.1 · PC2 -2.169PC1 -14.3 · PC2 -2.547PC1 1.902 · PC2 -1.558PC1 2.23 · PC2 -0.7871PC1 -0.4193 · PC2 -1.985PC1 3.28 · PC2 -1.602PC1 0.2695 · PC2 -0.4498PC1 2.136 · PC2 -0.5884PC1 2.276 · PC2 -0.7943PC1 -0.08403 · PC2 -0.3167PC1 0.1312 · PC2 0.335PC1 1.767 · PC2 0.01941PC1 0.3469 · PC2 0.0284PC1 -1.229 · PC2 -0.1938PC1 1.263 · PC2 -0.15PC1 -0.5848 · PC2 0.05036PC1 -0.3911 · PC2 -0.5352PC1 1.074 · PC2 -0.3575PC1 0.2516 · PC2 -0.7815PC1 -0.3659 · PC2 -0.1514PC1 1.221 · PC2 -0.7285PC1 1.353 · PC2 -0.6982PC1 1.261 · PC2 -0.6791PC1 2.054 · PC2 -0.5848PC1 2.007 · PC2 -0.3703PC1 1.004 · PC2 -1.979PC1 0.552 · PC2 -0.1658PC1 1.766 · PC2 -0.6501PC1 0.6036 · PC2 -0.4185PC1 0.9045 · PC2 -0.09821PC1 0.4596 · PC2 -0.1318PC1 0.4399 · PC2 -0.4831PC1 1.991 · PC2 -0.3707PC1 2.248 · PC2 -1.499PC1 3.085 · PC2 -1.572PC1 3.034 · PC2 -1.925PC1 3.237 · PC2 -1.542PC1 1.578 · PC2 -0.4496PC1 2.396 · PC2 -1.551PC1 1.429 · PC2 -0.7069PC1 2.103 · PC2 -1.073PC1 3.577 · PC2 -2.164PC1 3.816 · PC2 -1.842PC1 3.797 · PC2 -1.927PC1 1.965 · PC2 -0.6609PC1 1.226 · PC2 0.03601PC1 0.4912 · PC2 1.912PC1 0.7318 · PC2 0.593PC1 -0.4118 · PC2 0.6602PC1 0.3177 · PC2 0.684PC1 0.663 · PC2 2.338PC1 1.283 · PC2 1.521PC1 0.1052 · PC2 2.49PC1 -0.6609 · PC2 2.817PC1 1.505 · PC2 1.315PC1 2.337 · PC2 0.008242PC1 0.07503 · PC2 1.496PC1 -0.5528 · PC2 2PC1 -1.372 · PC2 2.241PC1 0.951 · PC2 0.1861PC1 1.497 · PC2 0.07991PC1 0.3411 · PC2 1.046PC1 0.7972 · PC2 0.3475PC1 0.5894 · PC2 0.9048PC1 1.09 · PC2 0.6664PC1 1.546 · PC2 -0.2138PC1 1.172 · PC2 0.2189PC1 0.4686 · PC2 0.9128PC1 2.513 · PC2 -1.514PC1 3.155 · PC2 -0.8647PC1 2.359 · PC2 -0.2352PC1 3.099 · PC2 -0.6915PC1 3.499 · PC2 -0.8489PC1 2.51 · PC2 -1.061PC1 2.046 · PC2 -0.3198PC1 3.557 · PC2 -2.049PC1 2.976 · PC2 -1.575PC1 3.237 · PC2 -1.776PC1 0.846 · PC2 1.695PC1 1.665 · PC2 0.625PC1 0.7849 · PC2 1.793PC1 2.34 · PC2 -0.08955PC1 2.121 · PC2 -0.4437PC1 2.062 · PC2 0.2923PC1 1.937 · PC2 0.1711PC1 2.15 · PC2 0.2384PC1 1.909 · PC2 0.2406PC1 2.739 · PC2 0.0107PC1 2.671 · PC2 0.04031PC1 2.522 · PC2 0.2398PC1 1.743 · PC2 0.4519PC1 1.884 · PC2 0.282PC1 1.644 · PC2 0.7089PC1 2.187 · PC2 0.5961PC1 3.028 · PC2 -0.4092PC1 2.296 · PC2 0.2267PC1 2.455 · PC2 0.06676PC1 1.91 · PC2 0.0555PC1 1.82 · PC2 0.0431PC1 1.858 · PC2 0.1138PC1 -0.5843 · PC2 0.5645PC1 0.1184 · PC2 1.135PC1 -0.4372 · PC2 1.934PC1 -2.466 · PC2 3.019PC1 -0.7255 · PC2 1.482PC1 -1.356 · PC2 1.893PC1 -2.189 · PC2 2.222PC1 -2.561 · PC2 3.97PC1 -1.468 · PC2 1.389PC1 -0.8557 · PC2 1.491PC1 -0.9836 · PC2 1.01PC1 0.1204 · PC2 0.9059PC1 -0.7845 · PC2 0.8486PC1 1.795 · PC2 -0.06981PC1 -0.2448 · PC2 0.3738PC1 1.177 · PC2 -0.3477PC1 -2.066 · PC2 2.054PC1 2.284 · PC2 -0.2677PC1 -1.051 · PC2 0.9808PC1 0.5259 · PC2 0.6071PC1 0.0458 · PC2 0.8527PC1 3.577 · PC2 -1.466PC1 3.912 · PC2 -1.538PC1 2.124 · PC2 0.447PC1 1.184 · PC2 0.0547PC1 1.914 · PC2 -1.897PC1 3.248 · PC2 -1.494PC1 3.264 · PC2 -1.404PC1 1.886 · PC2 0.2462PC1 1.578 · PC2 0.3114PC1 2.916 · PC2 -1.507PC1 2.284 · PC2 -0.5013PC1 -0.5843 · PC2 -0.7754PC1 -0.2728 · PC2 -1.966PC1 -1.944 · PC2 2.549PC1 -2.585 · PC2 1.846PC1 -1.766 · PC2 -2.775PC1 2.567 · PC2 -2.606PC1 -10.64 · PC2 -0.2191PC1 -2.179 · PC2 -0.5828PC1 1.58 · PC2 0.07262PC1 -1.809 · PC2 2.145PC1 0.99 · PC2 -1.04PC1 -12.63 · PC2 -3.42PC1 -6.885 · PC2 -1.328PC1 -0.55 · PC2 0.9848PC1 1.836 · PC2 -1.257PC1 1.69 · PC2 -1.498PC1 1.962 · PC2 -1.362PC1 1.951 · PC2 -1.364PC1 0.9187 · PC2 -1.42PC1 2.644 · PC2 -1.212PC1 2.338 · PC2 -0.938PC1 2.745 · PC2 -1.574PC1 2.803 · PC2 -1.267PC1 1.764 · PC2 -0.8848PC1 2.923 · PC2 -1.328PC1 2.873 · PC2 -1.154PC1 2.046 · PC2 -0.5356PC1 2.364 · PC2 -0.293PC1 2.69 · PC2 -0.5702PC1 1.73 · PC2 -0.6432PC1 1.737 · PC2 -0.5725PC1 2.523 · PC2 -0.06451PC1 -0.01569 · PC2 1.35PC1 1.302 · PC2 -0.2334PC1 2.445 · PC2 -0.1683PC1 2.76 · PC2 -0.5633PC1 2.961 · PC2 -1.205PC1 2.717 · PC2 -0.7314PC1 1.924 · PC2 -0.2891PC1 3.653 · PC2 -1.637PC1 4.003 · PC2 -1.645PC1 3.351 · PC2 -1.489PC1 3.322 · PC2 -1.949PC1 3.555 · PC2 -1.459PC1 3.476 · PC2 -1.817PC1 3.412 · PC2 -1.418PC1 2.868 · PC2 -1.677PC1 3.23 · PC2 -2.077PC1 1.746 · PC2 -0.6221PC1 2.754 · PC2 -1.515PC1 2.482 · PC2 -0.6435PC1 2.95 · PC2 -1.288PC1 2.758 · PC2 -1.155PC1 2.95 · PC2 -1.261PC1 2.978 · PC2 -1.349PC1 3.241 · PC2 -1.12PC1 2.777 · PC2 -1.291PC1 2.926 · PC2 -1.357PC1 3.017 · PC2 -1.408PC1 2.8 · PC2 -1.233PC1 2.852 · PC2 -1.363PC1 2.725 · PC2 -1.4PC1 -3.285 · PC2 -1.533PC1 -2.381 · PC2 1.894PC1 -2.805 · PC2 -3.003PC1 -0.4425 · PC2 0.2861PC1 -0.05827 · PC2 0.1193PC1 -0.3295 · PC2 1.578PC1 -9.06 · PC2 -2.247PC1 2.126 · PC2 -1.415PC1 2.284 · PC2 -1.075PC1 1.782 · PC2 -1.17PC1 2.872 · PC2 -1.063PC1 1.916 · PC2 3.63PC1 -4.891 · PC2 1.722PC1 -1.977 · PC2 0.2483PC1 -3.963 · PC2 0.4355PC1 -2.976 · PC2 0.1203PC1 -3.429 · PC2 0.7495PC1 -3.401 · PC2 1.334PC1 -7.927 · PC2 -1.46PC1 -2.47 · PC2 -0.759PC1 -3.714 · PC2 1.354PC1 -4.705 · PC2 0.362PC1 -4.33 · PC2 -0.2923PC1 -1.968 · PC2 0.947PC1 -4.717 · PC2 -1.363PC1 -1.472 · PC2 2.765PC1 1.873 · PC2 -2.205PC1 -1.509 · PC2 -3.612PC1 1.904 · PC2 -1.772PC1 7.982 · PC2 -3.512PC1 -2.068 · PC2 3.468PC1 -0.796 · PC2 1.336PC1 0.7471 · PC2 2.417PC1 -1.512 · PC2 -0.3554PC1 -10.42 · PC2 1.071PC1 2.673 · PC2 -1.119PC1 1.372 · PC2 -0.6306PC1 4.299 · PC2 0.966PC1 1.169 · PC2 -3.66PC1 2.265 · PC2 -0.009809PC1 3.253 · PC2 3.396PC1 3.292 · PC2 3.541PC1 2.789 · PC2 3.881PC1 3.445 · PC2 3.56PC1 2.952 · PC2 3.871PC1 2.656 · PC2 3.758PC1 2.546 · PC2 3.772PC1 2.689 · PC2 3.321PC1 2.469 · PC2 3.404PC1 -0.548 · PC2 2.599PC1 0.6717 · PC2 1.964PC1 -0.2529 · PC2 2.019PC1 0.4065 · PC2 0.5317PC1 0.7198 · PC2 0.4263PC1 0.1189 · PC2 0.5087PC1 -9.653 · PC2 1.019PC1 -11.58 · PC2 0.9668PC1 -10.96 · PC2 0.8957PC1 -10.26 · PC2 1.177PC1 -10.71 · PC2 1.689PC1 -10.19 · PC2 1.113PC1 -9.692 · PC2 1.251PC1 -10.28 · PC2 1.223PC1 -9.51 · PC2 1.266PC1 -9.527 · PC2 1.383PC1 -0.9984 · PC2 -1.722PC1 -0.7651 · PC2 -2.222PC1 -0.8252 · PC2 -2.084PC1 -4.028 · PC2 -1.414PC1 -1.675 · PC2 -1.407PC1 -0.7807 · PC2 -1.988PC1 -0.8761 · PC2 -1.859PC1 -2.079 · PC2 -1.348PC1 -4.116 · PC2 -1.389PC1 -1.679 · PC2 0.9983PC1 0.8412 · PC2 -0.1563PC1 0.6006 · PC2 -0.06743PC1 0.615 · PC2 -0.5132PC1 0.3021 · PC2 -2.251PC1 3.757 · PC2 -1.488PC1 0.797 · PC2 -1.855PC1 2.003 · PC2 -2.024PC1 1.038 · PC2 -1.871PC1 1.658 · PC2 -1.665PC1 3.731 · PC2 -1.82PC1 4.151 · PC2 -1.911PC1 1.639 · PC2 -0.7427PC1 2.067 · PC2 -0.3643PC1 3.07 · PC2 -1.305PC1 1.391 · PC2 -0.4926PC1 1.712 · PC2 -0.6753PC1 1.632 · PC2 -0.6293PC1 1.498 · PC2 -0.463PC1 2.592 · PC2 -0.6671PC1 2.291 · PC2 -0.6883PC1 2.342 · PC2 -0.7095PC1 3.677 · PC2 -1.676PC1 3.099 · PC2 -2.142PC1 3.731 · PC2 -2.294PC1 3.523 · PC2 -2.005PC1 3.566 · PC2 -1.896PC1 4.157 · PC2 -2.284PC1 3.143 · PC2 -1.462PC1 5.49 · PC2 -2.035PC1 3.095 · PC2 -1.493PC1 2.849 · PC2 -1.066PC1 2.863 · PC2 -1.386PC1 1.743 · PC2 -0.8972PC1 3.213 · PC2 -1.176PC1 3.342 · PC2 -1.719PC1 3.253 · PC2 -1.718PC1 3.744 · PC2 -1.944PC1 3.785 · PC2 -1.797PC1 2.841 · PC2 -1.19PC1 2.13 · PC2 -0.4851PC1 2.949 · PC2 -1.174PC1 2.556 · PC2 -1.265PC1 2.909 · PC2 -1.17PC1 2.964 · PC2 -1.212PC1 2.555 · PC2 -1.293PC1 2.101 · PC2 -1.214PC1 3.849 · PC2 -1.6PC1 3.433 · PC2 -1.221PC1 3.33 · PC2 -1.267PC1 3.279 · PC2 -1.355PC1 3.07 · PC2 -1.375PC1 3.265 · PC2 -1.388PC1 4.173 · PC2 -1.364PC1 1.627 · PC2 0.7063PC1 1.312 · PC2 0.6969PC1 2.622 · PC2 0.2928PC1 1.775 · PC2 0.7374PC1 1.268 · PC2 1PC1 1.256 · PC2 1.136PC1 1.975 · PC2 0.2795PC1 1.789 · PC2 0.2991PC1 1.374 · PC2 0.9144PC1 1.05 · PC2 1.036PC1 2.954 · PC2 -1.372PC1 2.312 · PC2 -1.224PC1 3.074 · PC2 -1.406PC1 1.228 · PC2 -0.3867PC1 1.723 · PC2 -0.319PC1 2.146 · PC2 -0.4295PC1 3.096 · PC2 -1.447PC1 2.671 · PC2 -1.342PC1 2.97 · PC2 -1.099PC1 3.956 · PC2 -1.495PC1 3.448 · PC2 -0.6013PC1 3.528 · PC2 -0.901PC1 5.737 · PC2 -2.156PC1 3.863 · PC2 -1.465PC1 4.369 · PC2 -1.501PC1 3.537 · PC2 -0.8965PC1 2.875 · PC2 -0.4126PC1 2.913 · PC2 -0.6916PC1 2.84 · PC2 -0.6747PC1 3.536 · PC2 -1.366PC1 3.993 · PC2 -1.627PC1 1.137 · PC2 0.3218PC1 4.094 · PC2 -1.711PC1 2.348 · PC2 0.1304PC1 0.3412 · PC2 -0.6764PC1 2.894 · PC2 -0.4724PC1 2.7 · PC2 -0.3828PC1 2.779 · PC2 -1.053PC1 1.762 · PC2 -0.6083PC1 2.83 · PC2 -0.2732PC1 2.273 · PC2 -1.006PC1 2.492 · PC2 -0.9137PC1 2.251 · PC2 -0.9867PC1 2.249 · PC2 -0.416PC1 2.554 · PC2 -0.966PC1 1.007 · PC2 -0.4357PC1 3.99 · PC2 -1.359PC1 2.791 · PC2 -0.4349PC1 2.537 · PC2 -0.7308PC1 2.501 · PC2 -0.5593PC1 3.332 · PC2 -1.47PC1 2.114 · PC2 0.04429PC1 3.352 · PC2 -1.36PC1 1.997 · PC2 -0.3118PC1 3.011 · PC2 -0.9424PC1 2.105 · PC2 -0.3654PC1 2.345 · PC2 -0.2251PC1 1.093 · PC2 0.5628PC1 0.8952 · PC2 0.02934PC1 0.6291 · PC2 1.297PC1 0.7042 · PC2 -0.3978PC1 3.228 · PC2 -1.557PC1 2.601 · PC2 -1.553PC1 1.893 · PC2 -1.016PC1 1.935 · PC2 -0.4661PC1 -1.834 · PC2 4.404PC1 -0.9604 · PC2 3.618PC1 -0.1357 · PC2 2.794PC1 0.3783 · PC2 3.069PC1 -0.4092 · PC2 3.095PC1 -0.851 · PC2 3.668PC1 0.382 · PC2 2.856PC1 -0.6352 · PC2 2.49PC1 -0.9618 · PC2 4.092PC1 -2.633 · PC2 4.945PC1 0.4997 · PC2 2.189PC1 0.2346 · PC2 2.925PC1 -1.11 · PC2 3.997PC1 -1.377 · PC2 3.498PC1 0.0412 · PC2 2.979PC1 -1.192 · PC2 4.848PC1 -0.3405 · PC2 4.427PC1 -0.7128 · PC2 3.896PC1 -1.088 · PC2 4.027PC1 0.217 · PC2 2.987PC1 0.7081 · PC2 2.455PC1 -0.5376 · PC2 2.816PC1 -0.1542 · PC2 2.525PC1 -0.3591 · PC2 3.271PC1 -2.109 · PC2 2.836PC1 -0.727 · PC2 2.779PC1 -1.253 · PC2 2.946PC1 -0.07053 · PC2 2.56PC1 0.4295 · PC2 2.51PC1 -0.002182 · PC2 2.419PC1 -0.2155 · PC2 2.403PC1 0.3317 · PC2 2.637PC1 1.063 · PC2 3.218PC1 -1.484 · PC2 1.936PC1 -0.1176 · PC2 2.385PC1 0.6519 · PC2 3.019PC1 0.4187 · PC2 2.613PC1 0.1617 · PC2 3.229PC1 -0.02236 · PC2 2.565PC1 0.3685 · PC2 3.059PC1 -0.1509 · PC2 2.492PC1 -1.218 · PC2 2.656PC1 2.291 · PC2 -0.8817PC1 -0.532 · PC2 0.1996PC1 2.251 · PC2 -0.9301PC1 1.956 · PC2 -1.083PC1 0.7364 · PC2 -0.1716PC1 2.139 · PC2 -1.207PC1 2.597 · PC2 -1.349PC1 2.813 · PC2 -2.959PC1 3.67 · PC2 -2.821PC1 3.991 · PC2 -3.076PC1 2.025 · PC2 -1.206PC1 3.242 · PC2 -1.13PC1 2.522 · PC2 -1.122PC1 1.314 · PC2 -0.553PC1 2.089 · PC2 -1.728PC1 2.055 · PC2 -0.744PC1 2.242 · PC2 -1.295PC1 2.103 · PC2 -1.32PC1 2.777 · PC2 -1.804PC1 0.2256 · PC2 -1.665PC1 0.231 · PC2 -1.03PC1 0.947 · PC2 -1.161PC1 0.8442 · PC2 -0.01746PC1 1.35 · PC2 -0.7228PC1 0.9183 · PC2 -0.8053PC1 1.933 · PC2 -1.605PC1 1.842 · PC2 -1.908PC1 2.344 · PC2 -1.945PC1 1.289 · PC2 -1.994PC1 1.369 · PC2 -1.53PC1 1.174 · PC2 -1.387PC1 1.182 · PC2 -1.495PC1 1.932 · PC2 -0.6982PC1 0.8746 · PC2 -0.1605PC1 -12.68 · PC2 -3.479PC1 -16.86 · PC2 -3.735PC1 -12.97 · PC2 -3.181PC1 -10.66 · PC2 -2.601PC1 -13.76 · PC2 -3.097PC1 -13.38 · PC2 -3.42PC1 -13.82 · PC2 -3.258PC1 -14.05 · PC2 -2.325PC1 -14.01 · PC2 -3.053PC1 -13.29 · PC2 -2.898PC1 -11.23 · PC2 -2.49PC1 -11.68 · PC2 -2.275PC1 -13.49 · PC2 -1.776PC1 -14.56 · PC2 -2.699PC1 -12.09 · PC2 -2.763PC1 -11.83 · PC2 -1.289PC1 -11.04 · PC2 -0.1954PC1 -13.62 · PC2 -1.208PC1 -11.44 · PC2 -3.133PC1 -11.63 · PC2 -1.156PC1 -15.78 · PC2 -1.693PC1 -15.02 · PC2 -2.496PC1 -10.99 · PC2 0.1658PC1 -21.17 · PC2 -3.899PC1 -16.3 · PC2 -2.2PC1 -9.481 · PC2 -3.193PC1 -10.32 · PC2 -0.5288PC1 -11.19 · PC2 -0.8852PC1 -12.67 · PC2 -2.134PC1 -11.15 · PC2 -0.45PC1 -13.29 · PC2 -1.042PC1 -14.03 · PC2 -1.618PC1 -16.92 · PC2 -2.078PC1 -16.02 · PC2 -2.131PC1 -16.93 · PC2 -0.7547PC1 3.499 · PC2 -0.9238PC1 3.248 · PC2 -0.5202PC1 2.546 · PC2 -0.5414PC1 2.717 · PC2 -1.311PC1 2.507 · PC2 -1.479PC1 1.607 · PC2 -0.5585PC1 2.685 · PC2 -3.126PC1 3.122 · PC2 -1.837PC1 4.171 · PC2 -2.442PC1 -0.6873 · PC2 0.0973PC1 1.987 · PC2 -0.8828PC1 1.199 · PC2 -0.8509PC1 -0.1165 · PC2 0.6173PC1 1.503 · PC2 -1.348PC1 1.739 · PC2 -0.7255PC1 2.234 · PC2 -1.28PC1 1.163 · PC2 -1.823PC1 0.1873 · PC2 -0.9405PC1 2.742 · PC2 -1.004PC1 1.003 · PC2 -0.9073PC1 1.014 · PC2 -0.1729PC1 1.255 · PC2 -0.7038PC1 0.1986 · PC2 -0.8692PC1 2.179 · PC2 -0.6905PC1 2.193 · PC2 -1.71PC1 2.787 · PC2 -1.865PC1 2.547 · PC2 -1.957PC1 -1.061 · PC2 -1.364PC1 0.06101 · PC2 -1.416PC1 2.261 · PC2 -1.032PC1 2.212 · PC2 -1.401PC1 1.408 · PC2 -0.8082PC1 -0.6244 · PC2 0.424PC1 4.876 · PC2 -3.87PC1 4.015 · PC2 -3.26PC1 3.906 · PC2 -3.584PC1 3.196 · PC2 -2.091PC1 2.07 · PC2 -1.031PC1 3.359 · PC2 -2.778PC1 3.14 · PC2 -2.606PC1 4.607 · PC2 -3.017PC1 3.359 · PC2 -2.982PC1 2.957 · PC2 -1.54PC1 3.668 · PC2 -3.257PC1 2.697 · PC2 -0.5507PC1 2.157 · PC2 -0.5912PC1 2.379 · PC2 -1.7PC1 3.619 · PC2 -1.987PC1 2.468 · PC2 -1.63PC1 0.6545 · PC2 -0.8818PC1 1.628 · PC2 -0.5895PC1 0.9388 · PC2 -0.3064PC1 2.861 · PC2 -1.223PC1 2.136 · PC2 -1.748PC1 2.818 · PC2 -1.87PC1 -0.6934 · PC2 -1.882PC1 2.649 · PC2 -1.263PC1 2.589 · PC2 -1.952PC1 3.037 · PC2 -1.446PC1 2.468 · PC2 -1.785PC1 0.1147 · PC2 0.6822PC1 1.822 · PC2 -0.2724PC1 2.49 · PC2 -0.4169PC1 3.269 · PC2 -1.448PC1 3.403 · PC2 -1.656PC1 3.071 · PC2 -1.588PC1 3.054 · PC2 -3.562PC1 4.127 · PC2 -2.94PC1 2.529 · PC2 -0.8444PC1 2.025 · PC2 -2.887PC1 2.739 · PC2 -1.374PC1 2.603 · PC2 -0.9742PC1 1.146 · PC2 0.4509PC1 2.739 · PC2 -1.658PC1 2.909 · PC2 -1.816PC1 2.724 · PC2 -1.402PC1 2.696 · PC2 -1.163PC1 2.825 · PC2 -2.075PC1 0.4369 · PC2 -1.708PC1 0.4952 · PC2 -1.411PC1 1.635 · PC2 -1.783PC1 2.474 · PC2 -1.791PC1 0.3435 · PC2 0.3974PC1 2.126 · PC2 -0.7297PC1 1.978 · PC2 -0.6087PC1 2.637 · PC2 -1.692PC1 3.381 · PC2 -1.944PC1 1.303 · PC2 -1.001PC1 0.4096 · PC2 -0.8216PC1 2.014 · PC2 -1.126PC1 2.882 · PC2 -1.248PC1 2.161 · PC2 -1.255PC1 2.805 · PC2 -1.027PC1 2.146 · PC2 -0.658PC1 2.767 · PC2 -1.47PC1 2.354 · PC2 -0.9737PC1 -8.217 · PC2 0.8562PC1 -4.023 · PC2 -0.9048PC1 -11.01 · PC2 -1.364PC1 -10.18 · PC2 -0.6091PC1 -10.01 · PC2 -0.3654PC1 -8.982 · PC2 -0.7708PC1 -10.35 · PC2 -1.075PC1 -9.409 · PC2 -0.7085PC1 -9.832 · PC2 -1.027PC1 -8.273 · PC2 -0.8934PC1 -9.716 · PC2 -0.3279PC1 -9.85 · PC2 -0.4918PC1 -10.17 · PC2 -0.6274PC1 -9.389 · PC2 -0.3791PC1 -8.722 · PC2 -0.3452PC1 -9.621 · PC2 -0.2093PC1 -8.502 · PC2 -0.7168PC1 -16.53 · PC2 -3.558PC1 -1.376 · PC2 1.83PC1 -0.3899 · PC2 1.92PC1 -3.187 · PC2 2.33PC1 -2.579 · PC2 2.299PC1 -3.559 · PC2 2.28PC1 -0.8753 · PC2 2.054PC1 -0.8121 · PC2 1.72PC1 -1.105 · PC2 2.143PC1 -6.419 · PC2 -1.415PC1 -1.144 · PC2 2.46PC1 -0.4035 · PC2 3PC1 -1.232 · PC2 3.024PC1 -0.2311 · PC2 3.267PC1 -1.279 · PC2 3.373PC1 -1.148 · PC2 2.95PC1 -0.7689 · PC2 3.391PC1 -6.28 · PC2 2.93PC1 -1.004 · PC2 2.996PC1 -1.062 · PC2 2.946PC1 -2.368 · PC2 2.964PC1 -1.029 · PC2 3.058PC1 -2.562 · PC2 3.195PC1 -0.6341 · PC2 3.028PC1 -1.477 · PC2 3.063PC1 -3.68 · PC2 1.721PC1 -3.577 · PC2 1.114PC1 -2.816 · PC2 1.39PC1 -3.192 · PC2 1.609PC1 -4.57 · PC2 1.229PC1 -2.675 · PC2 1.707PC1 -3.13 · PC2 2.656PC1 -0.2384 · PC2 2.853PC1 -2.175 · PC2 2.856PC1 -0.3002 · PC2 2.417PC1 -0.8859 · PC2 2.795PC1 0.6605 · PC2 2.885PC1 0.01247 · PC2 3.354PC1 -1.458 · PC2 2.987PC1 -0.5494 · PC2 2.526PC1 -0.02562 · PC2 3.039PC1 0.2266 · PC2 2.965PC1 2.023 · PC2 2.567PC1 1.705 · PC2 3.046PC1 -0.4757 · PC2 2.124PC1 1.787 · PC2 2.705PC1 -0.937 · PC2 2.855PC1 1.696 · PC2 2.633PC1 0.9368 · PC2 2.487PC1 1.142 · PC2 2.531PC1 1.509 · PC2 2.715PC1 1.568 · PC2 2.576PC1 1.13 · PC2 2.393PC1 1.579 · PC2 2.51PC1 1.302 · PC2 2.333PC1 1.073 · PC2 2.515PC1 -0.037 · PC2 2.353PC1 0.3289 · PC2 2.552PC1 0.1495 · PC2 2.45PC1 0.5002 · PC2 2.76PC1 -0.9001 · PC2 2.755PC1 -0.3451 · PC2 3.301PC1 -1.627 · PC2 3.89PC1 -2.253 · PC2 3.673PC1 -1.966 · PC2 2.877PC1 -5.177 · PC2 1.757PC1 -1.313 · PC2 3.095PC1 -2.464 · PC2 3.981PC1 -3.11 · PC2 2.877PC1 -1.67 · PC2 3.039PC1 -3.201 · PC2 2.925PC1 -2.486 · PC2 2.675PC1 -1 · PC2 3.121PC1 -1.169 · PC2 2.919PC1 -2.393 · PC2 2.233PC1 0.1035 · PC2 3.32PC1 -1.482 · PC2 3.705PC1 -0.07411 · PC2 3.007PC1 -4.173 · PC2 2.125PC1 -0.1713 · PC2 3.168PC1 -1.556 · PC2 3.546PC1 -1.7 · PC2 2.815PC1 -2.997 · PC2 2.373PC1 1.045 · PC2 3.245PC1 -3.025 · PC2 2.422PC1 0.008318 · PC2 3.267PC1 -1.225 · PC2 3.381PC1 -3.292 · PC2 2.636PC1 0.3619 · PC2 3.402PC1 0.6171 · PC2 3.096PC1 1.66 · PC2 3.177PC1 0.7001 · PC2 3.063PC1 1.412 · PC2 2.61PC1 1.422 · PC2 3.076PC1 1.522 · PC2 2.759PC1 1.329 · PC2 2.657PC1 1.456 · PC2 2.711PC1 1.467 · PC2 3.03PC1 1.761 · PC2 2.935PC1 1.682 · PC2 2.791PC1 1.34 · PC2 3.135PC1 1.229 · PC2 3.08PC1 1.743 · PC2 2.645PC1 0.9742 · PC2 3.269PC1 1.856 · PC2 2.698PC1 1.221 · PC2 3.181PC1 1.782 · PC2 2.639PC1 1.371 · PC2 3.175PC1 1.761 · PC2 2.812PC1 1.737 · PC2 3.051PC1 1.585 · PC2 2.941PC1 1.841 · PC2 2.88PC1 2.137 · PC2 2.623PC1 2.24 · PC2 2.676PC1 1.787 · PC2 2.74PC1 1.889 · PC2 2.761PC1 1.923 · PC2 2.75PC1 (78.9%)PC2 (15.7%)800 scores
View plotted PCA coordinates

Point numbers identify rows in this displayed coordinate list, not original observation or sample IDs. Non-integer coordinates use up to five significant digits. Original IDs, component loadings, preprocessing details and the observation selection method are not supplied with this plot.

Plotted observation positions (not original sample IDs)
PointPC1 scorePC2 score
12.51020.14335
20.100212.4962
3-0.795162.799
41.3171.2976
51.625-0.85514
62.6264-0.98
72.8331-0.98903
81.2742-0.069212
91.7145-0.17
101.7041-0.24147
111.4702-0.58373
121.20670.14121
132.0801-0.21618
140.176280.36097
151.2099-0.31492
160.153640.60816
170.805230.60108
181.9353-0.20028
190.064492-0.096427
201.6858-0.065642
210.43850.10372
22-1.8024-1.5106
231.2101-0.090374
241.6634-0.15719
251.9195-0.70796
260.74190.10232
270.91385-0.31518
281.9063-0.27352
290.12592-1.2718
301.979-0.94237
311.78420.0052851
321.74920.45076
331.35140.38772
340.62265-0.53094
350.73615-0.48474
361.5094-0.6725
37-1.03140.1973
382.8989-1.7972
390.44939-1.2784
400.72914-0.55446
412.5726-1.1253
422.3992-0.90787
432.4881-0.31221
440.060841-1.1486
450.56955-1.0785
46-0.30011-0.44776
471.3582-0.72304
481.52-1.247
49-0.36567-1.6034
501.8111-0.9135
510.57063-1.1223
521.9076-1.4805
531.8566-0.97609
543.5552-2.5073
550.26532-2.2222
563.1055-1.5783
57-12.274-4.7253
580.91372-1.6235
59-0.30453-0.79943
600.997650.16296
610.16968-0.98078
620.68281-1.563
631.2482-1.1409
64-5.5694-2.6351
651.4423-1.5048
661.6434-1.7219
67-1.5712-1.4617
681.3161-0.71002
692.4834-0.16875
700.091796-1.4029
712.8822-1.301
722.854-1.0918
73-14.098-2.1691
74-14.298-2.547
751.9015-1.5583
762.2302-0.78713
77-0.41927-1.9853
783.2805-1.6018
790.26945-0.4498
802.1364-0.58844
812.2761-0.79429
82-0.084025-0.31673
830.131220.335
841.76740.019405
850.346880.028404
86-1.2287-0.19378
871.2633-0.15001
88-0.584820.050364
89-0.39108-0.5352
901.0738-0.35749
910.25156-0.78149
92-0.36588-0.15135
931.2209-0.72851
941.3527-0.69817
951.2614-0.67913
962.0545-0.5848
972.007-0.37029
981.0042-1.9786
990.55204-0.16576
1001.7663-0.65013
1010.60362-0.41848
1020.90447-0.09821
1030.45959-0.13182
1040.43989-0.48307
1051.9914-0.37066
1062.2482-1.4992
1073.0846-1.5721
1083.0337-1.9249
1093.2368-1.5424
1101.5783-0.4496
1112.3957-1.5513
1121.4295-0.70694
1132.1032-1.0728
1143.5772-2.1641
1153.8164-1.8424
1163.7969-1.9266
1171.9652-0.66092
1181.22560.036006
1190.491191.9122
1200.731780.59305
121-0.41180.66022
1220.317670.68403
1230.663012.3385
1241.28291.5206
1250.105162.4903
126-0.660882.817
1271.50451.3153
1282.33740.0082421
1290.0750341.4964
130-0.552812.0003
131-1.37242.241
1320.950970.18608
1331.49660.079906
1340.341081.046
1350.797220.34755
1360.589380.90482
1371.09030.66639
1381.5463-0.21382
1391.17210.21895
1400.468620.91279
1412.5134-1.514
1423.1549-0.86466
1432.3588-0.23523
1443.0986-0.69151
1453.4995-0.84891
1462.5105-1.0607
1472.0464-0.31977
1483.5569-2.0487
1492.9755-1.5752
1503.2367-1.7759
1510.845971.6948
1521.66540.62505
1530.784871.7933
1542.3396-0.089549
1552.1214-0.44373
1562.06160.29234
1571.93690.17111
1582.14950.2384
1591.90870.24062
1602.73920.010696
1612.67090.040306
1622.52180.23985
1631.74340.45189
1641.88370.28201
1651.6440.70893
1662.18690.59606
1673.028-0.40917
1682.2960.22668
1692.4550.066758
1701.90970.055502
1711.82050.043101
1721.85790.11379
173-0.584320.56446
1740.118381.1354
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8001.92272.7501

PCA explained variance — how many dimensions?

Each bar reports the share of spectral variance explained by a component. Concentration in the first components indicates correlated wavelengths. This is not variance explained in the prediction target. This projection may use a different observation sample from the score plot.

PCA explained variance0%25%50%75%100%PC1: 79.7% (cumulative 79.7%)1PC2: 15.2% (cumulative 94.9%)2PC3: 4.0% (cumulative 99.0%)3PC4: 0.5% (cumulative 99.5%)4PC5: 0.2% (cumulative 99.7%)5PC6: 0.1% (cumulative 99.8%)6PC7: 0.1% (cumulative 99.9%)7PC8: 0.0% (cumulative 99.9%)8PC9: 0.0% (cumulative 99.9%)9PC10: 0.0% (cumulative 100.0%)10cumulative explained variancePC variancecumulativeprincipal component · cumulative (dashed)
View explained variance values
Variance per displayed principal component
ComponentSpectral variance
PC179.74%
PC215.21%
PC34.03%
PC40.55%
PC50.17%
PC60.09%
PC70.09%
PC80.04%
PC90.03%
PC100.02%
X-Y spectral correlation 18

Pearson r measures the linear association between each spectral channel (X) and a numeric target (Y), from −1 to +1. The table reports peak and average absolute correlation and the share of channels with |r| ≥ 0.5. These are in-dataset associations, not validation scores or proof of causation.

Spectral association with TOC

Read Pearson correlation (−1 to +1) along the spectral axis (nm); the solid curve shows |r| and the dashed curve shows signed r.

X_xds_standardized · TOC spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation01,0002,0003,000|r|signed raxis · Pearson correlation scale

Spectral association with TOM

Read Pearson correlation (−1 to +1) along the spectral axis (nm); the solid curve shows |r| and the dashed curve shows signed r.

X_xds_standardized · TOM spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation01,0002,0003,000|r|signed raxis · Pearson correlation scale

Spectral association with ASH

Read Pearson correlation (−1 to +1) along the spectral axis (nm); the solid curve shows |r| and the dashed curve shows signed r.

X_xds_standardized · ASH spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation01,0002,0003,000|r|signed raxis · Pearson correlation scale
Targetmax |r|axis @ maxmean |r||r| ≥ .5
TOC0.5941,1160.42851.7%
TOM0.6071,3140.44652.5%
ASH0.5991,1260.36244.3%
SOL0.492,4980.2040.0%
HEM0.5761,0940.37332.1%
CEL0.6251,3140.47454.1%
LIC0.3075960.1360.0%
TN0.521,1160.33419.6%
totP0.4294020.2210.0%
totK0.1434000.02990.0%
totCa0.6494040.30211.3%
totMg0.3782,1240.2230.0%
Cd0.4681,8520.4070.0%
Cr0.8741,0720.74593.8%
Cu0.5969000.4550.0%
Ni0.8241,0280.66885.0%
Pb0.5569480.41533.4%
Zn0.6439860.54673.0%

Metric interpretation reference

Definitions, formulas and possible explanations for the reported values. A possible cause requires verification against acquisition records. Reading guide

Metric catalog 30

Look up each measured quantity, its calculation and the heuristic alert mapping before interpreting a high or low value.

FamilyMetricWhat it measuresHigher values suggestLower values suggestPossible causesCalculation / score
Data integrityInvalid/masked cell count (owner report)Null or masked cells in the evaluated populationTo investigateComplete according to the declared maskMissing data or validity mask; not evidence of an acquisition causeNative Preflight count of cells unavailable under x/null and valid_mask; denominator stays in native_metric.denominators.n_cells. Not legacy NaN ratio.
Data integrityNaN ratioMissing dataCorrupted spectrumFull spectrumAcquisition/export errorcount(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Data integrityInf countInfinite valuesCorruptionNormalInvalid calculationscount(isinf(X))alert = min(1, inf_count / 1)
Data integrityZero ratioNull columns or cellsTruncated spectrumNormalExport, saturationcount(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Overall amplitudeMean reflectanceAverage levelToo light / background visibleToo darkBackground, geometrymean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Overall amplitudeArea under curveOverall intensityIllumination differenceNormalProbe distancetrapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Overall amplitudePeak-to-peak (PTP)Dynamic rangeStrong variabilityFlat spectrumSaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Overall amplitudeVarianceSpectral variabilityNormal or heterogeneousFlat spectrumBad contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
NoiseRoughness sigma proxyHigh frequency noiseNoisyStableLamp, detectormedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
NoiseRoughness S/N proxySignal qualityGood signalBad signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
NoiseBandwise roughness S/N proxyLocalized noiseReliable zoneProblem areaDetectormin(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Local artifactsSpike countNarrow peaksArtifactsClean spectrumCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Local artifactsSpike ratePeak densitySuspicious spectrumNormalInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Local artifactsJump countDiscontinuitiesDetector connectionContinuousSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Local artifactsJump rateJump frequencySpectral problemNormalCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Local artifactsClip fractionSaturationClippingNormalDetector saturatedfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Spectral formBaseline slopeOverall slopeDriftStableIlluminationlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Spectral formCurvature RMSCurvatureUnusual shapeSmoothBackground, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Spectral formD1 RMSLocal variabilityStructured spectrumFlatBiology or artifactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Multivariate outliersPCA Q (SPE)Not explained by PCAAtypical spectrumCompliantArtifact, mixturep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Multivariate outliersHotelling T²Extreme in PCAExtreme but consistentCentralNatural variabilityp95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Multivariate outliersMahalanobis HDistance to cloudGlobal outlierNormal populationDifferent domainp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparison to average spectrumRMS to mean spectrumAverage distanceDifferent spectrumTypicalDomain shiftp95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparison to average spectrumSpectral Angle Mapper (SAM)Difference in shapeDifferent shapeSimilarBackground, geometryp95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RepeatabilityRMS intra-IDReproducibilityPoor repeatabilityStablePositioningmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RepeatabilitySAM intra-IDShape variationUnstableStableAcquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RepeatabilityCV intra-IDInternal variabilityBad controlStableOperatormedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure of the datasetPCA score densityClustersSubpopulationsHomogeneousDifferent batches1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure of the datasetkNN distance ratio (not LOF)Local anomalyIsolated spectrumNormal populationRare casesp95(mean kNN distance / median population mean kNN distance) in PCA-score space; not LOFalert = min(1, max(0, kNN_distance_ratio_p95 - 1) / 2)
Structure of the datasetIsolation Forest scoreGlobal anomalyAtypical spectrumNormalVarious causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent
Technology-specific extensions

Find measurement-specific checks and acquisition context that can help interpret a source's spectral patterns.

TechnologyAdaptations / metricsTarget anomaliesPractical guidance
UV-Vis 300-1000 nmBaseline, overall slope, drift at edges 300-350 and 900-1000; metrics by zonesStray light, bad white, saturation, weak signal at the endsThe edges are often unstable; also calculate edge/middle scores.
UV-Vis 300-1000 nmSaturation / clipping near max absorbance or max reflectanceSignal clippedVery important if strong absorption.
UV-Vis 300-1000 nmRed-edge, peak position, band ratios for vegetationBiological shift or optical artifactHelps distinguish real change from acquisition problem.
UV-Vis 300-1000 nmSmoothness / roughness indexHigh frequency noiseOften more informative than the SNR alone.
MIR / ATR-FTIRATR contact quality index: overall intensity, total area, depth of key bandsPoor crystal-sample contactCrucial: many anomalies come from the ATR contact.
MIR / ATR-FTIRCO2 / H2O atmospheric bandsBad atmospheric correctionFrequent spurious peaks.
MIR / ATR-FTIRBaseline curvature / rubber-band residualDiffusion, contact, baseline driftVery useful before PCA.
MIR / ATR-FTIRPeak position shiftBad spectral alignment/calibrationImportant in FTIR because small shifts count.
MIR / ATR-FTIRBand area ratios on known bandsChemically incoherent spectrumTo be adapted by matrix: polysaccharides, proteins, lipids, etc.
HS-MSTotal Ion Current (TIC), Base Peak Intensity (BPI)Weak injection, unstable ionizationMS equivalent of the global spectral level.
HS-MSNumber of peaks detectedPoor or too noisy spectrumToo little = bad signal; too much = noise/contamination.
HS-MSMass accuracy / m/z driftMass calibration problemFundamental in HRMS.
HS-MSRetention time drift if LC/GC-MSChromatographic driftMonitor on standards/QC pools.
HS-MSBlank contamination scoreContaminants / carry-overCompare samples vs blanks.
HS-MSInternal standard CVInstrumental variabilityVery robust if standards available.
HS-MSMissingness by featureDetection instabilityCrucial for filtering variables.
With repetitionsIntra-sample RMSOverall repeatabilityApplicable to all technologies.
With repetitionsSAM / intra-sample correlationShape repeatabilityVery useful for spectra.
With repetitionsIntra-sample CV per band / featureLocal repeatabilityDetects unstable areas.
With repetitionsICC or variance componentsShare of sample vs. technical varianceVery useful if several repetitions per sample.
With repetitionsDistance to intra-ID centroidAberrant repetitionAllows you to flag the bad repetition rather than the entire sample.
Bug-hunting / supervised audits

Review suggested follow-up methods for suspected data issues and whether their results are available in this explorer.

Potential issue familySuggested methodsWhat it detectsExplorer availability
Global spectral shiftInter-dataset average spectrum correlation, DTW, cross-correlation, comparison of peak positionsWavelength shift, misalignment, different interpolationPartially calculated: cross-correlation lag and dispersion of peak positions vs. average spectrum.
Baseline / offset / gainRegression each spectrum vs average spectrum: x = a + b ref + residual; followed by a, b, residual RMSAdditive offset, multiplicative effect, baseline driftCalculated in reference.affine_*.
Row mixing / X-M-Y mismatchIndex check, row hash, ID duplication, intra-ID spectral distance, inconsistent labelsMixed up rows, misaligned metadata, Y assigned to wrong spectrumPartially covered by intra-ID repeatability; checks index/hash to add to the canonical pipeline.
Information leak / poorly split repetitionsGroupKFold by sample_id vs StratifiedKFold random; auditing partitions by sample_idArtificially good performance due to repetitionsRequires splits and benchmark model; not calculated by the descriptive card.
Label bugsClose samples in X but different Y, confident learning, systematic FP/FN errorsInverted Y, input errors, ambiguous classesRequires Y and/or model; recommended for supervised explorer.
Hidden subdomainsPCA/UMAP/t-SNE + unsupervised clustering + association with dataset/Y/date/operatorBatches, campaigns, probes, backgrounds not providedPartially calculated by PCA and distance ratio kNN (not LOF); UMAP/t-SNE outside the static card.
Unknown localized artifactsWavelength x dataset map: mean difference, variance difference, KS per wavelengthUnanticipated anomalous spectral regionsTo be calculated at the bank level when several datasets share a spectral axis.
Instrumental breaksDiscontinuities in derivatives, changepoint detectionSplice, detector connection, local jump not providedCalculated by jump/spike rates; more advanced changepoint to add.
Spectral mixing/contaminationNMF / unmixing / reconstruction by convex hullExternal component: background, plastic, soilNot calculated automatically; requires component assumptions or large library.
Unstable but predictive featuresModel importance vs. QC instability by variableModel that learns an artifact rather than a biological signalRequires supervised model; recommended for benchmark reporting.

Curated scientific variables

Scientific roles derived from the source contract. M denotes experimental metadata; Y denotes informative response variables. Reading guide

31 experimental metadata fields (M) · 20 informative targets (Y).

VariableRoleDistinct valuesCompleteRange / balance
standardized_on_xdsM2100%IR 1
collection_dateM108100%IR 46
townM6100%IR 728
townshipM3591.5%IR 99
animal_development_stagesM642.8%IR 105
animal_age_daysM4414.8%IR 9
animal_sexM354.2%IR 13.71
adult_typeM626.6%IR 25.25
exploitation_typeM288.2%IR 4.158
feed_ricebranM280.7%IR 2.405
feed_blackeyedpeasM280.7%IR 3.203
feed_industrializedfeedstuffM280.7%IR 1.211
feed_homemadefeedstuffM280.7%IR 9.088
feed_weedsM280.7%IR 3.16
feed_maizestalkM280.7%IR 806
feed_fodderM280.7%IR 30.04
feed_kitchenwasteM280.7%IR 66.25
feed_cottonresiduesM280.7%IR 49.44
feed_peanutsfoliageM280.7%IR 41.47
feed_sweetpotato_pumpkin_cassavaM280.7%IR 41.47
or_natureM19100%IR 395
or_nameM375.3%IR 4.099
litter_no_litterM275%IR 1.622
littter_rice_strawM221.8%IR 8.909
litter_grassM221.8%IR 53.5
litter_woodchipM221.8%IR 8.909
litter_fodderM221.8%IR 23.22
sample_typeM7100%IR 122.6
composted_non_conpostedM2100%IR 8.434
or_age_daysM134.4%IR 7
or_stageM32.4%IR 3.667
tocY17817.9%2 – 47.25
tomY17817.9%4.2 – 96.9
ashY868.6%2.747 – 98.5
solY868.6%0.1404 – 71.24
hemY858.6%0.0001 – 31.17
celY858.6%0.0001 – 45.38
licY868.6%0.0823 – 39.43
tnY17017.9%0.0001 – 99.2
totpY878.7%0.242 – 158.2
totkY878.7%0.647 – 151.6
totcaY656.5%0.197 – 137.7
totmgY666.6%0.682 – 23.12
cdY363.9%0.0001 – 3.28
crY373.8%4.83 – 293.5
cuY363.8%5.51 – 540.5
niY333.8%3.56 – 64.13
pbY333.8%0.0001 – 3,982
znY383.8%39.38 – 2221
or_categoryY4100%IR 43.15
or_originY18100%IR 340

Identifiers and sampling groups remain available separately. Statistics count exported observations.

Variables

Declared prediction targets and recorded metadata, with distributions and missing-value counts. Identifiers are not automatically prediction targets. Reading guide

Targets 20

TOC

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

TOC distribution020402 – 3.885: 63.885 – 5.771: 125.771 – 7.656: 67.656 – 9.542: 69.542 – 11.43: 1611.43 – 13.31: 2413.31 – 15.2: 2015.2 – 17.08: 817.08 – 18.97: 1018.97 – 20.85: 2020.85 – 22.74: 2222.74 – 24.62: 3424.62 – 26.51: 3026.51 – 28.4: 3028.4 – 30.28: 2230.28 – 32.17: 1632.17 – 34.05: 2434.05 – 35.94: 2035.94 – 37.82: 1037.82 – 39.71: 639.71 – 41.59: 441.59 – 43.48: 243.48 – 45.36: 245.36 – 47.25: 80204060
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,642
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
23.62 ± 9.99
Median
?50th percentile: half the non-missing values lie at or below it.
24.37
Range
?Smallest and largest recorded non-missing values.
2 – 47.25
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.423
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-0.077 / -0.5
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

TOM

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

TOM distribution020404.2 – 8.062: 48.062 – 11.92: 1211.92 – 15.79: 1015.79 – 19.65: 1019.65 – 23.51: 1623.51 – 27.38: 2227.38 – 31.24: 2231.24 – 35.1: 835.1 – 38.96: 1038.96 – 42.83: 1642.83 – 46.69: 3446.69 – 50.55: 3250.55 – 54.41: 3454.41 – 58.28: 2258.28 – 62.14: 3062.14 – 66: 1866 – 69.86: 2069.86 – 73.73: 1673.73 – 77.59: 877.59 – 81.45: 281.45 – 85.31: 285.31 – 89.18: 089.18 – 93.04: 493.04 – 96.9: 60255075100
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,642
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
46.7 ± 19.6
Median
?50th percentile: half the non-missing values lie at or below it.
48
Range
?Smallest and largest recorded non-missing values.
4.2 – 96.9
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.421
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
-0.02 / -0.37
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
yes

ASH

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

ASH distribution010202.747 – 6.737: 46.737 – 10.73: 010.73 – 14.72: 014.72 – 18.71: 418.71 – 22.69: 022.69 – 26.68: 626.68 – 30.67: 230.67 – 34.66: 1234.66 – 38.65: 1038.65 – 42.64: 1242.64 – 46.63: 1446.63 – 50.62: 1850.62 – 54.61: 1054.61 – 58.6: 658.6 – 62.59: 1062.59 – 66.58: 466.58 – 70.57: 870.57 – 74.56: 1474.56 – 78.55: 678.55 – 82.54: 682.54 – 86.53: 886.53 – 90.52: 890.52 – 94.51: 494.51 – 98.5: 60255075100
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,828
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
55.81 ± 22
Median
?50th percentile: half the non-missing values lie at or below it.
52.2
Range
?Smallest and largest recorded non-missing values.
2.747 – 98.5
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.395
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.0016 / -0.65
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

SOL

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

SOL distribution020400.1404 – 3.103: 183.103 – 6.065: 106.065 – 9.028: 109.028 – 11.99: 1011.99 – 14.95: 1614.95 – 17.92: 3217.92 – 20.88: 2220.88 – 23.84: 2223.84 – 26.8: 1826.8 – 29.77: 429.77 – 32.73: 032.73 – 35.69: 635.69 – 38.65: 038.65 – 41.62: 041.62 – 44.58: 044.58 – 47.54: 047.54 – 50.5: 250.5 – 53.47: 053.47 – 56.43: 056.43 – 59.39: 059.39 – 62.35: 062.35 – 65.32: 065.32 – 68.28: 068.28 – 71.24: 2020406080
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,828
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
17.19 ± 10.8
Median
?50th percentile: half the non-missing values lie at or below it.
17.31
Range
?Smallest and largest recorded non-missing values.
0.1404 – 71.24
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.627
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.6 / 6.6
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

HEM

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

HEM distribution01020300.0001 – 1.299: 221.299 – 2.598: 142.598 – 3.897: 143.897 – 5.196: 145.196 – 6.495: 106.495 – 7.794: 167.794 – 9.093: 69.093 – 10.39: 210.39 – 11.69: 611.69 – 12.99: 1212.99 – 14.29: 814.29 – 15.59: 1015.59 – 16.89: 1216.89 – 18.19: 818.19 – 19.48: 219.48 – 20.78: 620.78 – 22.08: 222.08 – 23.38: 423.38 – 24.68: 024.68 – 25.98: 025.98 – 27.28: 027.28 – 28.58: 028.58 – 29.88: 029.88 – 31.17: 4010203040
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,828
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
9.339 ± 7.2
Median
?50th percentile: half the non-missing values lie at or below it.
7.663
Range
?Smallest and largest recorded non-missing values.
0.0001 – 31.17
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.771
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.68 / -0.025
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

CEL

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

CEL distribution020400.0001 – 1.891: 341.891 – 3.782: 263.782 – 5.673: 105.673 – 7.564: 67.564 – 9.455: 129.455 – 11.35: 811.35 – 13.24: 1413.24 – 15.13: 2215.13 – 17.02: 817.02 – 18.91: 1218.91 – 20.8: 620.8 – 22.69: 422.69 – 24.58: 424.58 – 26.47: 026.47 – 28.37: 428.37 – 30.26: 030.26 – 32.15: 032.15 – 34.04: 034.04 – 35.93: 035.93 – 37.82: 037.82 – 39.71: 039.71 – 41.6: 041.6 – 43.49: 043.49 – 45.38: 20204060
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,828
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
9.862 ± 8.32
Median
?50th percentile: half the non-missing values lie at or below it.
8.762
Range
?Smallest and largest recorded non-missing values.
0.0001 – 45.38
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.844
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.1 / 2.3
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

LIC

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

LIC distribution020400.0823 – 1.722: 241.722 – 3.361: 223.361 – 5.001: 365.001 – 6.64: 146.64 – 8.28: 68.28 – 9.919: 249.919 – 11.56: 811.56 – 13.2: 1613.2 – 14.84: 614.84 – 16.48: 016.48 – 18.12: 218.12 – 19.76: 619.76 – 21.4: 021.4 – 23.03: 023.03 – 24.67: 024.67 – 26.31: 026.31 – 27.95: 227.95 – 29.59: 029.59 – 31.23: 231.23 – 32.87: 032.87 – 34.51: 234.51 – 36.15: 036.15 – 37.79: 037.79 – 39.43: 2010203040
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,828
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
7.834 ± 7.24
Median
?50th percentile: half the non-missing values lie at or below it.
5.265
Range
?Smallest and largest recorded non-missing values.
0.0823 – 39.43
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.924
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
2.1 / 5.4
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

TN

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

TN distribution02550750.0001 – 4.133: 204.133 – 8.267: 468.267 – 12.4: 4212.4 – 16.53: 2216.53 – 20.67: 3220.67 – 24.8: 4224.8 – 28.93: 7028.93 – 33.07: 2633.07 – 37.2: 1637.2 – 41.33: 1841.33 – 45.47: 1445.47 – 49.6: 249.6 – 53.73: 253.73 – 57.87: 057.87 – 62: 062 – 66.13: 066.13 – 70.27: 070.27 – 74.4: 474.4 – 78.53: 078.53 – 82.67: 082.67 – 86.8: 086.8 – 90.93: 090.93 – 95.07: 095.07 – 99.2: 20255075100
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,642
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
21.98 ± 14
Median
?50th percentile: half the non-missing values lie at or below it.
23.1
Range
?Smallest and largest recorded non-missing values.
0.0001 – 99.2
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.635
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.4 / 5.3
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

totP

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

totP distribution0501000.242 – 6.825: 986.825 – 13.41: 2813.41 – 19.99: 619.99 – 26.57: 426.57 – 33.15: 2033.15 – 39.74: 1439.74 – 46.32: 246.32 – 52.9: 052.9 – 59.49: 059.49 – 66.07: 066.07 – 72.65: 072.65 – 79.23: 079.23 – 85.82: 085.82 – 92.4: 092.4 – 98.98: 098.98 – 105.6: 0105.6 – 112.1: 0112.1 – 118.7: 0118.7 – 125.3: 0125.3 – 131.9: 0131.9 – 138.5: 0138.5 – 145.1: 0145.1 – 151.6: 0151.6 – 158.2: 2050100150200
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,826
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
13.23 ± 19.7
Median
?50th percentile: half the non-missing values lie at or below it.
6
Range
?Smallest and largest recorded non-missing values.
0.242 – 158.2
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
1.49
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
4.8 / 32
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

totK

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

totK distribution02040600.647 – 6.937: 386.937 – 13.23: 5613.23 – 19.52: 5019.52 – 25.81: 2225.81 – 32.1: 032.1 – 38.38: 638.38 – 44.67: 044.67 – 50.96: 050.96 – 57.25: 057.25 – 63.54: 063.54 – 69.83: 069.83 – 76.12: 076.12 – 82.41: 082.41 – 88.7: 088.7 – 94.99: 094.99 – 101.3: 0101.3 – 107.6: 0107.6 – 113.9: 0113.9 – 120.2: 0120.2 – 126.4: 0126.4 – 132.7: 0132.7 – 139: 0139 – 145.3: 0145.3 – 151.6: 2050100150200
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,826
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
14.28 ± 16.5
Median
?50th percentile: half the non-missing values lie at or below it.
12.2
Range
?Smallest and largest recorded non-missing values.
0.647 – 151.6
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
1.16
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
6.7 / 54
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

totCa

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

totCa distribution01020300.197 – 5.925: 165.925 – 11.65: 2811.65 – 17.38: 617.38 – 23.11: 1023.11 – 28.84: 1228.84 – 34.56: 034.56 – 40.29: 240.29 – 46.02: 646.02 – 51.75: 851.75 – 57.47: 257.47 – 63.2: 263.2 – 68.93: 068.93 – 74.66: 674.66 – 80.38: 280.38 – 86.11: 086.11 – 91.84: 491.84 – 97.57: 897.57 – 103.3: 4103.3 – 109: 6109 – 114.8: 4114.8 – 120.5: 0120.5 – 126.2: 2126.2 – 131.9: 0131.9 – 137.7: 2050100150
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,870
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
42.98 ± 39.3
Median
?50th percentile: half the non-missing values lie at or below it.
25.34
Range
?Smallest and largest recorded non-missing values.
0.197 – 137.7
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.915
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.75 / -0.86
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

totMg

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

totMg distribution01020300.682 – 1.617: 61.617 – 2.551: 02.551 – 3.486: 43.486 – 4.421: 184.421 – 5.356: 225.356 – 6.29: 86.29 – 7.225: 147.225 – 8.16: 188.16 – 9.095: 149.095 – 10.03: 210.03 – 10.96: 210.96 – 11.9: 011.9 – 12.83: 612.83 – 13.77: 613.77 – 14.7: 014.7 – 15.64: 415.64 – 16.57: 016.57 – 17.51: 417.51 – 18.44: 018.44 – 19.38: 019.38 – 20.31: 220.31 – 21.25: 021.25 – 22.18: 022.18 – 23.12: 20102030
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,868
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
7.656 ± 4.33
Median
?50th percentile: half the non-missing values lie at or below it.
6.931
Range
?Smallest and largest recorded non-missing values.
0.682 – 23.12
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.566
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.3 / 2
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Cd

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Cd distribution05100.0001 – 0.1368: 40.1368 – 0.2734: 100.2734 – 0.4101: 80.4101 – 0.5467: 60.5467 – 0.6834: 40.6834 – 0.8201: 60.8201 – 0.9567: 100.9567 – 1.093: 61.093 – 1.23: 21.23 – 1.367: 61.367 – 1.503: 21.503 – 1.64: 41.64 – 1.777: 21.777 – 1.913: 01.913 – 2.05: 02.05 – 2.187: 02.187 – 2.323: 02.323 – 2.46: 02.46 – 2.597: 22.597 – 2.733: 02.733 – 2.87: 02.87 – 3.007: 43.007 – 3.143: 03.143 – 3.28: 201234
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,922
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
0.968 ± 0.801
Median
?50th percentile: half the non-missing values lie at or below it.
0.83
Range
?Smallest and largest recorded non-missing values.
0.0001 – 3.28
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.828
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.4 / 1.5
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Cr

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Cr distribution010204.83 – 16.86: 2016.86 – 28.88: 1428.88 – 40.91: 440.91 – 52.94: 252.94 – 64.97: 064.97 – 76.99: 076.99 – 89.02: 489.02 – 101: 2101 – 113.1: 0113.1 – 125.1: 4125.1 – 137.1: 6137.1 – 149.2: 6149.2 – 161.2: 0161.2 – 173.2: 2173.2 – 185.2: 2185.2 – 197.3: 2197.3 – 209.3: 2209.3 – 221.3: 2221.3 – 233.3: 0233.3 – 245.4: 0245.4 – 257.4: 2257.4 – 269.4: 0269.4 – 281.5: 0281.5 – 293.5: 20100200300
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,924
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
84.18 ± 79.8
Median
?50th percentile: half the non-missing values lie at or below it.
39.05
Range
?Smallest and largest recorded non-missing values.
4.83 – 293.5
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.948
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.82 / -0.36
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Cu

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Cu distribution020405.51 – 27.8: 1827.8 – 50.09: 3250.09 – 72.38: 272.38 – 94.67: 294.67 – 117: 2117 – 139.3: 2139.3 – 161.5: 0161.5 – 183.8: 2183.8 – 206.1: 2206.1 – 228.4: 4228.4 – 250.7: 0250.7 – 273: 0273 – 295.3: 2295.3 – 317.6: 0317.6 – 339.9: 0339.9 – 362.2: 0362.2 – 384.5: 0384.5 – 406.7: 0406.7 – 429: 0429 – 451.3: 0451.3 – 473.6: 4473.6 – 495.9: 0495.9 – 518.2: 2518.2 – 540.5: 20200400600
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,924
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
109.9 ± 149
Median
?50th percentile: half the non-missing values lie at or below it.
40.69
Range
?Smallest and largest recorded non-missing values.
5.51 – 540.5
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
1.35
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.9 / 2.5
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Ni

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Ni distribution010203.56 – 6.084: 86.084 – 8.607: 208.607 – 11.13: 411.13 – 13.65: 213.65 – 16.18: 416.18 – 18.7: 218.7 – 21.23: 021.23 – 23.75: 423.75 – 26.27: 226.27 – 28.8: 228.8 – 31.32: 431.32 – 33.84: 033.84 – 36.37: 036.37 – 38.89: 638.89 – 41.42: 441.42 – 43.94: 443.94 – 46.46: 046.46 – 48.99: 448.99 – 51.51: 051.51 – 54.03: 054.03 – 56.56: 056.56 – 59.08: 259.08 – 61.61: 261.61 – 64.13: 2020406080
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,924
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
23.29 ± 18
Median
?50th percentile: half the non-missing values lie at or below it.
17.1
Range
?Smallest and largest recorded non-missing values.
3.56 – 64.13
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
0.773
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
0.71 / -0.69
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Pb

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Pb distribution02040600.0001 – 165.9: 52165.9 – 331.8: 0331.8 – 497.8: 4497.8 – 663.7: 0663.7 – 829.6: 2829.6 – 995.5: 6995.5 – 1161: 41161 – 1327: 01327 – 1493: 01493 – 1659: 41659 – 1825: 01825 – 1991: 21991 – 2157: 02157 – 2323: 02323 – 2489: 02489 – 2655: 02655 – 2821: 02821 – 2987: 02987 – 3152: 03152 – 3318: 03318 – 3484: 03484 – 3650: 03650 – 3816: 03816 – 3,982: 201,0002,0003,0004,000
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,924
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
429.2 ± 801
Median
?50th percentile: half the non-missing values lie at or below it.
12.1
Range
?Smallest and largest recorded non-missing values.
0.0001 – 3,982
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
1.87
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
2.7 / 8.8
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

Zn

target · numeric

Read the recorded value distribution and summary statistics; no measurement unit is recorded.

Zn distribution0102039.38 – 130.3: 8130.3 – 221.2: 14221.2 – 312.1: 16312.1 – 402.9: 6402.9 – 493.8: 10493.8 – 584.7: 0584.7 – 675.6: 0675.6 – 766.5: 2766.5 – 857.4: 4857.4 – 948.3: 2948.3 – 1039: 21039 – 1130: 01130 – 1221: 21221 – 1312: 01312 – 1403: 01403 – 1494: 01494 – 1585: 21585 – 1675: 41675 – 1766: 01766 – 1857: 01857 – 1948: 01948 – 2039: 02039 – 2130: 22130 – 2221: 201,0002,0003,000
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,924
Mean ± SD
?Arithmetic mean and standard deviation in the variable's stated unit.
559.1 ± 572
Median
?50th percentile: half the non-missing values lie at or below it.
320
Range
?Smallest and largest recorded non-missing values.
39.38 – 2221
CV
?Coefficient of variation: standard deviation divided by absolute mean; unstable when the mean is near zero.
1.02
Skew / kurtosis
?Distribution asymmetry and excess kurtosis. A Gaussian distribution has skewness 0 and excess kurtosis 0.
1.6 / 1.5
Normal?
?Descriptive normality-test result. A non-normal target is not automatically a defective variable.
no

OR_category

target · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

OR_category classesAnimalAnimal: 1,7261,726UrbanUrban: 162162AgroindustrialAgroindustrial: 7272Animal&urbanAnimal&urban: 4040
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 0
Classes
?Number of distinct recorded category values.
4
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.38
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
4e+01
Top class
?Most frequent recorded category and its observation count.
Animal (1,726)

OR_origin

target · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

OR_origin classespoultrypoultry: 680680pigpig: 600600bovinebovine: 416416landfilllandfill: 142142slaughterhouseslaughterhouse: 5454bovine&residuesbovine&residues: 4040goatgoat: 2424pottingsoilpottingsoil: 1616tobaccotobacco: 66fish processingfish processing: 44+8 more+8 more: 1818
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 0
Classes
?Number of distinct recorded category values.
18
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.55
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
340
Top class
?Most frequent recorded category and its observation count.
poultry (680)

Metadata 31

standardized_on_xds

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

standardized_on_xds classesNONO: 1,0001,000YESYES: 1,0001,000
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 0
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
1
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
1
Top class
?Most frequent recorded category and its observation count.
NO (1,000)

Collection_date

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Collection_date classes2011-08-26 00:00:002011-08-26 00:00:00: 92922011-07-06 00:00:002011-07-06 00:00:00: 88882011-04-19 00:00:002011-04-19 00:00:00: 72722009-05-13 00:00:002009-05-13 00:00:00: 54542009-05-06 00:00:002009-05-06 00:00:00: 52522011-07-13 00:00:002011-07-13 00:00:00: 46462009-06-19 00:00:002009-06-19 00:00:00: 44442009-04-03 00:00:002009-04-03 00:00:00: 42422009-04-08 00:00:002009-04-08 00:00:00: 42422009-04-29 00:00:002009-04-29 00:00:00: 4242+10 more+10 more: 392392
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 0
Classes
?Number of distinct recorded category values.
108
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.92
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
46
Top class
?Most frequent recorded category and its observation count.
2011-08-26 00:00:00 (92)

Town

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Town classesMahajangaMahajanga: 1,4561,456AntananarivoAntananarivo: 496496ItasyItasy: 3434ManjakandrianaManjakandriana: 1010AmbatondrazakaAmbatondrazaka: 22AntsirabeAntsirabe: 22
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 0
Classes
?Number of distinct recorded category values.
6
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.38
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
728
Top class
?Most frequent recorded category and its observation count.
Mahajanga (1,456)

Township

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Township classesBelobakaBelobaka: 198198AmbondronaAmbondrona: 186186AmborovyAmborovy: 182182AntanimasajyAntanimasajy: 168168AmpitilovaAmpitilova: 164164Antanambao AmbalavatoAntanambao Ambalavato: 136136AndakanaAndakana: 112112Tsararano AmbonyTsararano Ambony: 9898AndralanitraAndralanitra: 9696AntanimalandyAntanimalandy: 7272+10 more+10 more: 334334
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 170
Classes
?Number of distinct recorded category values.
35
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.82
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
99
Top class
?Most frequent recorded category and its observation count.
Belobaka (198)

Animal_Development`_stages

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Animal_Development`_stages classesadultadult: 420420youngyoung: 318318pigletpiglet: 8080chickchick: 1818calfcalf: 1616chikchik: 44
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,144
Classes
?Number of distinct recorded category values.
6
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.62
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
105
Top class
?Most frequent recorded category and its observation count.
adult (420)

Animal_Age(days)

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Animal_Age(days) classes36.036.0: 1818371.0371.0: 1616392.0392.0: 1616385.0385.0: 1414448.0448.0: 1010406.0406.0: 1010175.0175.0: 10105.05.0: 8812.012.0: 8819.019.0: 88+10 more+10 more: 8080
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,704
Classes
?Number of distinct recorded category values.
44
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.95
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
9
Top class
?Most frequent recorded category and its observation count.
36.0 (18)

Animal_Sex

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Animal_Sex classesfemalefemale: 658658male&femalemale&female: 378378malemale: 4848
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 916
Classes
?Number of distinct recorded category values.
3
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.74
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
1e+01
Top class
?Most frequent recorded category and its observation count.
female (658)

Adult_Type

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Adult_Type classeslaying henlaying hen: 202202lactatinglactating: 156156broilerbroiler: 8686castratedcastrated: 4848dry cowdry cow: 3232litterlitter: 88
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,468
Classes
?Number of distinct recorded category values.
6
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.82
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
3e+01
Top class
?Most frequent recorded category and its observation count.
laying hen (202)

Exploitation_Type

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Exploitation_Type classesfarmfarm: 1,4221,422industrialindustrial: 342342
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 236
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.71
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
4
Top class
?Most frequent recorded category and its observation count.
farm (1,422)

Feed_RiceBran

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Feed_RiceBran classesnono: 1,1401,140yesyes: 474474
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 386
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.87
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
2
Top class
?Most frequent recorded category and its observation count.
no (1,140)

Feed_BlackEyedPeas

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Feed_BlackEyedPeas classesnono: 1,2301,230yesyes: 384384
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 386
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.79
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
3
Top class
?Most frequent recorded category and its observation count.
no (1,230)

Feed_IndustrializedFeedstuff

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Feed_IndustrializedFeedstuff classesnono: 884884yesyes: 730730
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 386
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.99
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
1
Top class
?Most frequent recorded category and its observation count.
no (884)

Feed_HomemadeFeedstuff

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Feed_HomemadeFeedstuff classesnono: 1,4541,454yesyes: 160160
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 386
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.47
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
9
Top class
?Most frequent recorded category and its observation count.
no (1,454)

Feed_Weeds

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Feed_Weeds classesnono: 1,2261,226yesyes: 388388
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 386
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.8
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
3
Top class
?Most frequent recorded category and its observation count.
no (1,226)

Feed_Maizestalk

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Feed_Maizestalk classesnono: 1,6121,612yesyes: 22
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 386
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.014
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
806
Top class
?Most frequent recorded category and its observation count.
no (1,612)

Feed_Fodder

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Feed_Fodder classesnono: 1,5621,562yesyes: 5252
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 386
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.21
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
3e+01
Top class
?Most frequent recorded category and its observation count.
no (1,562)

Feed_KitchenWaste

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Feed_KitchenWaste classesnono: 1,5901,590yesyes: 2424
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 386
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.11
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
7e+01
Top class
?Most frequent recorded category and its observation count.
no (1,590)

Feed_CottonResidues

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Feed_CottonResidues classesnono: 1,5821,582yesyes: 3232
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 386
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.14
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
5e+01
Top class
?Most frequent recorded category and its observation count.
no (1,582)

Feed_peanutsfoliage

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Feed_peanutsfoliage classesnono: 1,5761,576yesyes: 3838
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 386
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.16
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
4e+01
Top class
?Most frequent recorded category and its observation count.
no (1,576)

Feed_sweetpotato&pumpkin&cassava

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Feed_sweetpotato&pumpkin&cassava classesnono: 1,5761,576yesyes: 3838
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 386
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.16
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
4e+01
Top class
?Most frequent recorded category and its observation count.
no (1,576)

OR_Nature

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

OR_Nature classesmanuremanure: 790790liquid manureliquid manure: 746746droppingsdroppings: 182182fermentable fraction of house…fermentable fraction of household waste: 142142bovine&residuesbovine&residues: 4040bovine hornbovine horn: 2626dried blooddried blood: 2626potting soilpotting soil: 1616litterlitter: 88ashash: 66+9 more+9 more: 1818
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 0
Classes
?Number of distinct recorded category values.
19
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.5
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
395
Top class
?Most frequent recorded category and its observation count.
manure (790)

OR_Name

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

OR_Name classesslurryslurry: 746746manuremanure: 578578droppingsdroppings: 182182
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 494
Classes
?Number of distinct recorded category values.
3
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.88
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
4
Top class
?Most frequent recorded category and its observation count.
slurry (746)

Litter/No_litter

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Litter/No_litter classesnono: 928928yesyes: 572572
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 500
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.96
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
2
Top class
?Most frequent recorded category and its observation count.
no (928)

Littter_Rice_straw

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Littter_Rice_straw classesnono: 392392yesyes: 4444
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,564
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.47
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
9
Top class
?Most frequent recorded category and its observation count.
no (392)

Litter_Grass

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Litter_Grass classesnono: 428428yesyes: 88
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,564
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.13
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
5e+01
Top class
?Most frequent recorded category and its observation count.
no (428)

Litter_Woodchip

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Litter_Woodchip classesyesyes: 392392nono: 4444
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,564
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.47
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
9
Top class
?Most frequent recorded category and its observation count.
yes (392)

Litter_Fodder

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Litter_Fodder classesnono: 418418yesyes: 1818
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,564
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.25
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
2e+01
Top class
?Most frequent recorded category and its observation count.
no (418)

Sample_type

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Sample_type classesCompositeComposite: 1,7161,716abreuvoirabreuvoir: 7878dortoirdortoir: 7878couvoircouvoir: 7878partie bassepartie basse: 2020partie centralepartie centrale: 1616partie hautepartie haute: 1414
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 0
Classes
?Number of distinct recorded category values.
7
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.32
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
1e+02
Top class
?Most frequent recorded category and its observation count.
Composite (1,716)

Composted/Non_conposted

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

Composted/Non_conposted classesncnc: 1,7881,788cc: 212212
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 0
Classes
?Number of distinct recorded category values.
2
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.49
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
8
Top class
?Most frequent recorded category and its observation count.
nc (1,788)

OR_age(days)

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

OR_age(days) classes18251825: 141436503650: 14143030: 1212180180: 1212365365: 1212sup3650sup3650: 10101414: 2277: 2222: 2221902190: 22+3 more+3 more: 66
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,912
Classes
?Number of distinct recorded category values.
13
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.88
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
7
Top class
?Most frequent recorded category and its observation count.
1825 (14)

OR_Stage

metadata · categorical

Compare recorded category counts and class balance. Bar lengths show observations in each displayed category.

OR_Stage classesmaturemature: 2222earlyearly: 2020jeunejeune: 66
n / missing
?Recorded observation count and number of missing values for this variable.
2,000 / 1,952
Classes
?Number of distinct recorded category values.
3
Balance (entropy)
?Normalized Shannon entropy, from 0 for one dominant class to 1 for equal class frequencies.
0.89
Imbalance ratio
?Largest class count divided by smallest class count; 1 indicates equal counts.
4
Top class
?Most frequent recorded category and its observation count.
mature (22)
Constant metadata 21

Fields with one recorded value provide context for every observation; they have no distribution to compare.

  • composition_metadata_urlhttps://dataverse.cirad.fr/dataset.xhtml?persistentId=doi:10.18167/DVN1/YRMQ52
  • api_urlhttps://dataverse.cirad.fr/api/datasets/:persistentId/?persistentId=doi:10.18167/DVN1/SZSTBD
  • article_urlhttps://www.sciencedirect.com/science/article/pii/S2352340922005510
  • doi_dataset10.18167/DVN1/SZSTBD
  • doi_composition_metadata10.18167/DVN1/YRMQ52
  • doi_publication10.1016/j.dib.2022.108102
  • licenseCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International
  • license_idcc-by-nc-sa-4.0
  • license_urlhttps://creativecommons.org/licenses/by-nc-sa/4.0/
  • rights_statusexplicit_open_noncommercial_sharealike
  • usage_scopeinternal_benchmark_only_until_license_review
  • public_release_allowedFalse
  • spectroscopy_typeVIS-NIR
  • axis_unitnm
  • axis_min400
  • axis_max2,498
  • n_points_original1,050
  • instrument_nameASD
  • signal_typeabsorbance
  • decoderpandas.read_excel/openpyxl
  • notesCC BY-NC-SA source, many chemical targets are sparse. Variable dictionary sheets retained in source workbooks.

Alignment

How observations join to physical sample IDs. Multiple exported rows are not necessarily independent samples or technical repetitions. Reading guide

Alignment levelsample
Sample id availableyes
Samples1,000
Observations (total)2,000
Exported observations per unitmin 1 · mean 1 · max 1
Identity statusavailable
Documented technical repetitionsND
Source unitMultiplicity of the declared identifier; protocol not established

Splits

Partitions supplied by the original dataset. They are documented here and are not applied automatically when loading. Reading guide

No original partition summaries are recorded. Loading does not create a train/test split.

Load this dataset

Use the appropriate route below: an assembled dataset, original raw resources, or the documented access procedure. Reading guide

# Download original raw resources; no Dataverse token required
from nirs4all_datasets import retrieve
result = retrieve("cirad_madagascar_organic_resources_asd_visnir", prepare=False)
print(result["resources"])
# Raw resources may need dataset-specific assembly before analysis

Metadata downloads are available for public datasets only. The dataset bytes are never served here — fetch them from the origin / DOI above.

Read the documentation

Access and rights are separate

Start with provenance and governance at the top of a card. Provenance links to the original resources and publications. Governance records the license, redistribution conditions and content identity.

Open source access means an open original-source route is recorded. Restricted redistribution is the display label for the catalog's private tier: rights are restricted or not cleared for redistribution. It does not mean that the original source is necessarily private.

Raw reproducible identifies a retrieval route. Assembly incomplete means raw resources can be available while dataset-specific conversion is missing. The loading example uses retrieve(id, prepare=False) for these raw downloads; it returns resource metadata rather than X/Y arrays. A complete original-source recipe uses get(id). Two Harvard datasets require explicit guestbook answers.

Token required means no open automatic route is recorded. A Dataverse token works only once the canonical data have a configured hosted DOI. Manual access requires the original provider's documented steps. Availability labels describe the catalog routes, not a fresh live check of every URL.

Why is PCA on a dataset card?

Spectra have many correlated wavelengths. Principal component analysis (PCA) compresses their variation into a few orthogonal directions, called principal components (PCs). It is an exploratory summary of X, calculated without using the target Y.

The score plot places each plotted observation on PC1 and PC2. Nearby dots have similar projections; groups can reveal batches, instruments or natural subpopulations. Axis percentages show explained spectral variance. Large sources are sampled to at most 800 observations. Similar positions in two dimensions do not guarantee similar full spectra; an isolated dot does not prove an acquisition fault.

The scree plot reports spectral variance per component, helping you see whether variation is concentrated in a few dimensions. It is not explained target variance or predictive accuracy. The scree projection and score-plot projection may use different row samples; their percentages need not match.

Q / SPE measures residual variation outside the retained PCA subspace. Hotelling T² measures an observation's scaled distance within that subspace; Mahalanobis H summarizes PCA-space distance. The displayed p95/median ratios compare the distribution's upper tail with its center, without an absolute rejection threshold.

Historical profiles replace missing cells with column medians before PCA, center channels, and use bounded row/component counts. Loadings and distances depend on signal units and preprocessing. Compare compatible sources and inspect the original spectra before acting.

Scores, evidence and missing values

Read measured values first. An alert score maps selected metrics to a heuristic 0–1 scale. The display bands are weak below 0.42, medium from 0.42 to below 0.72, and strong from 0.72. They are not universally validated pass/fail thresholds.

The radar's center is 0 and its outer ring is 1: outward means a stronger anomaly or heterogeneity signal for that axis. Its table gives values rounded to two decimals and explains each axis. Missing axes are left blank; partial profiles have no filled polygon. The mean profile score averages only available axes and is not an overall quality grade.

Diagnostic bars rank possible explanations from heuristic signatures. A score of 0.78 does not mean a 78% causal probability. Evidence coverage is the proportion of required signals available, not confidence in the cause. A dash or “unavailable” means the quantity could not be computed; zero is an actual recorded value.

RMS (root mean square distance) and SAM (Spectral Angle Mapper, in radians) use this dataset's mean spectrum as their reference. They do not compare with an independently acquired white or dark reference. Repeated IDs require a documented protocol before within-ID spread can be called technical repeatability.

X–Y plots show Pearson correlation within this dataset. They identify linear associations; they are not cross-validated predictions and do not establish causal relationships. Targets, metadata, observation counts and physical sample counts have different meanings.

Open the full reading guide