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CIRAD Madagascar organic resources ASD non-standardized VIS-NIR

cirad · NIR

CIRAD Madagascar organic resources ASD non-standardized VIS-NIR. v2.0 standardized NIRS package: 1 spectral source(s), 20 declared target(s). Auto-generated from dataset_card.json (verify before publication).

nirv2cirad
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Private dataset. Full metadata and metrics are shown, but the bytes are not redistributed here — exporting the data requires a Dataverse token. The identity card carries no spectra, only descriptive statistics.
1,000
samples
1,050
wavelengths
1
sources
20
targets
52
metadata
NIR
family

Dataset property explorer

Mean profile risk0.55
Highest axisArtefacts locaux · 1.00
Diagnostics8
Sources profiled1
CIRAD Madagascar organic resources ASD non-standardized VIS-NIR property profile0.250.50.751integritynoiseartefactsbaselinePCA outliersreferencerepeatabilitystructureCIRAD Madagascar organic resources ASD non-standardized VIS-NIR profileintegrity: 0.00noise: 0.00artefacts: 1.00baseline: 0.58PCA outliers: 0.80reference: 1.00repeatability: 0.00structure: 1.00CIRAD Madagasca…0 center · 1 outer ring · outward = stronger anomaly / heterogeneity signal

Profile axes

Intégrité0.00
Artefacts locaux1.00
Bruit0.00
Outliers PCA0.80
Distance à la référence1.00
Répétabilité0.00
Baseline / forme0.58
Structure multi-régimes1.00
Diagnostic hypotheses00.250.50.751hypothesis scoreSplice / raccord détecteursSplice / raccord détecteurs: 0.850.85Signature VERA25-likeSignature VERA25-like: 0.690.69Erreur interpolation / réécha…Erreur interpolation / rééchantillonnage: 0.680.68Erreur calibration / référenc…Erreur calibration / référence blanche: 0.680.68Dataset multi-régimesDataset multi-régimes: 0.650.65Spectre hors domaine valideSpectre hors domaine valide: 0.650.65Fond différentFond différent: 0.620.62Différence de sonde / géométr…Différence de sonde / géométrie: 0.600.60
DiagnosticScoreForceSignauxInterprétation probable
Splice / raccord détecteursX0.85forteSpike rate 1.00, RMS/SAM référence 1.00, SNR non dégradé 1.00Rupture aux jonctions de détecteurs, calibration locale ou sonde différente.
Signature VERA25-likeX0.69moyenneSpike rate 1.00, RMS/SAM référence 1.00, PCA Q 0.80Combinaison possible changement de sonde + splice, amplifiée par géométrie, fond ou calibration.
Erreur interpolation / rééchantillonnageX0.68moyenneSpike rate 1.00, SNR normal/élevé 1.00, Noise RMS faible 1.00Artefacts numériques ou traitement spectral incorrect.
Erreur calibration / référence blancheX0.68moyenneRMS/SAM référence 1.00, artefacts locaux 1.00, PCA Q 0.80Décalage systématique entre campagnes, instruments ou référence blanche.
Dataset multi-régimesX0.65moyenneStructure PCA 1.00, RMS/SAM référence 1.00, PCA Q 0.80Mélange de campagnes, opérateurs, lots, setups ou sous-populations spectrales.
Spectre hors domaine valideX0.65moyenneRMS/SAM référence 1.00, Structure PCA 1.00, Mahalanobis / T2 0.77Variété, espèce, lot ou condition différente mais physiquement plausible.
Fond différentX0.62moyenneRMS/SAM référence 1.00, PCA Q 0.80, Mahalanobis / T2 0.77Effet systématique du support, blanc/noir, transflectance ou environnement de mesure.
Différence de sonde / géométrieX0.60moyenneRMS/SAM référence 1.00, PCA Q 0.80, Mahalanobis / T2 0.77Modification de l'illumination, collecte, angle ou distance sonde-échantillon.

Spectral sources

ASD non-standardized absorbance

X · NIR · ASD
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

Wavelengths1,050
Axis range400–2,498 nm
Mean spacing2 nm
Griduniform
Observations1,000

Signal & quality

Value range0.0408 – 1.51
Mean range0.21 – 1.04
Mean level0.3934
Area824.5
PTP0.8334
Noise RMS6.023e-05
SNR6.5e+03
SNR dB8e+01 dB
Dynamic range0.833
Smoothness0.001071
Saturated0.0%
X-outliers498

Integrity & artefacts

NaN ratio0.00%
Inf count0
Zero ratio0.00%
Spike count43,243
Spike rate4.13%
Jump count7,718
Jump rate0.74%
Clip fraction0.00%

Shape & reference

Baseline slope-0.24326
Curvature RMS0.0008837
D1 RMS0.0021995
RMS to mean0.095029
RMS p950.34203
SAM to mean0.15501
SAM p950.34336
Affine offset p950.46644
Affine gain p95 Δ0.52421
Affine residual p950.091259
Xcorr lag p950

Outliers & repeatability

PCA Q p95/median6.4
Hotelling T2 p95/median6.2
Mahalanobis H p95/median2.5
Repeat groups0

Dimensionality (PCA)

Effective rank1.9
PCs → 95% var2
PCs → 99% var3
Top-10 cum. var100.0%
Computed metric scores 29worst 1.00
FamilleMétrique calculéeValeurScoreNiveauInterprétation datasetCauses typiquesCalcul / scoring
Intégrité des donnéesNaN ratiointegrity.nan_ratio0%0.00faibleSpectre completErreur acquisition/exportcount(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Intégrité des donnéesInf countintegrity.inf_count00.00faibleNormalCalculs invalidescount(isinf(X))alert = min(1, inf_count / 1)
Intégrité des donnéesZero ratiointegrity.zero_ratio0%0.00faibleNormalExport, saturationcount(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Amplitude globaleMean reflectanceamplitude.mean_reflectance0.393360.58moyenValeur atypique: Trop clair / fond visible ou Trop sombreFond, géométriemean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Amplitude globaleArea under curveamplitude.area_under_curve824.460.58moyenValeur atypique: Différence d'éclairement ou NormalDistance sondetrapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Amplitude globalePeak-to-peak (PTP)amplitude.peak_to_peak0.833350.00faibleVariabilité forteSaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Amplitude globaleVarianceamplitude.variance0.0576980.00faibleNormal ou hétérogèneMauvais contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
BruitNoise RMSnoise.noise_rms6.023e-050.00faibleStableLampe, détecteurmedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
BruitSNRnoise.snr6530.90.00faibleBon signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
BruitBandwise SNRnoise.bandwise_snr_min272.630.00faibleZone fiableDétecteurmin(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Artefacts locauxSpike countartefacts.spike_count43,2431.00fortArtefactsCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Artefacts locauxSpike rateartefacts.spike_rate4.13%1.00fortSpectre suspectInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Artefacts locauxJump countartefacts.jump_count7,7180.74fortRaccord détecteurSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Artefacts locauxJump rateartefacts.jump_rate0.736%0.74fortProblème spectralCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Artefacts locauxClip fractionartefacts.clip_fraction0.00019%0.00faibleNormalDétecteur saturéfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Forme spectraleBaseline slopeshape.baseline_slope-0.243260.58moyenDériveÉclairementlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Forme spectraleCurvature RMSshape.curvature_rms0.00088370.11faibleLisseFond, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Forme spectraleD1 RMSshape.d1_rms0.00219950.05faiblePlatBiologie ou artefactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Outliers multivariésPCA Q (SPE)outliers.pca_q_ratio6.38690.80fortSpectre atypiqueArtefact, mélangep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Outliers multivariésHotelling T²outliers.hotelling_t2_ratio6.17810.77fortExtrême mais cohérentVariabilité naturellep95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Outliers multivariésMahalanobis Houtliers.mahalanobis_h_ratio2.48560.62moyenOutlier globalDomaine différentp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparaison à référenceRMS to mean spectrumreference.rms_to_mean_spectrum_p950.342031.00fortSpectre différentDomain shiftp95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparaison à référenceSpectral Angle Mapper (SAM)reference.sam_to_mean_spectrum_p950.343360.98fortForme différenteFond, géométriep95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RépétabilitéRMS intra-IDrepeatability.rms_intra_id—0.00faibleStablePositionnementmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RépétabilitéSAM intra-IDrepeatability.sam_intra_id—0.00faibleStableAcquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RépétabilitéCV intra-IDrepeatability.cv_intra_id—0.00faibleStableOpérateurmedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure du datasetPCA score densitystructure.pca_score_density2.4231.00fortSous-populationsLots différents1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure du datasetLocal Outlier Factor (LOF)structure.local_outlier_factor_p953.21391.00fortSpectre isoléCas raresp95 approximate LOF from PCA-score kNN distancesalert = min(1, max(0, LOF_p95 - 1) / 2)
Structure du datasetIsolation Forest scorestructure.isolation_forest_score_p950.589911.00fortSpectre atypiqueDiverses causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent
X 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
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)
X-Y spectral correlation 18
X · TOC spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation01,0002,0003,000|r|signed raxis · Pearson correlation scale
X · TOM spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation01,0002,0003,000|r|signed raxis · Pearson correlation scale
X · 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

Metric catalog 29
FamilleMétriqueCe qu’elle détecteForte valeur =Faible valeur =Causes typiquesCalcul / score
Intégrité des donnéesNaN ratioDonnées manquantesSpectre corrompuSpectre completErreur acquisition/exportcount(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Intégrité des donnéesInf countValeurs infiniesCorruptionNormalCalculs invalidescount(isinf(X))alert = min(1, inf_count / 1)
Intégrité des donnéesZero ratioColonnes ou cellules nullesSpectre tronquéNormalExport, saturationcount(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Amplitude globaleMean reflectanceNiveau moyenTrop clair / fond visibleTrop sombreFond, géométriemean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Amplitude globaleArea under curveIntensité globaleDifférence d'éclairementNormalDistance sondetrapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Amplitude globalePeak-to-peak (PTP)DynamiqueVariabilité forteSpectre platSaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Amplitude globaleVarianceVariabilité spectraleNormal ou hétérogèneSpectre platMauvais contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
BruitNoise RMSBruit haute fréquenceBruitéStableLampe, détecteurmedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
BruitSNRQualité signalBon signalMauvais signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
BruitBandwise SNRBruit localiséZone fiableZone problématiqueDétecteurmin(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Artefacts locauxSpike countPics étroitsArtefactsSpectre propreCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Artefacts locauxSpike rateDensité de picsSpectre suspectNormalInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Artefacts locauxJump countDiscontinuitésRaccord détecteurContinuSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Artefacts locauxJump rateFréquence de sautsProblème spectralNormalCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Artefacts locauxClip fractionSaturationClippingNormalDétecteur saturéfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Forme spectraleBaseline slopePente globaleDériveStableÉclairementlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Forme spectraleCurvature RMSCourbureForme inhabituelleLisseFond, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Forme spectraleD1 RMSVariabilité localeSpectre structuréPlatBiologie ou artefactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Outliers multivariésPCA Q (SPE)Non expliqué par PCASpectre atypiqueConformeArtefact, mélangep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Outliers multivariésHotelling T²Extrême dans PCAExtrême mais cohérentCentralVariabilité naturellep95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Outliers multivariésMahalanobis HDistance au nuageOutlier globalPopulation normaleDomaine différentp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparaison à référenceRMS to mean spectrumDistance moyenneSpectre différentTypiqueDomain shiftp95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparaison à référenceSpectral Angle Mapper (SAM)Différence de formeForme différenteSimilaireFond, géométriep95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RépétabilitéRMS intra-IDReproductibilitéMauvaise répétabilitéStablePositionnementmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RépétabilitéSAM intra-IDVariation de formeInstableStableAcquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RépétabilitéCV intra-IDVariabilité interneMauvais contrôleStableOpérateurmedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure du datasetPCA score densityClustersSous-populationsHomogèneLots différents1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure du datasetLocal Outlier Factor (LOF)Anomalie localeSpectre isoléPopulation normaleCas raresp95 approximate LOF from PCA-score kNN distancesalert = min(1, max(0, LOF_p95 - 1) / 2)
Structure du datasetIsolation Forest scoreAnomalie globaleSpectre atypiqueNormalDiverses causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent
Technology-specific extensions
TechnologieAdaptations / métriquesAnomalies cibléesCommentaire pratique
UV-Vis 300-1000 nmBaseline, pente globale, dérive aux bords 300-350 et 900-1000; métriques par zonesLumière parasite, mauvais blanc, saturation, faible signal aux extrémitésLes bords sont souvent instables; calculer aussi des scores edge/middle.
UV-Vis 300-1000 nmSaturation / clipping proche absorbance max ou réflectance maxSignal écrêtéTrès important si absorption forte.
UV-Vis 300-1000 nmRed-edge, position de maximum, ratios de bandes si végétalDécalage biologique ou artefact optiqueAide à distinguer changement réel et problème d'acquisition.
UV-Vis 300-1000 nmSmoothness / roughness indexBruit haute fréquenceSouvent plus informatif que le SNR seul.
MIR / ATR-FTIRATR contact quality index: intensité globale, aire totale, profondeur des bandes clésMauvais contact cristal-échantillonCrucial: beaucoup d'anomalies viennent du contact ATR.
MIR / ATR-FTIRCO2 / H2O atmospheric bandsMauvaise correction atmosphériquePics parasites fréquents.
MIR / ATR-FTIRBaseline curvature / rubber-band residualDiffusion, contact, dérive baselineTrès utile avant PCA.
MIR / ATR-FTIRPeak position shiftMauvais alignement spectral / calibrationImportant en FTIR car de petits shifts comptent.
MIR / ATR-FTIRBand area ratios sur bandes connuesSpectre chimiquement incohérentÀ adapter par matrice: polysaccharides, protéines, lipides, etc.
HS-MSTotal Ion Current (TIC), Base Peak Intensity (BPI)Injection faible, ionisation instableÉquivalent MS du niveau global spectral.
HS-MSNombre de pics détectésSpectre pauvre ou trop bruitéTrop peu = mauvais signal; trop = bruit/contamination.
HS-MSMass accuracy / m/z driftProblème calibration masseFondamental en HRMS.
HS-MSRetention time drift si LC/GC-MSDérive chromatographiqueÀ suivre sur standards/QC pools.
HS-MSBlank contamination scoreContaminants / carry-overComparer échantillons vs blancs.
HS-MSInternal standard CVVariabilité instrumentaleTrès robuste si standards disponibles.
HS-MSMissingness par featureInstabilité de détectionCrucial pour filtrer les variables.
Avec répétitionsRMS intra-échantillonRépétabilité globaleApplicable à toutes les technologies.
Avec répétitionsSAM / corrélation intra-échantillonRépétabilité de formeTrès utile pour spectres.
Avec répétitionsCV intra-échantillon par bande / featureRépétabilité localeDétecte les zones instables.
Avec répétitionsICC ou variance componentsPart variance échantillon vs techniqueTrès utile si plusieurs répétitions par sample.
Avec répétitionsDistance au centroïde intra-IDRépétition aberrantePermet de flagger la mauvaise répétition plutôt que le sample entier.
Bug-hunting / supervised audits
Famille de bug potentielMéthodes à ajouterCe que ça détecteÉtat dans l’explorateur
Shift spectral globalCorrélation spectre moyen inter-dataset, DTW, cross-correlation, comparaison positions de picsDécalage en longueur d'onde, mauvais alignement, interpolation différentePartiellement calculé: cross-correlation lag et dispersion des positions de pics vs spectre moyen.
Baseline / offset / gainRégression chaque spectre vs spectre moyen: x = a + b ref + residual; suivi de a, b, RMS résiduelOffset additif, effet multiplicatif, dérive de baselineCalculé dans reference.affine_*.
Mélange de lignes / mauvais appariement X-M-YVérification index, hash des lignes, duplication ID, distance spectrale intra-ID, labels incohérentsLignes mélangées, metadata mal alignées, Y attribué au mauvais spectrePartiellement couvert par répétabilité intra-ID; checks index/hash à ajouter au pipeline canonical.
Fuite d'information / répétitions mal splitéesGroupKFold par sample_id vs StratifiedKFold random; audit des partitions par sample_idPerformance artificiellement bonne due aux répétitionsNécessite splits et benchmark modèle; non calculé par la carte descriptive.
Label bugsÉchantillons proches en X mais Y différents, confident learning, erreurs systématiques FP/FNY inversés, erreurs de saisie, classes ambiguësNécessite Y et/ou modèle; recommandé pour l'explorateur supervisé.
Sous-domaines cachésPCA/UMAP/t-SNE + clustering non supervisé + association avec dataset/Y/date/operatorLots, campagnes, sondes, backgrounds non renseignésPartiellement calculé par structure PCA/LOF; UMAP/t-SNE hors carte statique.
Artefacts localisés inconnusCarte wavelength x dataset: différence moyenne, différence variance, KS par longueur d'ondeRégions spectrales anormales non anticipéesÀ calculer au niveau banque quand plusieurs datasets partagent un axe spectral.
Ruptures instrumentalesDiscontinuités dans dérivées, changepoint detectionSplice, raccord détecteur, saut local non prévuCalculé par jump/spike rates; changepoint plus avancé à ajouter.
Mélange / contamination spectraleNMF / unmixing / reconstruction par convex hullComposante externe: fond, plastique, solNon calculé automatiquement; nécessite hypothèses de composants ou grande bibliothèque.
Features instables mais prédictivesImportance modèle vs instabilité QC par variableModèle qui apprend un artefact plutôt qu'un signal biologiqueNécessite modèle supervisé; recommandé pour rapports de benchmark.

Variables

Targets 20

TOC

target · numeric
TOC distribution010202 – 3.885: 33.885 – 5.771: 65.771 – 7.656: 37.656 – 9.542: 39.542 – 11.43: 811.43 – 13.31: 1213.31 – 15.2: 1015.2 – 17.08: 417.08 – 18.97: 518.97 – 20.85: 1020.85 – 22.74: 1122.74 – 24.62: 1724.62 – 26.51: 1526.51 – 28.4: 1528.4 – 30.28: 1130.28 – 32.17: 832.17 – 34.05: 1234.05 – 35.94: 1035.94 – 37.82: 537.82 – 39.71: 339.71 – 41.59: 241.59 – 43.48: 143.48 – 45.36: 145.36 – 47.25: 40204060
n / missing1,000 / 821
Mean ± SD23.62 ± 10
Median24.37
Range2 – 47.25
CV0.423
Skew / kurtosis-0.077 / -0.49
Normal?yes

TOM

target · numeric
TOM distribution010204.2 – 8.062: 28.062 – 11.92: 611.92 – 15.79: 515.79 – 19.65: 519.65 – 23.51: 823.51 – 27.38: 1127.38 – 31.24: 1131.24 – 35.1: 435.1 – 38.96: 538.96 – 42.83: 842.83 – 46.69: 1746.69 – 50.55: 1650.55 – 54.41: 1754.41 – 58.28: 1158.28 – 62.14: 1562.14 – 66: 966 – 69.86: 1069.86 – 73.73: 873.73 – 77.59: 477.59 – 81.45: 181.45 – 85.31: 185.31 – 89.18: 089.18 – 93.04: 293.04 – 96.9: 30255075100
n / missing1,000 / 821
Mean ± SD46.7 ± 19.7
Median48
Range4.2 – 96.9
CV0.421
Skew / kurtosis-0.02 / -0.35
Normal?yes

ASH

target · numeric
ASH distribution05102.747 – 6.737: 26.737 – 10.73: 010.73 – 14.72: 014.72 – 18.71: 218.71 – 22.69: 022.69 – 26.68: 326.68 – 30.67: 130.67 – 34.66: 634.66 – 38.65: 538.65 – 42.64: 642.64 – 46.63: 746.63 – 50.62: 950.62 – 54.61: 554.61 – 58.6: 358.6 – 62.59: 562.59 – 66.58: 266.58 – 70.57: 470.57 – 74.56: 774.56 – 78.55: 378.55 – 82.54: 382.54 – 86.53: 486.53 – 90.52: 490.52 – 94.51: 294.51 – 98.5: 30255075100
n / missing1,000 / 914
Mean ± SD55.81 ± 22.1
Median52.2
Range2.747 – 98.5
CV0.396
Skew / kurtosis0.0016 / -0.63
Normal?yes

SOL

target · numeric
SOL distribution010200.1404 – 3.103: 93.103 – 6.065: 56.065 – 9.028: 59.028 – 11.99: 511.99 – 14.95: 814.95 – 17.92: 1617.92 – 20.88: 1120.88 – 23.84: 1123.84 – 26.8: 926.8 – 29.77: 229.77 – 32.73: 032.73 – 35.69: 335.69 – 38.65: 038.65 – 41.62: 041.62 – 44.58: 044.58 – 47.54: 047.54 – 50.5: 150.5 – 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: 1020406080
n / missing1,000 / 914
Mean ± SD17.19 ± 10.8
Median17.31
Range0.1404 – 71.24
CV0.628
Skew / kurtosis1.6 / 6.8
Normal?no

HEM

target · numeric
HEM distribution0510150.0001 – 1.299: 111.299 – 2.598: 72.598 – 3.897: 73.897 – 5.196: 75.196 – 6.495: 56.495 – 7.794: 87.794 – 9.093: 39.093 – 10.39: 110.39 – 11.69: 311.69 – 12.99: 612.99 – 14.29: 414.29 – 15.59: 515.59 – 16.89: 616.89 – 18.19: 418.19 – 19.48: 119.48 – 20.78: 320.78 – 22.08: 122.08 – 23.38: 223.38 – 24.68: 024.68 – 25.98: 025.98 – 27.28: 027.28 – 28.58: 028.58 – 29.88: 029.88 – 31.17: 2010203040
n / missing1,000 / 914
Mean ± SD9.339 ± 7.22
Median7.663
Range0.0001 – 31.17
CV0.774
Skew / kurtosis0.69 / 0.011
Normal?no

CEL

target · numeric
CEL distribution010200.0001 – 1.891: 171.891 – 3.782: 133.782 – 5.673: 55.673 – 7.564: 37.564 – 9.455: 69.455 – 11.35: 411.35 – 13.24: 713.24 – 15.13: 1115.13 – 17.02: 417.02 – 18.91: 618.91 – 20.8: 320.8 – 22.69: 222.69 – 24.58: 224.58 – 26.47: 026.47 – 28.37: 228.37 – 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: 10204060
n / missing1,000 / 914
Mean ± SD9.862 ± 8.34
Median8.762
Range0.0001 – 45.38
CV0.846
Skew / kurtosis1.1 / 2.4
Normal?no

LIC

target · numeric
LIC distribution010200.0823 – 1.722: 121.722 – 3.361: 113.361 – 5.001: 185.001 – 6.64: 76.64 – 8.28: 38.28 – 9.919: 129.919 – 11.56: 411.56 – 13.2: 813.2 – 14.84: 314.84 – 16.48: 016.48 – 18.12: 118.12 – 19.76: 319.76 – 21.4: 021.4 – 23.03: 023.03 – 24.67: 024.67 – 26.31: 026.31 – 27.95: 127.95 – 29.59: 029.59 – 31.23: 131.23 – 32.87: 032.87 – 34.51: 134.51 – 36.15: 036.15 – 37.79: 037.79 – 39.43: 1010203040
n / missing1,000 / 914
Mean ± SD7.834 ± 7.26
Median5.265
Range0.0823 – 39.43
CV0.927
Skew / kurtosis2.1 / 5.6
Normal?no

TN

target · numeric
TN distribution020400.0001 – 4.133: 104.133 – 8.267: 238.267 – 12.4: 2112.4 – 16.53: 1116.53 – 20.67: 1620.67 – 24.8: 2124.8 – 28.93: 3528.93 – 33.07: 1333.07 – 37.2: 837.2 – 41.33: 941.33 – 45.47: 745.47 – 49.6: 149.6 – 53.73: 153.73 – 57.87: 057.87 – 62: 062 – 66.13: 066.13 – 70.27: 070.27 – 74.4: 274.4 – 78.53: 078.53 – 82.67: 082.67 – 86.8: 086.8 – 90.93: 090.93 – 95.07: 095.07 – 99.2: 10255075100
n / missing1,000 / 821
Mean ± SD21.98 ± 14
Median23.1
Range0.0001 – 99.2
CV0.636
Skew / kurtosis1.4 / 5.4
Normal?no

totP

target · numeric
totP distribution02040600.242 – 6.825: 496.825 – 13.41: 1413.41 – 19.99: 319.99 – 26.57: 226.57 – 33.15: 1033.15 – 39.74: 739.74 – 46.32: 146.32 – 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: 1050100150200
n / missing1,000 / 913
Mean ± SD13.23 ± 19.8
Median6
Range0.242 – 158.2
CV1.49
Skew / kurtosis4.9 / 33
Normal?no

totK

target · numeric
totK distribution01020300.647 – 6.937: 196.937 – 13.23: 2813.23 – 19.52: 2519.52 – 25.81: 1125.81 – 32.1: 032.1 – 38.38: 338.38 – 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: 1050100150200
n / missing1,000 / 913
Mean ± SD14.28 ± 16.6
Median12.2
Range0.647 – 151.6
CV1.16
Skew / kurtosis6.8 / 56
Normal?no

totCa

target · numeric
totCa distribution0510150.197 – 5.925: 85.925 – 11.65: 1411.65 – 17.38: 317.38 – 23.11: 523.11 – 28.84: 628.84 – 34.56: 034.56 – 40.29: 140.29 – 46.02: 346.02 – 51.75: 451.75 – 57.47: 157.47 – 63.2: 163.2 – 68.93: 068.93 – 74.66: 374.66 – 80.38: 180.38 – 86.11: 086.11 – 91.84: 291.84 – 97.57: 497.57 – 103.3: 2103.3 – 109: 3109 – 114.8: 2114.8 – 120.5: 0120.5 – 126.2: 1126.2 – 131.9: 0131.9 – 137.7: 1050100150
n / missing1,000 / 935
Mean ± SD42.98 ± 39.5
Median25.34
Range0.197 – 137.7
CV0.919
Skew / kurtosis0.76 / -0.85
Normal?no

totMg

target · numeric
totMg distribution0510150.682 – 1.617: 31.617 – 2.551: 02.551 – 3.486: 23.486 – 4.421: 94.421 – 5.356: 115.356 – 6.29: 46.29 – 7.225: 77.225 – 8.16: 98.16 – 9.095: 79.095 – 10.03: 110.03 – 10.96: 110.96 – 11.9: 011.9 – 12.83: 312.83 – 13.77: 313.77 – 14.7: 014.7 – 15.64: 215.64 – 16.57: 016.57 – 17.51: 217.51 – 18.44: 018.44 – 19.38: 019.38 – 20.31: 120.31 – 21.25: 021.25 – 22.18: 022.18 – 23.12: 10102030
n / missing1,000 / 934
Mean ± SD7.656 ± 4.35
Median6.931
Range0.682 – 23.12
CV0.568
Skew / kurtosis1.3 / 2.1
Normal?no

Cd

target · numeric
Cd distribution02460.0001 – 0.1368: 20.1368 – 0.2734: 50.2734 – 0.4101: 40.4101 – 0.5467: 30.5467 – 0.6834: 20.6834 – 0.8201: 30.8201 – 0.9567: 50.9567 – 1.093: 31.093 – 1.23: 11.23 – 1.367: 31.367 – 1.503: 11.503 – 1.64: 21.64 – 1.777: 11.777 – 1.913: 01.913 – 2.05: 02.05 – 2.187: 02.187 – 2.323: 02.323 – 2.46: 02.46 – 2.597: 12.597 – 2.733: 02.733 – 2.87: 02.87 – 3.007: 23.007 – 3.143: 03.143 – 3.28: 101234
n / missing1,000 / 961
Mean ± SD0.968 ± 0.807
Median0.83
Range0.0001 – 3.28
CV0.833
Skew / kurtosis1.4 / 1.7
Normal?no

Cr

target · numeric
Cr distribution05104.83 – 16.86: 1016.86 – 28.88: 728.88 – 40.91: 240.91 – 52.94: 152.94 – 64.97: 064.97 – 76.99: 076.99 – 89.02: 289.02 – 101: 1101 – 113.1: 0113.1 – 125.1: 2125.1 – 137.1: 3137.1 – 149.2: 3149.2 – 161.2: 0161.2 – 173.2: 1173.2 – 185.2: 1185.2 – 197.3: 1197.3 – 209.3: 1209.3 – 221.3: 1221.3 – 233.3: 0233.3 – 245.4: 0245.4 – 257.4: 1257.4 – 269.4: 0269.4 – 281.5: 0281.5 – 293.5: 10100200300
n / missing1,000 / 962
Mean ± SD84.18 ± 80.4
Median39.05
Range4.83 – 293.5
CV0.955
Skew / kurtosis0.84 / -0.29
Normal?yes

Cu

target · numeric
Cu distribution010205.51 – 27.8: 927.8 – 50.09: 1650.09 – 72.38: 172.38 – 94.67: 194.67 – 117: 1117 – 139.3: 1139.3 – 161.5: 0161.5 – 183.8: 1183.8 – 206.1: 1206.1 – 228.4: 2228.4 – 250.7: 0250.7 – 273: 0273 – 295.3: 1295.3 – 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: 2473.6 – 495.9: 0495.9 – 518.2: 1518.2 – 540.5: 10200400600
n / missing1,000 / 962
Mean ± SD109.9 ± 150
Median40.69
Range5.51 – 540.5
CV1.36
Skew / kurtosis1.9 / 2.7
Normal?no

Ni

target · numeric
Ni distribution05103.56 – 6.084: 46.084 – 8.607: 108.607 – 11.13: 211.13 – 13.65: 113.65 – 16.18: 216.18 – 18.7: 118.7 – 21.23: 021.23 – 23.75: 223.75 – 26.27: 126.27 – 28.8: 128.8 – 31.32: 231.32 – 33.84: 033.84 – 36.37: 036.37 – 38.89: 338.89 – 41.42: 241.42 – 43.94: 243.94 – 46.46: 046.46 – 48.99: 248.99 – 51.51: 051.51 – 54.03: 054.03 – 56.56: 056.56 – 59.08: 159.08 – 61.61: 161.61 – 64.13: 1020406080
n / missing1,000 / 962
Mean ± SD23.29 ± 18.1
Median17.1
Range3.56 – 64.13
CV0.779
Skew / kurtosis0.72 / -0.66
Normal?yes

Pb

target · numeric
Pb distribution01020300.0001 – 165.9: 26165.9 – 331.8: 0331.8 – 497.8: 2497.8 – 663.7: 0663.7 – 829.6: 1829.6 – 995.5: 3995.5 – 1161: 21161 – 1327: 01327 – 1493: 01493 – 1659: 21659 – 1825: 01825 – 1991: 11991 – 2157: 02157 – 2323: 02323 – 2489: 02489 – 2655: 02655 – 2821: 02821 – 2987: 02987 – 3152: 03152 – 3318: 03318 – 3484: 03484 – 3650: 03650 – 3816: 03816 – 3,982: 101,0002,0003,0004,000
n / missing1,000 / 962
Mean ± SD429.2 ± 806
Median12.1
Range0.0001 – 3,982
CV1.88
Skew / kurtosis2.8 / 9.5
Normal?no

Zn

target · numeric
Zn distribution051039.38 – 130.3: 4130.3 – 221.2: 7221.2 – 312.1: 8312.1 – 402.9: 3402.9 – 493.8: 5493.8 – 584.7: 0584.7 – 675.6: 0675.6 – 766.5: 1766.5 – 857.4: 2857.4 – 948.3: 1948.3 – 1039: 11039 – 1130: 01130 – 1221: 11221 – 1312: 01312 – 1403: 01403 – 1494: 01494 – 1585: 11585 – 1675: 21675 – 1766: 01766 – 1857: 01857 – 1948: 01948 – 2039: 02039 – 2130: 12130 – 2221: 101,0002,0003,000
n / missing1,000 / 962
Mean ± SD559.1 ± 576
Median320
Range39.38 – 2221
CV1.03
Skew / kurtosis1.6 / 1.7
Normal?no

OR_category

target · categorical
OR_category classesAnimalAnimal: 863863UrbanUrban: 8181AgroindustrialAgroindustrial: 3636Animal&urbanAnimal&urban: 2020
n / missing1,000 / 0
Classes4
Balance (entropy)0.38
Imbalance ratio4e+01
Top classAnimal (863)

OR_origin

target · categorical
OR_origin classespoultrypoultry: 340340pigpig: 300300bovinebovine: 208208landfilllandfill: 7171slaughterhouseslaughterhouse: 2727bovine&residuesbovine&residues: 2020goatgoat: 1212pottingsoilpottingsoil: 88tobaccotobacco: 33fish processingfish processing: 22+8 more+8 more: 99
n / missing1,000 / 0
Classes18
Balance (entropy)0.55
Imbalance ratio340
Top classpoultry (340)

Metadata 30

Collection_date

metadata · categorical
Collection_date classes2011-08-26 00:00:002011-08-26 00:00:00: 46462011-07-06 00:00:002011-07-06 00:00:00: 44442011-04-19 00:00:002011-04-19 00:00:00: 36362009-05-13 00:00:002009-05-13 00:00:00: 27272009-05-06 00:00:002009-05-06 00:00:00: 26262011-07-13 00:00:002011-07-13 00:00:00: 23232009-06-19 00:00:002009-06-19 00:00:00: 22222009-04-03 00:00:002009-04-03 00:00:00: 21212009-04-08 00:00:002009-04-08 00:00:00: 21212009-04-29 00:00:002009-04-29 00:00:00: 2121+10 more+10 more: 196196
n / missing1,000 / 0
Classes108
Balance (entropy)0.92
Imbalance ratio46
Top class2011-08-26 00:00:00 (46)

Town

metadata · categorical
Town classesMahajangaMahajanga: 728728AntananarivoAntananarivo: 248248ItasyItasy: 1717ManjakandrianaManjakandriana: 55AmbatondrazakaAmbatondrazaka: 11AntsirabeAntsirabe: 11
n / missing1,000 / 0
Classes6
Balance (entropy)0.38
Imbalance ratio728
Top classMahajanga (728)

Township

metadata · categorical
Township classesBelobakaBelobaka: 9999AmbondronaAmbondrona: 9393AmborovyAmborovy: 9191AntanimasajyAntanimasajy: 8484AmpitilovaAmpitilova: 8282Antanambao AmbalavatoAntanambao Ambalavato: 6868AndakanaAndakana: 5656Tsararano AmbonyTsararano Ambony: 4949AndralanitraAndralanitra: 4848AntanimalandyAntanimalandy: 3636+10 more+10 more: 167167
n / missing1,000 / 85
Classes35
Balance (entropy)0.82
Imbalance ratio99
Top classBelobaka (99)

Animal_Development`_stages

metadata · categorical
Animal_Development`_stages classesadultadult: 210210youngyoung: 159159pigletpiglet: 4040chickchick: 99calfcalf: 88chikchik: 22
n / missing1,000 / 572
Classes6
Balance (entropy)0.62
Imbalance ratio105
Top classadult (210)

Animal_Age(days)

metadata · categorical
Animal_Age(days) classes36.036.0: 99371.0371.0: 88392.0392.0: 88385.0385.0: 77448.0448.0: 55406.0406.0: 55175.0175.0: 555.05.0: 4412.012.0: 4419.019.0: 44+10 more+10 more: 4040
n / missing1,000 / 852
Classes44
Balance (entropy)0.95
Imbalance ratio9
Top class36.0 (9)

Animal_Sex

metadata · categorical
Animal_Sex classesfemalefemale: 329329male&femalemale&female: 189189malemale: 2424
n / missing1,000 / 458
Classes3
Balance (entropy)0.74
Imbalance ratio1e+01
Top classfemale (329)

Adult_Type

metadata · categorical
Adult_Type classeslaying henlaying hen: 101101lactatinglactating: 7878broilerbroiler: 4343castratedcastrated: 2424dry cowdry cow: 1616litterlitter: 44
n / missing1,000 / 734
Classes6
Balance (entropy)0.82
Imbalance ratio3e+01
Top classlaying hen (101)

Exploitation_Type

metadata · categorical
Exploitation_Type classesfarmfarm: 711711industrialindustrial: 171171
n / missing1,000 / 118
Classes2
Balance (entropy)0.71
Imbalance ratio4
Top classfarm (711)

Feed_RiceBran

metadata · categorical
Feed_RiceBran classesnono: 570570yesyes: 237237
n / missing1,000 / 193
Classes2
Balance (entropy)0.87
Imbalance ratio2
Top classno (570)

Feed_BlackEyedPeas

metadata · categorical
Feed_BlackEyedPeas classesnono: 615615yesyes: 192192
n / missing1,000 / 193
Classes2
Balance (entropy)0.79
Imbalance ratio3
Top classno (615)

Feed_IndustrializedFeedstuff

metadata · categorical
Feed_IndustrializedFeedstuff classesnono: 442442yesyes: 365365
n / missing1,000 / 193
Classes2
Balance (entropy)0.99
Imbalance ratio1
Top classno (442)

Feed_HomemadeFeedstuff

metadata · categorical
Feed_HomemadeFeedstuff classesnono: 727727yesyes: 8080
n / missing1,000 / 193
Classes2
Balance (entropy)0.47
Imbalance ratio9
Top classno (727)

Feed_Weeds

metadata · categorical
Feed_Weeds classesnono: 613613yesyes: 194194
n / missing1,000 / 193
Classes2
Balance (entropy)0.8
Imbalance ratio3
Top classno (613)

Feed_Maizestalk

metadata · categorical
Feed_Maizestalk classesnono: 806806yesyes: 11
n / missing1,000 / 193
Classes2
Balance (entropy)0.014
Imbalance ratio806
Top classno (806)

Feed_Fodder

metadata · categorical
Feed_Fodder classesnono: 781781yesyes: 2626
n / missing1,000 / 193
Classes2
Balance (entropy)0.21
Imbalance ratio3e+01
Top classno (781)

Feed_KitchenWaste

metadata · categorical
Feed_KitchenWaste classesnono: 795795yesyes: 1212
n / missing1,000 / 193
Classes2
Balance (entropy)0.11
Imbalance ratio7e+01
Top classno (795)

Feed_CottonResidues

metadata · categorical
Feed_CottonResidues classesnono: 791791yesyes: 1616
n / missing1,000 / 193
Classes2
Balance (entropy)0.14
Imbalance ratio5e+01
Top classno (791)

Feed_peanutsfoliage

metadata · categorical
Feed_peanutsfoliage classesnono: 788788yesyes: 1919
n / missing1,000 / 193
Classes2
Balance (entropy)0.16
Imbalance ratio4e+01
Top classno (788)

Feed_sweetpotato&pumpkin&cassava

metadata · categorical
Feed_sweetpotato&pumpkin&cassava classesnono: 788788yesyes: 1919
n / missing1,000 / 193
Classes2
Balance (entropy)0.16
Imbalance ratio4e+01
Top classno (788)

OR_Nature

metadata · categorical
OR_Nature classesmanuremanure: 395395liquid manureliquid manure: 373373droppingsdroppings: 9191fermentable fraction of house…fermentable fraction of household waste: 7171bovine&residuesbovine&residues: 2020bovine hornbovine horn: 1313dried blooddried blood: 1313potting soilpotting soil: 88litterlitter: 44ashash: 33+9 more+9 more: 99
n / missing1,000 / 0
Classes19
Balance (entropy)0.5
Imbalance ratio395
Top classmanure (395)

OR_Name

metadata · categorical
OR_Name classesslurryslurry: 373373manuremanure: 289289droppingsdroppings: 9191
n / missing1,000 / 247
Classes3
Balance (entropy)0.88
Imbalance ratio4
Top classslurry (373)

Litter/No_litter

metadata · categorical
Litter/No_litter classesnono: 464464yesyes: 286286
n / missing1,000 / 250
Classes2
Balance (entropy)0.96
Imbalance ratio2
Top classno (464)

Littter_Rice_straw

metadata · categorical
Littter_Rice_straw classesnono: 196196yesyes: 2222
n / missing1,000 / 782
Classes2
Balance (entropy)0.47
Imbalance ratio9
Top classno (196)

Litter_Grass

metadata · categorical
Litter_Grass classesnono: 214214yesyes: 44
n / missing1,000 / 782
Classes2
Balance (entropy)0.13
Imbalance ratio5e+01
Top classno (214)

Litter_Woodchip

metadata · categorical
Litter_Woodchip classesyesyes: 196196nono: 2222
n / missing1,000 / 782
Classes2
Balance (entropy)0.47
Imbalance ratio9
Top classyes (196)

Litter_Fodder

metadata · categorical
Litter_Fodder classesnono: 209209yesyes: 99
n / missing1,000 / 782
Classes2
Balance (entropy)0.25
Imbalance ratio2e+01
Top classno (209)

Sample_type

metadata · categorical
Sample_type classesCompositeComposite: 858858abreuvoirabreuvoir: 3939dortoirdortoir: 3939couvoircouvoir: 3939partie bassepartie basse: 1010partie centralepartie centrale: 88partie hautepartie haute: 77
n / missing1,000 / 0
Classes7
Balance (entropy)0.32
Imbalance ratio1e+02
Top classComposite (858)

Composted/Non_conposted

metadata · categorical
Composted/Non_conposted classesncnc: 894894cc: 106106
n / missing1,000 / 0
Classes2
Balance (entropy)0.49
Imbalance ratio8
Top classnc (894)

OR_age(days)

metadata · categorical
OR_age(days) classes18251825: 7736503650: 773030: 66180180: 66365365: 66sup3650sup3650: 551414: 1177: 1122: 1121902190: 11+3 more+3 more: 33
n / missing1,000 / 956
Classes13
Balance (entropy)0.88
Imbalance ratio7
Top class1825 (7)

OR_Stage

metadata · categorical
OR_Stage classesmaturemature: 1111earlyearly: 1010jeunejeune: 33
n / missing1,000 / 976
Classes3
Balance (entropy)0.89
Imbalance ratio4
Top classmature (11)
Constant metadata 22
  • 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
  • standardized_on_xdsNO
  • decoderpandas.read_excel/openpyxl
  • notesCC BY-NC-SA source, many chemical targets are sparse. Variable dictionary sheets retained in source workbooks.

Alignment

Alignment levelsample
Sample id availableyes
Samples1,000
Observations (total)1,000
Reps per samplemin 1 · mean 1 · max 1

Provenance & citation

ContributorSpectroscopic VIS-NIR analysis of organic resources from Madagascar
Origin · dataverse [open]10.18167/DVN1/SZSTBD — CIRAD Dataverse
Origin · dataverse [open]10.18167/DVN1/YRMQ52 — CIRAD Dataverse
Origin · url [open]https://creativecommons.org/licenses/by-nc-sa/4.0/
Origin · script [manual]source_to_standard.py — standardization script (maintainer-only)

Governance & integrity

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 version1.0.0
Schema / protocol2.0
Content hash461df0c1eefa9db4…
Processing hash6b3c02c86ef5be53…
Metadata hashdabfdce228a060e1…

Load this dataset

# pip install nirs4all-datasets
from nirs4all_datasets import get

# private dataset — export requires a Dataverse token
ds = get("cirad_madagascar_organic_resources_asd_nonstd_visnir", token="…")
X, y = ds.x(), ds.y()
print(X.shape, y.shape)

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