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EcoSIS Ground-leaf CABO spectra from herbarium project v2 (reflectance)

ecosis · NIR

EcoSIS Ground-leaf CABO spectra from herbarium project v2 (reflectance). v2.0 standardized NIRS package: 1 spectral source(s), 31 declared target(s). Auto-generated from dataset_card.json (verify before publication).

nirv2ecosis
607
samples
2,001
wavelengths
1
sources
31
targets
27
metadata
NIR
family

Dataset property explorer

Mean profile risk0.37
Highest axisArtefacts locaux · 1.00
Diagnostics8
Sources profiled1
EcoSIS Ground-leaf CABO spectra from herbarium project v2 (reflectance) property profile0.250.50.751integritynoiseartefactsbaselinePCA outliersreferencerepeatabilitystructureEcoSIS Ground-leaf CABO spectra from herbarium project v2 (reflectance) profileintegrity: 0.00noise: 0.00artefacts: 1.00baseline: 0.41PCA outliers: 0.45reference: 0.48repeatability: 0.00structure: 0.61EcoSIS Ground-l…0 center · 1 outer ring · outward = stronger anomaly / heterogeneity signal

Profile axes

Intégrité0.00
Artefacts locaux1.00
Bruit0.00
Outliers PCA0.45
Distance à la référence0.48
Répétabilité0.00
Baseline / forme0.41
Structure multi-régimes0.61
Diagnostic hypotheses00.250.50.751hypothesis scoreSplice / raccord détecteursSplice / raccord détecteurs: 0.720.72Erreur interpolation / réécha…Erreur interpolation / rééchantillonnage: 0.610.61Signature VERA25-likeSignature VERA25-like: 0.540.54Erreur calibration / référenc…Erreur calibration / référence blanche: 0.430.43Différence de sonde / géométr…Différence de sonde / géométrie: 0.400.40Fond différentFond différent: 0.360.36Spectre hors domaine valideSpectre hors domaine valide: 0.360.36Dataset multi-régimesDataset multi-régimes: 0.360.36
DiagnosticScoreForceSignauxInterprétation probable
Splice / raccord détecteursX0.72moyenneSpike rate 1.00, Jump rate 1.00, SNR non dégradé 1.00Rupture aux jonctions de détecteurs, calibration locale ou sonde différente.
Erreur interpolation / rééchantillonnageX0.61moyenneSpike rate 1.00, Jump rate 1.00, SNR normal/élevé 1.00Artefacts numériques ou traitement spectral incorrect.
Signature VERA25-likeX0.54moyenneSpike rate 1.00, Jump rate 1.00, RMS/SAM référence 0.48Combinaison possible changement de sonde + splice, amplifiée par géométrie, fond ou calibration.
Erreur calibration / référence blancheX0.43moyenneartefacts locaux 1.00, RMS/SAM référence 0.48, Mahalanobis / T2 0.45Décalage systématique entre campagnes, instruments ou référence blanche.
Différence de sonde / géométrieX0.40faibleRMS/SAM référence 0.48, Mahalanobis / T2 0.45, PCA Q 0.42Modification de l'illumination, collecte, angle ou distance sonde-échantillon.
Fond différentX0.36faibleRMS/SAM référence 0.48, Mahalanobis / T2 0.45, PCA Q 0.42Effet systématique du support, blanc/noir, transflectance ou environnement de mesure.
Spectre hors domaine valideX0.36faibleStructure PCA 0.61, RMS/SAM référence 0.48, Mahalanobis / T2 0.45Variété, espèce, lot ou condition différente mais physiquement plausible.
Dataset multi-régimesX0.36faibleStructure PCA 0.61, RMS/SAM référence 0.48, Mahalanobis / T2 0.45Mélange de campagnes, opérateurs, lots, setups ou sous-populations spectrales.

Spectral sources

ground_spec_avg.csv

X · NIR · Spectral Evolution PSR+ 3500
ground_spec_avg.csv spectra0.000.250.500.751.0005001,0001,5002,0002,500q05-q95 envelopeq25-q75 envelopemedian spectrummedianq25–q75q05–q95wavelength / nm400nm — median 0.08098 (q25–q75 0.07309–0.0891)414nm — median 0.06104 (q25–q75 0.05262–0.06997)429nm — median 0.0732 (q25–q75 0.0628–0.08564)443nm — median 0.0897 (q25–q75 0.07611–0.1069)458nm — median 0.1096 (q25–q75 0.09245–0.1305)472nm — median 0.1285 (q25–q75 0.106–0.1528)486nm — median 0.1452 (q25–q75 0.1227–0.1722)501nm — median 0.1568 (q25–q75 0.1357–0.1856)515nm — median 0.1788 (q25–q75 0.1558–0.2119)529nm — median 0.2007 (q25–q75 0.1752–0.2381)544nm — median 0.2094 (q25–q75 0.182–0.2464)558nm — median 0.2349 (q25–q75 0.2053–0.2748)573nm — median 0.2525 (q25–q75 0.2235–0.2941)587nm — median 0.2676 (q25–q75 0.2345–0.3086)601nm — median 0.245 (q25–q75 0.2168–0.2894)616nm — median 0.223 (q25–q75 0.1963–0.2645)630nm — median 0.2373 (q25–q75 0.2103–0.2802)645nm — median 0.2128 (q25–q75 0.1877–0.2554)659nm — median 0.1439 (q25–q75 0.1228–0.1811)673nm — median 0.1129 (q25–q75 0.09518–0.1423)688nm — median 0.2042 (q25–q75 0.1778–0.2499)702nm — median 0.3306 (q25–q75 0.2937–0.3772)717nm — median 0.4447 (q25–q75 0.4089–0.4932)731nm — median 0.5214 (q25–q75 0.4819–0.5661)745nm — median 0.5832 (q25–q75 0.5387–0.6206)760nm — median 0.6232 (q25–q75 0.5795–0.6677)774nm — median 0.6525 (q25–q75 0.6096–0.6951)788nm — median 0.6756 (q25–q75 0.6326–0.7146)803nm — median 0.6935 (q25–q75 0.6527–0.7302)817nm — median 0.71 (q25–q75 0.6697–0.7431)832nm — median 0.724 (q25–q75 0.6859–0.7565)846nm — median 0.7361 (q25–q75 0.7–0.7675)860nm — median 0.7469 (q25–q75 0.7129–0.7774)875nm — median 0.7577 (q25–q75 0.7258–0.7861)889nm — median 0.7663 (q25–q75 0.735–0.7928)904nm — median 0.7733 (q25–q75 0.7443–0.7987)918nm — median 0.7807 (q25–q75 0.7521–0.8061)932nm — median 0.788 (q25–q75 0.7592–0.8126)947nm — median 0.7952 (q25–q75 0.7675–0.8198)961nm — median 0.8003 (q25–q75 0.7728–0.8235)976nm — median 0.8018 (q25–q75 0.7769–0.8246)990nm — median 0.8045 (q25–q75 0.7811–0.8258)1,004nm — median 0.8096 (q25–q75 0.7871–0.8303)1,019nm — median 0.8146 (q25–q75 0.7923–0.8344)1,033nm — median 0.8189 (q25–q75 0.7964–0.8387)1,047nm — median 0.8225 (q25–q75 0.8012–0.8428)1,062nm — median 0.8269 (q25–q75 0.8054–0.8467)1,076nm — median 0.8299 (q25–q75 0.8089–0.85)1,091nm — median 0.8328 (q25–q75 0.8118–0.853)1,105nm — median 0.8362 (q25–q75 0.8145–0.8552)1,119nm — median 0.837 (q25–q75 0.8164–0.8553)1,134nm — median 0.8353 (q25–q75 0.8156–0.8527)1,148nm — median 0.8332 (q25–q75 0.8137–0.8503)1,163nm — median 0.8274 (q25–q75 0.8094–0.8439)1,177nm — median 0.8204 (q25–q75 0.8037–0.8355)1,191nm — median 0.8144 (q25–q75 0.798–0.829)1,206nm — median 0.812 (q25–q75 0.7966–0.8264)1,220nm — median 0.8161 (q25–q75 0.8008–0.8301)1,235nm — median 0.825 (q25–q75 0.8094–0.8385)1,249nm — median 0.8308 (q25–q75 0.8149–0.8447)1,263nm — median 0.8334 (q25–q75 0.8179–0.8474)1,278nm — median 0.8348 (q25–q75 0.8199–0.8491)1,292nm — median 0.8377 (q25–q75 0.823–0.8518)1,306nm — median 0.8402 (q25–q75 0.8254–0.8544)1,321nm — median 0.8403 (q25–q75 0.8258–0.8542)1,335nm — median 0.8366 (q25–q75 0.8225–0.8505)1,350nm — median 0.826 (q25–q75 0.8131–0.8384)1,364nm — median 0.8128 (q25–q75 0.8006–0.8235)1,378nm — median 0.8015 (q25–q75 0.7898–0.8112)1,393nm — median 0.7892 (q25–q75 0.7786–0.7996)1,407nm — median 0.7688 (q25–q75 0.7582–0.7799)1,422nm — median 0.723 (q25–q75 0.7126–0.7336)1,436nm — median 0.6841 (q25–q75 0.6732–0.6952)1,450nm — median 0.6671 (q25–q75 0.6552–0.6788)1,465nm — median 0.6657 (q25–q75 0.6541–0.6773)1,479nm — median 0.6691 (q25–q75 0.6574–0.68)1,494nm — median 0.6718 (q25–q75 0.6608–0.6819)1,508nm — median 0.6766 (q25–q75 0.6664–0.6871)1,522nm — median 0.6833 (q25–q75 0.6728–0.6938)1,537nm — median 0.6881 (q25–q75 0.6778–0.6984)1,551nm — median 0.69 (q25–q75 0.6799–0.7003)1,565nm — median 0.6909 (q25–q75 0.6806–0.7015)1,580nm — median 0.6924 (q25–q75 0.6822–0.703)1,594nm — median 0.6956 (q25–q75 0.6855–0.7064)1,609nm — median 0.701 (q25–q75 0.6909–0.7118)1,623nm — median 0.7045 (q25–q75 0.6949–0.7154)1,637nm — median 0.706 (q25–q75 0.6966–0.7169)1,652nm — median 0.703 (q25–q75 0.692–0.7141)1,666nm — median 0.6945 (q25–q75 0.6845–0.7054)1,681nm — median 0.6856 (q25–q75 0.6763–0.6954)1,695nm — median 0.6729 (q25–q75 0.6637–0.6833)1,709nm — median 0.6613 (q25–q75 0.653–0.6711)1,724nm — median 0.6507 (q25–q75 0.6418–0.6612)1,738nm — median 0.654 (q25–q75 0.6455–0.6642)1,753nm — median 0.6592 (q25–q75 0.6505–0.6692)1,767nm — median 0.6631 (q25–q75 0.6536–0.6725)1,781nm — median 0.6715 (q25–q75 0.6624–0.6812)1,796nm — median 0.6762 (q25–q75 0.6666–0.6855)1,810nm — median 0.6794 (q25–q75 0.6694–0.6887)1,824nm — median 0.6825 (q25–q75 0.6729–0.6921)1,839nm — median 0.6884 (q25–q75 0.6786–0.6977)1,853nm — median 0.6932 (q25–q75 0.6834–0.7033)1,868nm — median 0.6947 (q25–q75 0.684–0.7052)1,882nm — median 0.6856 (q25–q75 0.6734–0.6972)1,896nm — median 0.6567 (q25–q75 0.6411–0.6701)1,911nm — median 0.6115 (q25–q75 0.5911–0.6276)1,925nm — median 0.5916 (q25–q75 0.5695–0.6095)1,940nm — median 0.5892 (q25–q75 0.5703–0.6073)1,954nm — median 0.5947 (q25–q75 0.5768–0.6121)1,968nm — median 0.601 (q25–q75 0.5841–0.6171)1,983nm — median 0.6068 (q25–q75 0.591–0.6222)1,997nm — median 0.6083 (q25–q75 0.5942–0.6226)2,012nm — median 0.6005 (q25–q75 0.5893–0.6137)2,026nm — median 0.5808 (q25–q75 0.5706–0.5932)2,040nm — median 0.5545 (q25–q75 0.544–0.5666)2,055nm — median 0.5303 (q25–q75 0.5193–0.5429)2,069nm — median 0.518 (q25–q75 0.5061–0.5304)2,083nm — median 0.5096 (q25–q75 0.4978–0.5229)2,098nm — median 0.5029 (q25–q75 0.4902–0.517)2,112nm — median 0.5001 (q25–q75 0.4857–0.5143)2,127nm — median 0.4988 (q25–q75 0.4828–0.5139)2,141nm — median 0.4969 (q25–q75 0.4812–0.5128)2,155nm — median 0.4987 (q25–q75 0.4826–0.5139)2,170nm — median 0.5 (q25–q75 0.4842–0.5152)2,184nm — median 0.5043 (q25–q75 0.4893–0.5193)2,199nm — median 0.5116 (q25–q75 0.4974–0.5266)2,213nm — median 0.5181 (q25–q75 0.5049–0.5334)2,227nm — median 0.5196 (q25–q75 0.5069–0.5348)2,242nm — median 0.5055 (q25–q75 0.4935–0.5191)2,256nm — median 0.4795 (q25–q75 0.4687–0.4933)2,271nm — median 0.4589 (q25–q75 0.448–0.4706)2,285nm — median 0.4483 (q25–q75 0.4369–0.4597)2,299nm — median 0.4356 (q25–q75 0.4239–0.4477)2,314nm — median 0.4298 (q25–q75 0.4181–0.4422)2,328nm — median 0.4423 (q25–q75 0.4313–0.4533)2,342nm — median 0.4396 (q25–q75 0.4278–0.4511)2,357nm — median 0.4469 (q25–q75 0.4348–0.4579)2,371nm — median 0.4533 (q25–q75 0.4415–0.4646)2,386nm — median 0.4545 (q25–q75 0.4428–0.4663)2,400nm — median 0.4541 (q25–q75 0.4431–0.4662)

Sampling

Wavelengths2,001
Axis range400–2,400 nm
Mean spacing1 nm
Griduniform
Observations607

Signal & quality

Value range0.0357 – 0.907
Mean range0.0622 – 0.838
Mean level0.5949
Area1190
PTP0.7756
Noise RMS2.3215e-05
SNR2.6e+04
SNR dB9e+01 dB
Dynamic range0.776
Smoothness0.0002647
Saturated0.0%
X-outliers278

Integrity & artefacts

NaN ratio0.00%
Inf count0
Zero ratio0.00%
Spike count66,675
Spike rate5.49%
Jump count28,010
Jump rate2.31%
Clip fraction0.00%

Shape & reference

Baseline slope0.15984
Curvature RMS0.00026357
D1 RMS0.0017031
RMS to mean0.029239
RMS p950.059914
SAM to mean0.037981
SAM p950.07722
Affine offset p950.086165
Affine gain p95 Δ0.13774
Affine residual p950.044221
Xcorr lag p950

Outliers & repeatability

PCA Q p95/median3.4
Hotelling T2 p95/median3.3
Mahalanobis H p95/median1.8
Repeat groups0

Dimensionality (PCA)

Effective rank3.7
PCs → 95% var5
PCs → 99% var8
Top-10 cum. var99.4%
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_reflectance0.594930.41faibleTrop sombreFond, géométriemean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Amplitude globaleArea under curveamplitude.area_under_curve1190.20.41faibleNormalDistance 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_peak0.775590.00faibleVariabilité forteSaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Amplitude globaleVarianceamplitude.variance0.0456920.00faibleNormal ou hétérogèneMauvais contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
BruitNoise RMSnoise.noise_rms2.3215e-050.00faibleStableLampe, détecteurmedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
BruitSNRnoise.snr256270.00faibleBon signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
BruitBandwise SNRnoise.bandwise_snr_min554.830.00faibleZone 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_count66,6751.00fortArtefactsCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Artefacts locauxSpike rateartefacts.spike_rate5.49%1.00fortSpectre suspectInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Artefacts locauxJump countartefacts.jump_count28,0101.00fortRaccord détecteurSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Artefacts locauxJump rateartefacts.jump_rate2.31%1.00fortProblème spectralCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Artefacts locauxClip fractionartefacts.clip_fraction0.000165%0.00faibleNormalDétecteur saturéfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Forme spectraleBaseline slopeshape.baseline_slope0.159840.41faibleStableÉclairementlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Forme spectraleCurvature RMSshape.curvature_rms0.000263570.03faibleLisseFond, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Forme spectraleD1 RMSshape.d1_rms0.00170310.04faiblePlatBiologie ou artefactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Outliers multivariésPCA Q (SPE)outliers.pca_q_ratio3.37570.42moyenSpectre atypiqueArtefact, mélangep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Outliers multivariésHotelling T²outliers.hotelling_t2_ratio3.25150.41faibleCentralVariabilité naturellep95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Outliers multivariésMahalanobis Houtliers.mahalanobis_h_ratio1.80320.45moyenOutlier globalDomaine différentp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparaison à référenceRMS to mean spectrumreference.rms_to_mean_spectrum_p950.0599140.31faibleTypiqueDomain 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_p950.077220.22faibleSimilaireFond, 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_density2.27280.61moyenSous-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_p951.81640.41faiblePopulation normaleCas raresp95 approximate LOF from PCA-score kNN distancesalert = min(1, max(0, LOF_p95 - 1) / 2)
Structure du datasetIsolation Forest scorestructure.isolation_forest_score_p950.542140.61moyenSpectre atypiqueDiverses causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent
X PCA score plot-4-20246-2024PC1 -0.7489 · PC2 -0.9978PC1 -0.4968 · PC2 -0.8171PC1 -1.163 · PC2 -0.7341PC1 -1.2 · PC2 -1.165PC1 -0.03097 · PC2 -0.9827PC1 -1.106 · PC2 -0.3049PC1 -0.8672 · PC2 -0.3773PC1 -1.125 · PC2 -0.7717PC1 -0.7584 · PC2 -0.05535PC1 -0.799 · PC2 -0.7106PC1 -1.827 · PC2 -0.2484PC1 -1.244 · PC2 -0.2256PC1 -1.304 · PC2 0.5494PC1 -1.683 · PC2 0.0885PC1 -1.14 · PC2 0.03585PC1 -1.188 · PC2 -0.4932PC1 -1.465 · PC2 -0.1593PC1 -1.391 · PC2 -0.3615PC1 -1.672 · PC2 0.07865PC1 -1.346 · PC2 0.3578PC1 1.167 · PC2 -0.2352PC1 0.6179 · PC2 -0.5471PC1 -0.2929 · PC2 0.1889PC1 -0.1723 · PC2 -0.2526PC1 -0.9468 · PC2 0.185PC1 -0.2944 · PC2 0.1545PC1 -0.5042 · PC2 0.1714PC1 0.02308 · PC2 -0.5966PC1 0.3487 · PC2 -0.3914PC1 0.3355 · PC2 -0.7339PC1 0.1386 · PC2 -0.9717PC1 0.7388 · PC2 -0.7301PC1 0.1386 · PC2 -0.8754PC1 -0.02975 · PC2 -0.8215PC1 -0.09386 · PC2 -1.08PC1 -0.5376 · PC2 -0.9291PC1 -0.008426 · PC2 -0.5661PC1 -1.374 · PC2 -0.9233PC1 0.1324 · PC2 -0.7442PC1 0.4477 · PC2 -0.5278PC1 -0.4056 · PC2 -1.242PC1 -0.3976 · PC2 -0.7042PC1 0.4695 · PC2 -0.694PC1 0.1001 · PC2 -0.897PC1 0.308 · PC2 -0.7459PC1 0.01905 · PC2 -0.7735PC1 0.08961 · PC2 -0.8657PC1 -1.323 · PC2 -0.9904PC1 -0.01139 · PC2 -0.474PC1 -1.438 · PC2 -0.2197PC1 -1.878 · PC2 -0.3792PC1 -1.36 · PC2 -0.04761PC1 -1.622 · PC2 -0.4806PC1 -1.396 · PC2 0.07347PC1 -0.9455 · PC2 -0.2904PC1 -1.454 · PC2 -0.1628PC1 -0.9019 · PC2 0.04344PC1 -1.133 · PC2 -0.4206PC1 0.8271 · PC2 -0.5445PC1 0.5251 · PC2 -0.5685PC1 0.4375 · PC2 -0.6396PC1 2.266 · PC2 -0.4898PC1 2.022 · PC2 -0.2763PC1 2.697 · PC2 -0.6779PC1 1.993 · PC2 -0.6932PC1 1.573 · PC2 -0.7349PC1 1.817 · PC2 -0.7406PC1 2.149 · PC2 -0.07644PC1 0.1368 · PC2 -0.1901PC1 -0.8442 · PC2 -0.8454PC1 -0.6657 · PC2 -0.3806PC1 -0.1 · PC2 -0.7979PC1 -0.6663 · PC2 -0.8585PC1 0.06766 · PC2 -0.5371PC1 0.8945 · PC2 -0.7536PC1 -0.514 · PC2 -0.5087PC1 -0.1889 · PC2 -0.6963PC1 -0.04722 · PC2 -0.7541PC1 0.7134 · PC2 -0.5946PC1 -0.3172 · PC2 -0.9633PC1 -0.6181 · PC2 -0.4406PC1 -1.113 · PC2 -0.255PC1 -0.191 · PC2 -0.8249PC1 -0.3915 · PC2 -0.2229PC1 0.09188 · PC2 -0.4008PC1 -0.4979 · PC2 -0.6544PC1 -0.5864 · PC2 -1.003PC1 0.1183 · PC2 -0.5912PC1 -0.01626 · PC2 -0.05575PC1 -0.3946 · PC2 -0.1178PC1 0.4062 · PC2 0.01976PC1 -0.5358 · PC2 -0.1185PC1 -0.02284 · PC2 -0.6944PC1 -0.4962 · PC2 -0.1421PC1 0.3184 · PC2 -0.7003PC1 1.347 · PC2 0.1289PC1 2.96 · PC2 -0.08532PC1 2.065 · PC2 0.1066PC1 0.5512 · PC2 -0.7403PC1 0.2077 · PC2 -0.7675PC1 -0.1539 · PC2 -0.8713PC1 -0.1463 · PC2 -1.02PC1 -0.1011 · PC2 -0.8307PC1 -0.0477 · PC2 -0.8439PC1 0.2596 · PC2 -1.165PC1 -0.02854 · PC2 -0.8398PC1 -0.5303 · PC2 -1.115PC1 -0.2905 · PC2 -1.132PC1 -0.9109 · PC2 -0.71PC1 -0.737 · PC2 -0.8703PC1 -1.322 · PC2 -0.08384PC1 -0.3769 · PC2 -1.004PC1 -0.9122 · PC2 -0.8714PC1 0.1982 · PC2 -1.202PC1 -0.5411 · PC2 -0.9504PC1 -0.6375 · PC2 -1.016PC1 -0.6518 · PC2 -0.9932PC1 -0.5161 · PC2 -0.8617PC1 -0.2846 · PC2 -0.8478PC1 -0.4458 · PC2 -0.9632PC1 -0.4612 · PC2 -0.7159PC1 -0.2874 · PC2 -1.198PC1 -0.1454 · PC2 -1.158PC1 0.4242 · PC2 -0.6281PC1 0.6206 · PC2 -0.123PC1 0.3496 · PC2 -0.6071PC1 0.07481 · PC2 -0.2744PC1 0.3317 · PC2 -0.1135PC1 0.9657 · PC2 -0.05017PC1 -0.2215 · PC2 -0.2772PC1 0.5887 · PC2 0.08298PC1 0.5033 · PC2 -0.351PC1 -0.02337 · PC2 -0.2705PC1 -0.3206 · PC2 0.2618PC1 0.1394 · PC2 0.1469PC1 0.6736 · PC2 -0.0402PC1 -0.07487 · PC2 -0.5636PC1 0.2099 · PC2 0.07468PC1 -0.974 · PC2 0.00167PC1 -1.488 · PC2 -0.1923PC1 -1.201 · PC2 -0.1901PC1 -0.731 · PC2 -0.5088PC1 -0.665 · PC2 -0.6726PC1 -0.9647 · PC2 -0.1836PC1 -1.85 · PC2 -0.2602PC1 -1.723 · PC2 -0.3754PC1 -1.451 · PC2 -0.1993PC1 -0.5668 · PC2 -0.6284PC1 -0.9469 · PC2 -0.3363PC1 -0.6597 · PC2 -0.8422PC1 -1.021 · PC2 -0.4901PC1 0.03949 · PC2 -0.8243PC1 -0.9316 · PC2 -0.7235PC1 -1.208 · PC2 -0.3811PC1 -1.207 · PC2 -0.1965PC1 -1.488 · PC2 -0.2053PC1 -1.852 · PC2 -0.06885PC1 -0.5944 · PC2 -0.7605PC1 0.5872 · PC2 -0.4484PC1 -0.8534 · PC2 -0.4662PC1 -1.321 · PC2 0.1644PC1 0.4033 · PC2 -0.1427PC1 -0.6514 · PC2 -0.4118PC1 -0.604 · PC2 -0.4457PC1 0.3482 · PC2 -0.4557PC1 0.4425 · PC2 -0.6171PC1 -0.2083 · PC2 -0.612PC1 2.198 · PC2 -0.04305PC1 0.1295 · PC2 -0.08055PC1 0.6769 · PC2 -0.3232PC1 2.01 · PC2 -0.445PC1 1.6 · PC2 -0.1984PC1 2.229 · PC2 0.1622PC1 2.13 · PC2 0.01669PC1 2.341 · PC2 -0.3052PC1 0.2471 · PC2 -0.09077PC1 1.763 · PC2 0.111PC1 1.982 · PC2 -0.2238PC1 0.6345 · PC2 -0.4512PC1 0.1615 · PC2 -0.03707PC1 0.4512 · PC2 0.2465PC1 0.4328 · PC2 -0.2944PC1 0.1396 · PC2 0.1847PC1 0.481 · PC2 0.07397PC1 -0.06659 · PC2 -0.2965PC1 -0.2849 · PC2 0.09936PC1 -0.1748 · PC2 0.2431PC1 -0.1384 · PC2 0.02024PC1 -0.5058 · PC2 -0.2082PC1 -0.9134 · PC2 0.2186PC1 -0.5839 · PC2 0.1998PC1 -0.3829 · PC2 -0.3876PC1 -1.29 · PC2 0.2297PC1 -1.143 · PC2 0.05033PC1 -0.221 · PC2 -0.395PC1 -0.315 · PC2 -0.4667PC1 -0.1431 · PC2 -0.5482PC1 -0.6189 · PC2 0.1003PC1 0.4022 · PC2 -0.09473PC1 0.02358 · PC2 0.06429PC1 0.7333 · PC2 -0.161PC1 1.422 · PC2 -0.1998PC1 0.1384 · PC2 0.07896PC1 -0.09819 · PC2 -0.0835PC1 0.9349 · PC2 -0.6747PC1 -0.5322 · PC2 0.11PC1 1.455 · PC2 -0.329PC1 1.095 · PC2 -0.4113PC1 -0.4266 · PC2 0.1626PC1 0.6106 · PC2 -0.5513PC1 -0.002102 · PC2 -0.4079PC1 -0.04669 · PC2 0.1363PC1 0.09712 · PC2 -0.2766PC1 -0.2842 · PC2 -0.01637PC1 0.5815 · PC2 -0.1591PC1 0.5758 · PC2 -0.8381PC1 0.7581 · PC2 -0.1909PC1 -0.5499 · PC2 -0.925PC1 0.2671 · PC2 -0.655PC1 -0.4616 · PC2 -0.8018PC1 -0.5398 · PC2 -0.4053PC1 -0.2041 · PC2 -0.3108PC1 -0.2145 · PC2 -1.246PC1 0.3993 · PC2 -0.5481PC1 -0.1085 · PC2 -0.9919PC1 0.2352 · PC2 -0.932PC1 0.2047 · PC2 -0.6232PC1 -0.4436 · PC2 0.4062PC1 -0.9835 · PC2 -0.5418PC1 2.046 · PC2 -0.07413PC1 1.665 · PC2 -0.1372PC1 -0.2861 · PC2 -0.3102PC1 -0.2452 · PC2 -0.716PC1 0.4885 · PC2 -0.6076PC1 -0.3241 · PC2 -0.759PC1 -0.3634 · PC2 -0.9812PC1 -0.7279 · PC2 -0.3879PC1 -1.098 · PC2 -0.07004PC1 -0.8847 · PC2 -0.7401PC1 0.7672 · PC2 -0.2485PC1 0.05315 · PC2 -0.2158PC1 0.2356 · PC2 -0.4897PC1 0.7571 · PC2 -0.5676PC1 -0.2728 · PC2 0.2676PC1 0.5378 · PC2 -0.341PC1 -0.763 · PC2 0.3494PC1 -0.5968 · PC2 0.4819PC1 2.961 · PC2 -0.08468PC1 4.567 · PC2 0.5014PC1 4.886 · PC2 -0.08674PC1 4.133 · PC2 0.3558PC1 3.861 · PC2 0.04871PC1 4.611 · PC2 0.3567PC1 4.81 · PC2 0.02927PC1 5.099 · PC2 0.1778PC1 3.953 · PC2 0.3643PC1 3.067 · PC2 0.349PC1 1.401 · PC2 -0.6548PC1 1.189 · PC2 -0.03739PC1 0.7405 · PC2 -0.5204PC1 0.6964 · PC2 -0.3694PC1 1.141 · PC2 -0.6701PC1 -1.101 · PC2 -0.03931PC1 -1.534 · PC2 -0.8827PC1 -1.469 · PC2 0.1713PC1 -1.894 · PC2 -0.2288PC1 -1.099 · PC2 0.008903PC1 -1.122 · PC2 -0.1766PC1 -0.1745 · PC2 -0.5684PC1 -1.087 · PC2 -0.2234PC1 -0.5079 · PC2 -0.6744PC1 -0.9198 · PC2 -0.4367PC1 0.581 · PC2 -0.1778PC1 1.263 · PC2 -0.4894PC1 0.4178 · PC2 0.03946PC1 0.7596 · PC2 -0.6537PC1 0.6852 · PC2 -0.2648PC1 -0.4909 · PC2 -0.9341PC1 0.1154 · PC2 -0.2667PC1 0.2563 · PC2 -0.1991PC1 -0.9614 · PC2 -0.3357PC1 -0.1049 · PC2 -0.4949PC1 0.1539 · PC2 -0.1133PC1 -0.701 · PC2 -0.8001PC1 1.105 · PC2 -0.6581PC1 -0.5465 · PC2 -1.151PC1 -0.5859 · PC2 -0.4958PC1 -0.8178 · PC2 0.01414PC1 -0.9663 · PC2 -1.296PC1 0.2397 · PC2 -1.384PC1 -0.03676 · PC2 -0.2854PC1 -0.5966 · PC2 -0.4645PC1 -0.9359 · PC2 0.01198PC1 -0.2927 · PC2 -0.9716PC1 -0.7696 · PC2 -0.4754PC1 -1.033 · PC2 -0.1589PC1 -1.58 · PC2 -0.6977PC1 4.643 · PC2 0.2179PC1 0.276 · PC2 -0.04521PC1 0.4544 · PC2 -0.3504PC1 1.381 · PC2 -0.6962PC1 3.089 · PC2 -0.00292PC1 -0.3177 · PC2 -0.1664PC1 -0.8325 · PC2 -0.1677PC1 0.2693 · PC2 -0.1883PC1 -0.0766 · PC2 -0.4548PC1 -0.191 · PC2 -0.3717PC1 0.03239 · PC2 -0.7102PC1 -0.0283 · PC2 -1.026PC1 -0.09946 · PC2 -0.785PC1 -0.3904 · PC2 -0.408PC1 0.002166 · PC2 -0.3235PC1 -0.5029 · PC2 -0.4378PC1 -0.3885 · PC2 -0.6341PC1 0.2741 · PC2 -0.3399PC1 -0.411 · PC2 -0.4768PC1 -0.2871 · PC2 -0.2521PC1 1.902 · PC2 0.04114PC1 0.2682 · PC2 -0.1438PC1 -0.5365 · PC2 -0.261PC1 0.1003 · PC2 0.1353PC1 -0.03275 · PC2 0.2392PC1 -0.3515 · PC2 0.2354PC1 0.3147 · PC2 0.2618PC1 0.5245 · PC2 0.1948PC1 0.1003 · PC2 0.3364PC1 1.414 · PC2 0.3212PC1 -1.332 · PC2 0.4391PC1 0.6918 · PC2 1.111PC1 1.481 · PC2 0.5444PC1 0.5017 · PC2 0.2748PC1 -0.5855 · PC2 0.2984PC1 0.07318 · PC2 2.044PC1 0.4547 · PC2 0.1287PC1 1.767 · PC2 0.437PC1 1.821 · PC2 0.0298PC1 -0.1117 · PC2 0.1153PC1 -0.463 · PC2 0.9399PC1 0.05599 · PC2 -0.2919PC1 -1.027 · PC2 0.3721PC1 1.161 · PC2 0.7998PC1 -0.374 · PC2 0.3738PC1 -0.328 · PC2 1.797PC1 1.243 · PC2 -0.2971PC1 -1.159 · PC2 0.11PC1 -0.4302 · PC2 0.2456PC1 -0.6598 · PC2 -0.05563PC1 -0.6153 · PC2 -0.1697PC1 -0.09639 · PC2 0.2007PC1 -0.1273 · PC2 -0.0275PC1 -1.687 · PC2 0.6205PC1 1.92 · PC2 0.2379PC1 0.008745 · PC2 2.045PC1 -0.1323 · PC2 0.771PC1 -0.5421 · PC2 2.219PC1 -1.434 · PC2 0.162PC1 -0.9088 · PC2 0.6462PC1 0.3872 · PC2 -0.2146PC1 -0.805 · PC2 0.2308PC1 -0.511 · PC2 0.3413PC1 -0.1222 · PC2 1.696PC1 -0.9063 · PC2 0.8257PC1 -1.334 · PC2 1.676PC1 -0.9811 · PC2 1.278PC1 -1.4 · PC2 1.359PC1 -1.382 · PC2 1.168PC1 -1.647 · PC2 1.249PC1 -1.634 · PC2 1.572PC1 -0.1334 · PC2 0.9846PC1 1.825 · PC2 0.4356PC1 1.411 · PC2 0.689PC1 -1.292 · PC2 0.3833PC1 -1.997 · PC2 0.7755PC1 0.8298 · PC2 0.8875PC1 -1.186 · PC2 0.0812PC1 2.793 · PC2 0.7634PC1 -1.742 · PC2 0.2829PC1 -1.708 · PC2 0.8794PC1 -1.518 · PC2 0.7355PC1 -1.133 · PC2 0.2049PC1 -0.71 · PC2 0.219PC1 -0.6536 · PC2 2.125PC1 2.024 · PC2 0.684PC1 0.7575 · PC2 0.8848PC1 -1.916 · PC2 0.1927PC1 -0.9951 · PC2 0.255PC1 1.461 · PC2 1.169PC1 1.387 · PC2 1.458PC1 -0.4472 · PC2 1.781PC1 0.6083 · PC2 -0.06843PC1 0.0683 · PC2 1.708PC1 0.4545 · PC2 1.071PC1 0.5165 · PC2 2.341PC1 0.9755 · PC2 2.281PC1 1.665 · PC2 0.9089PC1 3.218 · PC2 0.8872PC1 0.9697 · PC2 0.8912PC1 1.95 · PC2 0.863PC1 1.496 · PC2 0.6876PC1 0.09079 · PC2 2.025PC1 0.9521 · PC2 0.8305PC1 0.4006 · PC2 3.316PC1 0.4886 · PC2 2.095PC1 0.1926 · PC2 1.671PC1 -0.08407 · PC2 1.656PC1 0.5164 · PC2 1.307PC1 1.645 · PC2 0.7959PC1 1.521 · PC2 0.7574PC1 0.5166 · PC2 0.3899PC1 -0.3391 · PC2 -0.6668PC1 -0.4241 · PC2 -0.2264PC1 0.517 · PC2 -0.4788PC1 0.7665 · PC2 -0.3334PC1 -1.281 · PC2 0.1477PC1 -0.7097 · PC2 -0.0371PC1 0.03486 · PC2 -0.37PC1 -0.5923 · PC2 0.3239PC1 -0.4172 · PC2 -0.08083PC1 0.08223 · PC2 0.01895PC1 -0.05904 · PC2 -0.4348PC1 0.08848 · PC2 0.02319PC1 0.5617 · PC2 -0.312PC1 0.6948 · PC2 -0.1958PC1 0.6305 · PC2 -0.1513PC1 0.2414 · PC2 -0.6503PC1 0.6224 · PC2 -0.002479PC1 0.2393 · PC2 -0.311PC1 0.2531 · PC2 -0.471PC1 1.491 · PC2 0.09946PC1 0.2917 · PC2 0.1521PC1 1.407 · PC2 0.1252PC1 0.9008 · PC2 0.419PC1 0.9511 · PC2 0.253PC1 0.4496 · PC2 -0.4076PC1 0.7772 · PC2 -0.6878PC1 0.4511 · PC2 -0.06384PC1 0.05776 · PC2 0.2206PC1 1.166 · PC2 -0.3843PC1 -0.3408 · PC2 0.3467PC1 -0.4797 · PC2 -0.5225PC1 -1.01 · PC2 0.8472PC1 -0.595 · PC2 0.1338PC1 0.5357 · PC2 -0.02642PC1 0.6743 · PC2 -0.07717PC1 -0.1442 · PC2 -0.07256PC1 -0.02974 · PC2 0.3132PC1 -0.08116 · PC2 -0.6905PC1 0.2978 · PC2 0.09155PC1 0.2486 · PC2 -0.01473PC1 0.02573 · PC2 -0.4056PC1 1.162 · PC2 -0.2773PC1 0.7659 · PC2 -0.4206PC1 0.3316 · PC2 -0.1222PC1 0.5978 · PC2 -0.1047PC1 0.8919 · PC2 -0.02796PC1 0.2152 · PC2 -0.3442PC1 -0.4038 · PC2 0.09921PC1 0.1968 · PC2 0.2317PC1 0.35 · PC2 -0.1325PC1 0.2524 · PC2 0.2844PC1 0.8659 · PC2 -0.07474PC1 0.4035 · PC2 -0.2199PC1 0.491 · PC2 0.2762PC1 0.1338 · PC2 -0.09982PC1 0.6336 · PC2 0.6737PC1 -0.7051 · PC2 0.02442PC1 -0.6564 · PC2 0.084PC1 0.2328 · PC2 -0.1591PC1 -1.302 · PC2 -1.474PC1 -2.204 · PC2 -1.154PC1 -0.3575 · PC2 -1.215PC1 -0.01698 · PC2 -0.6145PC1 0.8749 · PC2 -0.4088PC1 0.1998 · PC2 -0.7107PC1 -1.207 · PC2 -1.102PC1 -0.2719 · PC2 0.03225PC1 1.783 · PC2 -0.02478PC1 2.881 · PC2 0.1528PC1 -1.402 · PC2 0.2723PC1 -1.553 · PC2 -0.274PC1 0.5306 · PC2 0.03496PC1 3.948 · PC2 -0.09309PC1 -0.5464 · PC2 -0.3913PC1 -1.674 · PC2 0.1344PC1 -0.3482 · PC2 -0.6621PC1 0.9736 · PC2 -0.2501PC1 -1.096 · PC2 -0.5701PC1 -0.9789 · PC2 0.2192PC1 -1.973 · PC2 0.01355PC1 -0.8455 · PC2 0.9271PC1 2.635 · PC2 1.551PC1 -2.66 · PC2 -0.2657PC1 0.9613 · PC2 -0.4806PC1 -1.18 · PC2 -0.1663PC1 -1.455 · PC2 -0.6265PC1 -0.1315 · PC2 -0.3522PC1 0.2601 · PC2 -0.5077PC1 2.378 · PC2 -0.3319PC1 1.724 · PC2 0.1407PC1 1.067 · PC2 0.04963PC1 -0.7975 · PC2 -0.2745PC1 1.483 · PC2 -0.7509PC1 3.301 · PC2 -0.1889PC1 3.249 · PC2 0.337PC1 0.3602 · PC2 -0.8946PC1 0.3919 · PC2 -0.5538PC1 -0.6531 · PC2 -0.4692PC1 2.963 · PC2 0.1065PC1 1.89 · PC2 -0.2037PC1 -1.405 · PC2 -0.6888PC1 -0.7169 · PC2 -0.633PC1 -0.03482 · PC2 -0.9143PC1 -0.04517 · PC2 -0.7814PC1 -1.991 · PC2 -0.07381PC1 -1.143 · PC2 0.1795PC1 -1.029 · PC2 -0.1216PC1 -0.4508 · PC2 -0.3754PC1 -0.04321 · PC2 1.33PC1 -0.8799 · PC2 0.441PC1 -0.05455 · PC2 -0.1576PC1 -0.7699 · PC2 -0.2342PC1 -0.5639 · PC2 0.1676PC1 0.2613 · PC2 2.423PC1 -0.6393 · PC2 0.7775PC1 1.509 · PC2 -0.1038PC1 2.68 · PC2 0.348PC1 2.476 · PC2 1.131PC1 -0.7227 · PC2 -0.1208PC1 0.9704 · PC2 -0.6417PC1 4.063 · PC2 0.01902PC1 0.8251 · PC2 -0.1616PC1 2.109 · PC2 0.1891PC1 1.582 · PC2 0.4978PC1 -0.7936 · PC2 -0.3077PC1 -0.5091 · PC2 1.007PC1 -0.08123 · PC2 1.839PC1 -0.4871 · PC2 1.021PC1 -0.3957 · PC2 0.8985PC1 1.779 · PC2 0.4145PC1 -1.316 · PC2 0.5434PC1 -1.136 · PC2 1.146PC1 -0.9422 · PC2 -0.07147PC1 -0.6727 · PC2 0.627PC1 -0.7174 · PC2 -0.04388PC1 -1.775 · PC2 0.72PC1 -2.033 · PC2 0.5521PC1 -1.732 · PC2 1.048PC1 -1.513 · PC2 1.013PC1 -0.7217 · PC2 1.88PC1 -1.185 · PC2 1.487PC1 -1.808 · PC2 1.465PC1 -0.8338 · PC2 1.274PC1 -1.791 · PC2 0.5132PC1 -2.355 · PC2 1.073PC1 0.5606 · PC2 1.18PC1 0.06424 · PC2 1.369PC1 -0.8696 · PC2 1.386PC1 -1.011 · PC2 1.047PC1 -1.391 · PC2 1.355PC1 -0.7633 · PC2 1.48PC1 -0.6298 · PC2 1.247PC1 -1.403 · PC2 0.6066PC1 -0.811 · PC2 0.7564PC1 -1.601 · PC2 0.4268PC1 -0.7736 · PC2 0.5184PC1 -0.3409 · PC2 1.263PC1 0.4829 · PC2 1.6PC1 0.7185 · PC2 1.881PC1 -0.2026 · PC2 1.608PC1 -0.5848 · PC2 1.604PC1 -0.5958 · PC2 0.7608PC1 -1.341 · PC2 0.9921PC1 -1.053 · PC2 1.049PC1 -0.5444 · PC2 1.642PC1 -1.405 · PC2 1.151PC1 -0.0648 · PC2 1.534PC1 -1.867 · PC2 1.184PC1 -0.153 · PC2 1.494PC1 -0.6796 · PC2 1.361PC1 0.3871 · PC2 1.507PC1 0.3332 · PC2 1.162PC1 0.8196 · PC2 1.72PC1 -0.6326 · PC2 1.563PC1 0.07878 · PC2 1.45PC1 -0.8705 · PC2 1.595PC1 0.859 · PC2 1.642PC1 0.271 · PC2 1.93PC1 -0.7078 · PC2 0.751PC1 -1.03 · PC2 1.052PC1 -0.3803 · PC2 1.679PC1 -0.7151 · PC2 1.323PC1 -0.9293 · PC2 1.045PC1 -0.6171 · PC2 0.5922PC1 -1.197 · PC2 0.9813PC1 -0.7789 · PC2 1.241PC1 -1.508 · PC2 0.9421PC1 -1.315 · PC2 0.7632PC1 -0.5654 · PC2 0.1266PC1 -0.8688 · PC2 0.587PC1 -1.568 · PC2 0.1194PC1 -0.1662 · PC2 0.7307PC1 -1.328 · PC2 0.6521PC1 0.07291 · PC2 -0.8262PC1 -0.6599 · PC2 -1.106PC1 0.5042 · PC2 -0.9565PC1 -0.1127 · PC2 -1.059PC1 0.3328 · PC2 -0.7812PC1 (56.0%)PC2 (22.2%)607 scores
PCA explained variance0%25%50%75%100%PC1: 56.0% (cumulative 56.0%)1PC2: 22.2% (cumulative 78.2%)2PC3: 12.8% (cumulative 91.0%)3PC4: 3.5% (cumulative 94.5%)4PC5: 2.2% (cumulative 96.7%)5PC6: 1.2% (cumulative 97.9%)6PC7: 0.7% (cumulative 98.6%)7PC8: 0.4% (cumulative 99.0%)8PC9: 0.3% (cumulative 99.3%)9PC10: 0.2% (cumulative 99.4%)10cumulative explained variancePC variancecumulativeprincipal component · cumulative (dashed)
X-Y spectral correlation 20
X · Discoloration spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation05001,0001,5002,0002,500|r|signed raxis · Pearson correlation scale
X · LMA spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation05001,0001,5002,0002,500|r|signed raxis · Pearson correlation scale
X · LDMC spectral correlation-1-0.500.51absolute correlation envelopesigned correlationabsolute correlation05001,0001,5002,0002,500|r|signed raxis · Pearson correlation scale
Targetmax |r|axis @ maxmean |r||r| ≥ .5
Discoloration0.3177240.09420.0%
LMA0.6786970.2096.7%
LDMC0.4981,4420.1960.0%
EWT0.7016980.1929.3%
EWT_rehydrated0.6586970.1674.2%
N0.6866420.2329.9%
C0.4252,3020.1330.0%
NDF0.4111,2160.20.0%
ADF0.4349730.2210.0%
ADL0.4874330.1870.0%
solubles0.4111,2160.20.0%
hemicellulose0.3551,3670.1420.0%
cellulose0.4871,3380.1890.0%
lignin0.5024330.1880.5%
chlA0.726430.20514.1%
chlB0.7166910.20714.5%
car0.6356430.1877.8%
Al0.4771,3200.1870.0%
Ca0.4792,2570.1690.0%
Cu0.351,4430.1360.0%

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 31

Species

target · categorical
Species classesPopulus tremuloides MichauxPopulus tremuloides Michaux: 100100Betula populifolia MarshallBetula populifolia Marshall: 8585Acer rubrum LinnaeusAcer rubrum Linnaeus: 7272Agonis flexuosa (Willd.) SweetAgonis flexuosa (Willd.) Sweet: 6868Acer saccharum MarshallAcer saccharum Marshall: 4040Quercus rubra LinnaeusQuercus rubra Linnaeus: 2626Fagus grandifolia EhrhartFagus grandifolia Ehrhart: 2525Betula papyrifera MarshallBetula papyrifera Marshall: 2121Populus grandidentata MichauxPopulus grandidentata Michaux: 2121Acer saccharinum LinnaeusAcer saccharinum Linnaeus: 2121+10 more+10 more: 6868
n / missing607 / 0
Classes66
Balance (entropy)0.71
Imbalance ratio100
Top classPopulus tremuloides Michaux (100)

LatinGenus

target · categorical
LatinGenus classesAcerAcer: 136136PopulusPopulus: 123123BetulaBetula: 107107AgonisAgonis: 6868QuercusQuercus: 2626FagusFagus: 2525SolidagoSolidago: 1313PhragmitesPhragmites: 1111CornusCornus: 1010RubusRubus: 99+10 more+10 more: 5050
n / missing607 / 0
Classes46
Balance (entropy)0.64
Imbalance ratio136
Top classAcer (136)

LatinSpecies

target · categorical
LatinSpecies classestremuloidestremuloides: 100100populifoliapopulifolia: 8585rubrumrubrum: 7272flexuosaflexuosa: 6868saccharumsaccharum: 4040rubrarubra: 2626grandifoliagrandifolia: 2525papyriferapapyrifera: 2121grandidentatagrandidentata: 2121saccharinumsaccharinum: 2121+10 more+10 more: 7171
n / missing607 / 0
Classes61
Balance (entropy)0.72
Imbalance ratio100
Top classtremuloides (100)

Discoloration

target · numeric
Discoloration distribution02004000 – 0.1667: 3900.1667 – 0.3333: 00.3333 – 0.5: 00.5 – 0.6667: 00.6667 – 0.8333: 00.8333 – 1: 01 – 1.167: 1461.167 – 1.333: 01.333 – 1.5: 01.5 – 1.667: 01.667 – 1.833: 01.833 – 2: 02 – 2.167: 502.167 – 2.333: 02.333 – 2.5: 02.5 – 2.667: 02.667 – 2.833: 02.833 – 3: 03 – 3.167: 163.167 – 3.333: 03.333 – 3.5: 03.5 – 3.667: 03.667 – 3.833: 03.833 – 4: 501234
n / missing607 / 0
Mean ± SD0.5173 ± 0.82
Median0
Range0 – 4
CV1.59
Skew / kurtosis1.7 / 3
Normal?no

GrowthForm

target · categorical
GrowthForm classestreetree: 498498herbherb: 5959shrubshrub: 4444vinevine: 66
n / missing607 / 0
Classes4
Balance (entropy)0.45
Imbalance ratio83
Top classtree (498)

LMA

target · numeric
LMA distribution0501001500.02339 – 0.0315: 70.0315 – 0.0396: 120.0396 – 0.04771: 330.04771 – 0.05582: 780.05582 – 0.06392: 1200.06392 – 0.07203: 1250.07203 – 0.08013: 720.08013 – 0.08824: 530.08824 – 0.09634: 240.09634 – 0.1044: 60.1044 – 0.1126: 30.1126 – 0.1207: 30.1207 – 0.1288: 10.1288 – 0.1369: 20.1369 – 0.145: 70.145 – 0.1531: 60.1531 – 0.1612: 60.1612 – 0.1693: 70.1693 – 0.1774: 100.1774 – 0.1855: 70.1855 – 0.1936: 90.1936 – 0.2017: 60.2017 – 0.2098: 20.2098 – 0.2179: 30.000.050.100.150.200.25
n / missing607 / 5
Mean ± SD0.07732 ± 0.0367
Median0.06706
Range0.02339 – 0.2179
CV0.474
Skew / kurtosis2 / 3.6
Normal?no

LDMC

target · numeric
LDMC distribution050100158.8 – 176: 3176 – 193.3: 3193.3 – 210.6: 3210.6 – 227.8: 6227.8 – 245.1: 3245.1 – 262.3: 3262.3 – 279.6: 6279.6 – 296.8: 10296.8 – 314.1: 17314.1 – 331.4: 19331.4 – 348.6: 23348.6 – 365.9: 24365.9 – 383.1: 60383.1 – 400.4: 84400.4 – 417.6: 79417.6 – 434.9: 72434.9 – 452.2: 68452.2 – 469.4: 42469.4 – 486.7: 21486.7 – 503.9: 32503.9 – 521.2: 13521.2 – 538.4: 6538.4 – 555.7: 2555.7 – 573: 11002005001,000
n / missing607 / 7
Mean ± SD403.1 ± 64.7
Median406.9
Range158.8 – 573
CV0.161
Skew / kurtosis-0.87 / 1.6
Normal?no

EWT

target · numeric
EWT distribution0501000.04433 – 0.05325: 80.05325 – 0.06217: 190.06217 – 0.0711: 590.0711 – 0.08002: 770.08002 – 0.08894: 780.08894 – 0.09786: 890.09786 – 0.1068: 720.1068 – 0.1157: 290.1157 – 0.1246: 100.1246 – 0.1336: 100.1336 – 0.1425: 100.1425 – 0.1514: 160.1514 – 0.1603: 170.1603 – 0.1692: 150.1692 – 0.1782: 70.1782 – 0.1871: 30.1871 – 0.196: 40.196 – 0.2049: 30.2049 – 0.2139: 20.2139 – 0.2228: 00.2228 – 0.2317: 10.2317 – 0.2406: 10.2406 – 0.2495: 10.2495 – 0.2585: 20.00.10.20.3
n / missing607 / 74
Mean ± SD0.0992 ± 0.0341
Median0.09184
Range0.04433 – 0.2585
CV0.344
Skew / kurtosis1.6 / 3
Normal?no

EWT_rehydrated

target · numeric
EWT_rehydrated distribution0501000.04878 – 0.05848: 60.05848 – 0.06818: 170.06818 – 0.07788: 460.07788 – 0.08758: 890.08758 – 0.09728: 850.09728 – 0.107: 930.107 – 0.1167: 930.1167 – 0.1264: 320.1264 – 0.1361: 200.1361 – 0.1458: 190.1458 – 0.1555: 200.1555 – 0.1652: 210.1652 – 0.1749: 200.1749 – 0.1846: 120.1846 – 0.1943: 60.1943 – 0.204: 70.204 – 0.2137: 40.2137 – 0.2234: 10.2234 – 0.2331: 30.2331 – 0.2428: 00.2428 – 0.2524: 10.2524 – 0.2621: 20.2621 – 0.2718: 00.2718 – 0.2815: 30.00.10.20.3
n / missing607 / 7
Mean ± SD0.1115 ± 0.0365
Median0.1045
Range0.04878 – 0.2815
CV0.327
Skew / kurtosis1.5 / 3
Normal?no

N

target · numeric
N distribution0501001500.8833 – 1.081: 141.081 – 1.278: 411.278 – 1.475: 281.475 – 1.672: 421.672 – 1.87: 601.87 – 2.067: 702.067 – 2.264: 1152.264 – 2.461: 882.461 – 2.659: 782.659 – 2.856: 372.856 – 3.053: 103.053 – 3.25: 53.25 – 3.448: 43.448 – 3.645: 43.645 – 3.842: 43.842 – 4.04: 34.04 – 4.237: 14.237 – 4.434: 04.434 – 4.631: 14.631 – 4.829: 04.829 – 5.026: 15.026 – 5.223: 05.223 – 5.42: 05.42 – 5.618: 10246
n / missing607 / 0
Mean ± SD2.118 ± 0.581
Median2.14
Range0.8833 – 5.618
CV0.275
Skew / kurtosis0.81 / 3.5
Normal?no

C

target · numeric
C distribution05010039.54 – 40.13: 140.13 – 40.71: 040.71 – 41.3: 141.3 – 41.88: 341.88 – 42.47: 442.47 – 43.05: 1143.05 – 43.64: 543.64 – 44.23: 1144.23 – 44.81: 944.81 – 45.4: 2445.4 – 45.98: 3345.98 – 46.57: 2546.57 – 47.16: 3347.16 – 47.74: 4647.74 – 48.33: 8848.33 – 48.91: 8748.91 – 49.5: 7449.5 – 50.09: 5550.09 – 50.67: 3550.67 – 51.26: 2251.26 – 51.84: 2551.84 – 52.43: 1152.43 – 53.01: 353.01 – 53.6: 1102050100
n / missing607 / 0
Mean ± SD48.13 ± 2.19
Median48.38
Range39.54 – 53.6
CV0.0455
Skew / kurtosis-0.65 / 0.67
Normal?no

NDF

target · numeric
NDF distribution05010015011.05 – 13.16: 313.16 – 15.26: 815.26 – 17.36: 1117.36 – 19.46: 1919.46 – 21.56: 3121.56 – 23.66: 3823.66 – 25.76: 6025.76 – 27.86: 8227.86 – 29.96: 10329.96 – 32.06: 7032.06 – 34.16: 3334.16 – 36.26: 3836.26 – 38.36: 2438.36 – 40.47: 1240.47 – 42.57: 1542.57 – 44.67: 1744.67 – 46.77: 1046.77 – 48.87: 248.87 – 50.97: 550.97 – 53.07: 753.07 – 55.17: 355.17 – 57.27: 357.27 – 59.37: 359.37 – 61.47: 2020406080
n / missing607 / 8
Mean ± SD29.98 ± 8.21
Median28.81
Range11.05 – 61.47
CV0.274
Skew / kurtosis1 / 1.7
Normal?no

ADF

target · numeric
ADF distribution0501007.972 – 9.084: 59.084 – 10.2: 710.2 – 11.31: 611.31 – 12.42: 812.42 – 13.53: 2113.53 – 14.64: 2014.64 – 15.76: 3515.76 – 16.87: 6216.87 – 17.98: 6517.98 – 19.09: 7919.09 – 20.2: 5620.2 – 21.32: 5021.32 – 22.43: 4922.43 – 23.54: 2423.54 – 24.65: 3124.65 – 25.76: 2325.76 – 26.88: 1826.88 – 27.99: 927.99 – 29.1: 1429.1 – 30.21: 630.21 – 31.32: 231.32 – 32.44: 032.44 – 33.55: 233.55 – 34.66: 3010203040
n / missing607 / 12
Mean ± SD19.43 ± 4.45
Median18.85
Range7.972 – 34.66
CV0.229
Skew / kurtosis0.36 / 0.4
Normal?no

ADL

target · numeric
ADL distribution02550751.145 – 2.006: 52.006 – 2.866: 172.866 – 3.727: 223.727 – 4.587: 154.587 – 5.448: 275.448 – 6.309: 386.309 – 7.169: 457.169 – 8.03: 688.03 – 8.89: 578.89 – 9.751: 599.751 – 10.61: 6010.61 – 11.47: 5211.47 – 12.33: 3612.33 – 13.19: 2913.19 – 14.05: 2314.05 – 14.91: 1314.91 – 15.78: 1115.78 – 16.64: 216.64 – 17.5: 617.5 – 18.36: 218.36 – 19.22: 419.22 – 20.08: 020.08 – 20.94: 320.94 – 21.8: 10102030
n / missing607 / 12
Mean ± SD9.038 ± 3.46
Median8.91
Range1.145 – 21.8
CV0.383
Skew / kurtosis0.37 / 0.48
Normal?no

solubles

target · numeric
solubles distribution05010015038.53 – 40.63: 240.63 – 42.73: 342.73 – 44.83: 344.83 – 46.93: 346.93 – 49.03: 749.03 – 51.13: 551.13 – 53.23: 253.23 – 55.33: 1055.33 – 57.43: 1757.43 – 59.53: 1559.53 – 61.64: 1261.64 – 63.74: 2463.74 – 65.84: 3865.84 – 67.94: 3367.94 – 70.04: 7070.04 – 72.14: 10372.14 – 74.24: 8274.24 – 76.34: 5976.34 – 78.44: 3978.44 – 80.54: 3180.54 – 82.64: 1982.64 – 84.74: 1184.74 – 86.84: 886.84 – 88.95: 3102050100
n / missing607 / 8
Mean ± SD70.03 ± 8.21
Median71.19
Range38.53 – 88.95
CV0.117
Skew / kurtosis-1 / 1.7
Normal?no

hemicellulose

target · numeric
hemicellulose distribution0501001502.773 – 4.078: 64.078 – 5.384: 135.384 – 6.689: 626.689 – 7.994: 1067.994 – 9.299: 1209.299 – 10.6: 7110.6 – 11.91: 6211.91 – 13.21: 5013.21 – 14.52: 1614.52 – 15.82: 1415.82 – 17.13: 1817.13 – 18.44: 1818.44 – 19.74: 519.74 – 21.05: 721.05 – 22.35: 722.35 – 23.66: 123.66 – 24.96: 024.96 – 26.27: 426.27 – 27.57: 627.57 – 28.88: 128.88 – 30.18: 330.18 – 31.49: 131.49 – 32.79: 132.79 – 34.1: 3010203040
n / missing607 / 12
Mean ± SD10.58 ± 4.89
Median9.175
Range2.773 – 34.1
CV0.462
Skew / kurtosis2 / 5
Normal?no

cellulose

target · numeric
cellulose distribution0501005.19 – 6.048: 66.048 – 6.905: 176.905 – 7.763: 577.763 – 8.62: 948.62 – 9.477: 909.477 – 10.33: 9310.33 – 11.19: 7711.19 – 12.05: 4612.05 – 12.91: 3812.91 – 13.76: 2213.76 – 14.62: 1514.62 – 15.48: 915.48 – 16.34: 616.34 – 17.19: 417.19 – 18.05: 218.05 – 18.91: 118.91 – 19.77: 019.77 – 20.62: 120.62 – 21.48: 021.48 – 22.34: 122.34 – 23.2: 423.2 – 24.05: 424.05 – 24.91: 624.91 – 25.77: 20102030
n / missing607 / 12
Mean ± SD10.4 ± 3.2
Median9.755
Range5.19 – 25.77
CV0.307
Skew / kurtosis2.3 / 7.3
Normal?no

lignin

target · numeric
lignin distribution02550750.918 – 1.775: 61.775 – 2.631: 152.631 – 3.488: 193.488 – 4.344: 184.344 – 5.201: 295.201 – 6.057: 366.057 – 6.914: 496.914 – 7.77: 737.77 – 8.626: 518.626 – 9.483: 639.483 – 10.34: 6010.34 – 11.2: 5511.2 – 12.05: 3312.05 – 12.91: 2712.91 – 13.77: 2313.77 – 14.62: 1214.62 – 15.48: 815.48 – 16.33: 216.33 – 17.19: 517.19 – 18.05: 318.05 – 18.9: 418.9 – 19.76: 019.76 – 20.62: 320.62 – 21.47: 10102030
n / missing607 / 12
Mean ± SD8.72 ± 3.42
Median8.644
Range0.918 – 21.47
CV0.392
Skew / kurtosis0.37 / 0.64
Normal?no

chlA

target · numeric
chlA distribution02550751.242 – 1.836: 101.836 – 2.43: 302.43 – 3.025: 233.025 – 3.619: 223.619 – 4.213: 324.213 – 4.807: 344.807 – 5.401: 505.401 – 5.996: 735.996 – 6.59: 676.59 – 7.184: 647.184 – 7.778: 547.778 – 8.373: 358.373 – 8.967: 308.967 – 9.561: 149.561 – 10.16: 710.16 – 10.75: 510.75 – 11.34: 011.34 – 11.94: 111.94 – 12.53: 312.53 – 13.13: 413.13 – 13.72: 113.72 – 14.31: 014.31 – 14.91: 014.91 – 15.5: 105101520
n / missing607 / 47
Mean ± SD5.991 ± 2.19
Median6.059
Range1.242 – 15.5
CV0.365
Skew / kurtosis0.27 / 0.76
Normal?no

chlB

target · numeric
chlB distribution02550750.486 – 0.6863: 160.6863 – 0.8866: 270.8866 – 1.087: 281.087 – 1.287: 241.287 – 1.487: 361.487 – 1.688: 401.688 – 1.888: 541.888 – 2.088: 672.088 – 2.289: 702.289 – 2.489: 622.489 – 2.689: 382.689 – 2.89: 392.89 – 3.09: 203.09 – 3.29: 143.29 – 3.49: 93.49 – 3.691: 33.691 – 3.891: 33.891 – 4.091: 04.091 – 4.292: 34.292 – 4.492: 24.492 – 4.692: 24.692 – 4.892: 24.892 – 5.093: 05.093 – 5.293: 10246
n / missing607 / 47
Mean ± SD2.035 ± 0.761
Median2.035
Range0.486 – 5.293
CV0.374
Skew / kurtosis0.49 / 1.1
Normal?no

car

target · numeric
car distribution0501000.193 – 0.312: 50.312 – 0.4309: 300.4309 – 0.5499: 290.5499 – 0.6688: 50.6688 – 0.7878: 130.7878 – 0.9067: 180.9067 – 1.026: 421.026 – 1.145: 511.145 – 1.264: 761.264 – 1.383: 711.383 – 1.501: 711.501 – 1.62: 531.62 – 1.739: 411.739 – 1.858: 231.858 – 1.977: 111.977 – 2.096: 82.096 – 2.215: 32.215 – 2.334: 32.334 – 2.453: 02.453 – 2.572: 62.572 – 2.691: 02.691 – 2.81: 02.81 – 2.929: 02.929 – 3.048: 101234
n / missing607 / 47
Mean ± SD1.251 ± 0.44
Median1.277
Range0.193 – 3.048
CV0.352
Skew / kurtosis-0.075 / 0.61
Normal?no

Al

target · numeric
Al distribution0501001500 – 0.01579: 600.01579 – 0.03158: 1220.03158 – 0.04738: 1330.04738 – 0.06317: 1130.06317 – 0.07896: 590.07896 – 0.09475: 390.09475 – 0.1105: 270.1105 – 0.1263: 120.1263 – 0.1421: 60.1421 – 0.1579: 60.1579 – 0.1737: 70.1737 – 0.1895: 80.1895 – 0.2053: 50.2053 – 0.2211: 10.2211 – 0.2369: 30.2369 – 0.2527: 00.2527 – 0.2685: 10.2685 – 0.2843: 00.2843 – 0.3: 10.3 – 0.3158: 10.3158 – 0.3316: 00.3316 – 0.3474: 10.3474 – 0.3632: 00.3632 – 0.379: 10.00.10.20.30.4
n / missing607 / 1
Mean ± SD0.05697 ± 0.0464
Median0.046
Range0 – 0.379
CV0.815
Skew / kurtosis2.5 / 9.5
Normal?no

Ca

target · numeric
Ca distribution0501001.639 – 3.093: 73.093 – 4.547: 214.547 – 6.001: 696.001 – 7.455: 687.455 – 8.91: 898.91 – 10.36: 7510.36 – 11.82: 7011.82 – 13.27: 4513.27 – 14.73: 2814.73 – 16.18: 2416.18 – 17.63: 2417.63 – 19.09: 1919.09 – 20.54: 2020.54 – 22: 1022 – 23.45: 723.45 – 24.9: 724.9 – 26.36: 526.36 – 27.81: 027.81 – 29.27: 529.27 – 30.72: 530.72 – 32.18: 332.18 – 33.63: 533.63 – 35.08: 035.08 – 36.54: 1010203040
n / missing607 / 0
Mean ± SD11.33 ± 6.05
Median9.929
Range1.639 – 36.54
CV0.534
Skew / kurtosis1.4 / 2.1
Normal?no

Cu

target · numeric
Cu distribution01002003000 – 0.002167: 890.002167 – 0.004333: 560.004333 – 0.0065: 1110.0065 – 0.008667: 840.008667 – 0.01083: 2010.01083 – 0.013: 200.013 – 0.01517: 110.01517 – 0.01733: 90.01733 – 0.0195: 30.0195 – 0.02167: 40.02167 – 0.02383: 10.02383 – 0.026: 50.026 – 0.02817: 30.02817 – 0.03033: 20.03033 – 0.0325: 10.0325 – 0.03467: 10.03467 – 0.03683: 10.03683 – 0.039: 00.039 – 0.04117: 00.04117 – 0.04333: 00.04333 – 0.0455: 10.0455 – 0.04767: 10.04767 – 0.04983: 10.04983 – 0.052: 10.000.020.040.06
n / missing607 / 1
Mean ± SD0.00784 ± 0.00591
Median0.008
Range0 – 0.052
CV0.754
Skew / kurtosis2.9 / 15
Normal?no

Fe

target · numeric
Fe distribution0501001500.01497 – 0.02543: 280.02543 – 0.03589: 390.03589 – 0.04635: 480.04635 – 0.05681: 790.05681 – 0.06727: 1030.06727 – 0.07773: 700.07773 – 0.08819: 600.08819 – 0.09865: 430.09865 – 0.1091: 420.1091 – 0.1196: 140.1196 – 0.13: 280.13 – 0.1405: 70.1405 – 0.1509: 110.1509 – 0.1614: 140.1614 – 0.1719: 20.1719 – 0.1823: 20.1823 – 0.1928: 20.1928 – 0.2032: 00.2032 – 0.2137: 50.2137 – 0.2242: 30.2242 – 0.2346: 30.2346 – 0.2451: 10.2451 – 0.2555: 00.2555 – 0.266: 20.00.10.20.3
n / missing607 / 1
Mean ± SD0.0763 ± 0.0402
Median0.069
Range0.01497 – 0.266
CV0.527
Skew / kurtosis1.5 / 3.4
Normal?no

K

target · numeric
K distribution01002001.058 – 2.604: 382.604 – 4.15: 694.15 – 5.695: 1455.695 – 7.241: 1597.241 – 8.787: 738.787 – 10.33: 4210.33 – 11.88: 2811.88 – 13.42: 713.42 – 14.97: 514.97 – 16.51: 1016.51 – 18.06: 1118.06 – 19.61: 519.61 – 21.15: 621.15 – 22.7: 022.7 – 24.24: 124.24 – 25.79: 125.79 – 27.33: 127.33 – 28.88: 028.88 – 30.43: 030.43 – 31.97: 131.97 – 33.52: 133.52 – 35.06: 235.06 – 36.61: 136.61 – 38.15: 1010203040
n / missing607 / 0
Mean ± SD7.167 ± 4.65
Median6.186
Range1.058 – 38.15
CV0.649
Skew / kurtosis2.9 / 13
Normal?no

Mg

target · numeric
Mg distribution0501001500.482 – 0.7895: 60.7895 – 1.097: 81.097 – 1.405: 291.405 – 1.712: 561.712 – 2.019: 912.019 – 2.327: 1012.327 – 2.635: 952.635 – 2.942: 732.942 – 3.25: 533.25 – 3.557: 303.557 – 3.864: 233.864 – 4.172: 124.172 – 4.479: 94.479 – 4.787: 54.787 – 5.095: 65.095 – 5.402: 25.402 – 5.71: 25.71 – 6.017: 16.017 – 6.325: 26.325 – 6.632: 06.632 – 6.939: 16.939 – 7.247: 07.247 – 7.554: 07.554 – 7.862: 202468
n / missing607 / 0
Mean ± SD2.495 ± 0.931
Median2.37
Range0.482 – 7.862
CV0.373
Skew / kurtosis1.5 / 4.8
Normal?no

Mn

target · numeric
Mn distribution01002000 – 0.04262: 1530.04262 – 0.08525: 1320.08525 – 0.1279: 800.1279 – 0.1705: 410.1705 – 0.2131: 370.2131 – 0.2557: 180.2557 – 0.2984: 220.2984 – 0.341: 240.341 – 0.3836: 160.3836 – 0.4262: 140.4262 – 0.4689: 150.4689 – 0.5115: 120.5115 – 0.5541: 100.5541 – 0.5967: 100.5967 – 0.6394: 30.6394 – 0.682: 10.682 – 0.7246: 30.7246 – 0.7672: 30.7672 – 0.8099: 40.8099 – 0.8525: 50.8525 – 0.8951: 00.8951 – 0.9377: 20.9377 – 0.9804: 10.9804 – 1.023: 10.00.51.01.5
n / missing607 / 0
Mean ± SD0.1721 ± 0.19
Median0.09
Range0 – 1.023
CV1.1
Skew / kurtosis1.8 / 3.1
Normal?no

Na

target · numeric
Na distribution0501001500 – 0.203: 840.203 – 0.406: 620.406 – 0.609: 850.609 – 0.812: 1100.812 – 1.015: 981.015 – 1.218: 611.218 – 1.421: 281.421 – 1.624: 181.624 – 1.827: 131.827 – 2.03: 132.03 – 2.233: 32.233 – 2.436: 92.436 – 2.639: 42.639 – 2.842: 52.842 – 3.045: 33.045 – 3.248: 23.248 – 3.451: 13.451 – 3.654: 03.654 – 3.857: 43.857 – 4.06: 14.06 – 4.263: 14.263 – 4.466: 14.466 – 4.669: 04.669 – 4.872: 10246
n / missing607 / 0
Mean ± SD0.8523 ± 0.696
Median0.74
Range0 – 4.872
CV0.817
Skew / kurtosis2 / 6.1
Normal?no

P

target · numeric
P distribution01002000.2228 – 0.5126: 210.5126 – 0.8024: 90.8024 – 1.092: 651.092 – 1.382: 1681.382 – 1.672: 1441.672 – 1.962: 561.962 – 2.251: 272.251 – 2.541: 272.541 – 2.831: 202.831 – 3.121: 143.121 – 3.411: 73.411 – 3.7: 143.7 – 3.99: 63.99 – 4.28: 94.28 – 4.57: 14.57 – 4.86: 54.86 – 5.149: 35.149 – 5.439: 15.439 – 5.729: 35.729 – 6.019: 26.019 – 6.309: 26.309 – 6.598: 16.598 – 6.888: 06.888 – 7.178: 202468
n / missing607 / 0
Mean ± SD1.758 ± 1.02
Median1.458
Range0.2228 – 7.178
CV0.578
Skew / kurtosis2.2 / 6.1
Normal?no

Zn

target · numeric
Zn distribution01002003000.001424 – 0.02832: 2790.02832 – 0.05522: 900.05522 – 0.08212: 340.08212 – 0.109: 400.109 – 0.1359: 530.1359 – 0.1628: 370.1628 – 0.1897: 260.1897 – 0.2166: 140.2166 – 0.2435: 130.2435 – 0.2704: 60.2704 – 0.2973: 60.2973 – 0.3242: 20.3242 – 0.3511: 20.3511 – 0.378: 10.378 – 0.4049: 10.4049 – 0.4318: 00.4318 – 0.4587: 00.4587 – 0.4856: 00.4856 – 0.5125: 00.5125 – 0.5394: 00.5394 – 0.5663: 20.5663 – 0.5932: 00.5932 – 0.6201: 00.6201 – 0.647: 10.00.20.40.60.8
n / missing607 / 0
Mean ± SD0.07022 ± 0.0817
Median0.03
Range0.001424 – 0.647
CV1.16
Skew / kurtosis2.2 / 8.2
Normal?no

Metadata 3

latitude

metadata · numeric
latitude distribution0200400600-34.81 – -31.44: 68-31.44 – -28.08: 0-28.08 – -24.71: 0-24.71 – -21.34: 0-21.34 – -17.98: 0-17.98 – -14.61: 0-14.61 – -11.24: 0-11.24 – -7.876: 0-7.876 – -4.509: 0-4.509 – -1.143: 0-1.143 – 2.224: 02.224 – 5.59: 05.59 – 8.957: 08.957 – 12.32: 012.32 – 15.69: 015.69 – 19.06: 019.06 – 22.42: 022.42 – 25.79: 025.79 – 29.16: 029.16 – 32.52: 032.52 – 35.89: 035.89 – 39.26: 039.26 – 42.62: 042.62 – 45.99: 539-50-2502550
n / missing607 / 0
Mean ± SD36.61 ± 25.3
Median45.55
Range-34.81 – 45.99
CV0.692
Skew / kurtosis-2.5 / 4.1
Normal?no

longitude

metadata · numeric
longitude distribution0200400600-75.52 – -67.54: 539-67.54 – -59.55: 0-59.55 – -51.57: 0-51.57 – -43.59: 0-43.59 – -35.61: 0-35.61 – -27.62: 0-27.62 – -19.64: 0-19.64 – -11.66: 0-11.66 – -3.674: 0-3.674 – 4.309: 04.309 – 12.29: 012.29 – 20.27: 020.27 – 28.26: 028.26 – 36.24: 036.24 – 44.22: 044.22 – 52.21: 052.21 – 60.19: 060.19 – 68.17: 068.17 – 76.16: 076.16 – 84.14: 084.14 – 92.12: 092.12 – 100.1: 0100.1 – 108.1: 0108.1 – 116.1: 68-100-50050100150
n / missing607 / 0
Mean ± SD-52.58 ± 59.9
Median-73.47
Range-75.52 – 116.1
CV1.14
Skew / kurtosis2.5 / 4.1
Normal?no

species

metadata · categorical
species classesPopulus tremuloides MichauxPopulus tremuloides Michaux: 100100Betula populifolia MarshallBetula populifolia Marshall: 8585Acer rubrum LinnaeusAcer rubrum Linnaeus: 7272Agonis flexuosa (Willd.) SweetAgonis flexuosa (Willd.) Sweet: 6868Acer saccharum MarshallAcer saccharum Marshall: 4040Quercus rubra LinnaeusQuercus rubra Linnaeus: 2626Fagus grandifolia EhrhartFagus grandifolia Ehrhart: 2525Betula papyrifera MarshallBetula papyrifera Marshall: 2121Populus grandidentata MichauxPopulus grandidentata Michaux: 2121Acer saccharinum LinnaeusAcer saccharinum Linnaeus: 2121+10 more+10 more: 6868
n / missing607 / 0
Classes66
Balance (entropy)0.71
Imbalance ratio100
Top classPopulus tremuloides Michaux (100)
Constant metadata 18
  • ecosis_resource_id2dcfc28e-d7f5-432b-a469-5d71e37384d8
  • coordinate_precision_notessource-provided coordinates when available
  • year2,022
  • plant_partLeaf
  • canopy_or_leafleaf
  • instrumentSpectral Evolution PSR+ 3500
  • acquisition_modeContact
  • signal_typereflectance
  • axis_unitnm
  • axis_min400
  • axis_max2,400
  • n_points_original2,001
  • publication_doi10.1111/2041-210X.13958 | 10.21232/DCpLBYke | 10.21232/dcplbyke | 10.21232/vj7AGhTC
  • citationShan Kothari, Rosalie Beauchamp-Rioux, Etienne Laliberté and Jeannine Cavender-Bares. 2022. Ground-leaf CABO spectra from herbarium project v2. Data set. Available on-line [http://ecosis.org] from the Ecological Spectral Information System (EcoSIS). 10.21232/vj7AGhTC
  • licenseCreative Commons Attribution Share-Alike
  • rights_statusexplicit_open
  • usage_scopepublic_reuse_possible
  • notesEcoSIS package ground-leaf-cabo-spectra-from-herbarium-project-v2, no interpolation applied by project.

6 variable(s) omitted (no recorded values).

Alignment

Alignment levelobservation
Sample id availableyes
Samples607
Observations (total)607
Reps per samplemin 1 · mean 1 · max 1

Provenance & citation

ContributorGround-leaf CABO spectra from herbarium project v2
Origin · url [open]https://data.ecosis.org/dataset/ground-leaf-cabo-spectra-from-herbarium-project-v2
Origin · script [manual]source_to_standard.py — standardization script (maintainer-only)
Publication10.1111/2041-210X.13958 — Reflectance spectroscopy allows rapid, accurate, and non-destructive estimates of functional traits from pressed leaves
Publication10.21232/vj7AGhTC — Ground-leaf CABO spectra from herbarium project v2
Publication10.21232/DCpLBYke
Publication10.21232/dcplbyke

Governance & integrity

Tierpublic
LicenseCC-BY-SA-4.0
Permitted useResearch and benchmarking.
Access policyOpen per source license.
RedistributionEcoSIS CKAN metadata exposes an open license.
Content version1.0.0
Schema / protocol2.0
Content hash3448ae9748fb734d…
Processing hash72c40315dbbc6c45…
Metadata hashea8a98b9ac43dfb3…

Load this dataset

# pip install nirs4all-datasets
from nirs4all_datasets import get

ds = get("ecosis_ground_leaf_cabo_spectra_from_herbarium_project_v2_reflectance_nirs")            # DOI-pinned, checksum-verified, cached
X, y = ds.x(), ds.y()
print(X.shape, y.shape)
card.jsoncroissant.jsonIdentity metadata only — the dataset bytes live at the origin / DOI.