01997naa a2200337 a 450000100080000000500110000800800410001902400510006010000160011124501220012726000090024950000180025852009960027665000250127265000200129765000210131765000230133865300190136165300230138065300270140365300240143070000190145470000170147370000140149070000140150470000240151870000280154270000180157070000160158877300550160421320072026-02-11       2021    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1016/j.cdc.2021.1007192DOI1 aMELO, T. O.  aUnivariate statistical analysis of gaschromatographybmass spectrometry fingerprints analyses.h[electronic resource]  c2021  aDate article.  aGas Chromatography - Mass Spectrometry (GC–MS) has been used for a long time in fingerprint analysis. We present a workflow of univariate statistical treatment of compound by considering their type of response variables. Two data sources were used: (i) comparative data from two Brazilian Amazon soils, and (ii) the Nitrogen-dose response experiment involving two Ilex paraguariensis clones. During type of response variables selection, the following assumptions were tested: normality and homogeneity of variances. After defining a strategy to select the type of response variables, the compounds were classified according to the statistical test that must be used to evaluate them: analysis of variance (ANOVA, LM), generalized linear model (GLM), and a non-parametric (NP) test. The developed workflow allows individual compound and class comparisons, and a couple examples that illustrate a wider range of similar datasets are open to the readers to test either their own data or ours.  aAnalytical chemistry  aBiogeochemistry  aPlant physiology  aFisiologia Vegetal  aBiogeoquímica  aBiological markers  aMarcadores biológicos  aQuímica analítica1 aFRANCISCON, L.1 aBROWN, G. G.1 aKOPKA, J.1 aCUNHA, L.1 aMARTINEZ-SEIDEL, F.1 aMADUREIRA, L. A. dos S.1 aHANSEL, F. A.1 aTPI Network  tChemical Date Collectionsgv. 33, Id 100719, 2021.