TY - JOUR
T1 - Univariate statistical analysis of gas chromatography – mass spectrometry fingerprints analyses
AU - Melo, Tamires Oliveira
AU - Franciscon, Luziane
AU - Brown, George
AU - Kopka, Joachim
AU - Cunha, Luis
AU - Martinez-Seidel, Federico
AU - Madureira, Luiz Augusto Dos Santos
AU - Hansel, Fabricio Augusto
AU - TPI Network, null
PY - 2021/5/8
Y1 - 2021/5/8
N2 - Gas 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.
AB - Gas 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.
U2 - 10.1016/j.cdc.2021.100719
DO - 10.1016/j.cdc.2021.100719
M3 - Article
SN - 2405-8300
VL - 33
SP - 100719
JO - Chemical Data Collections
JF - Chemical Data Collections
IS - 00
M1 - 100719
ER -