Univariate statistical analysis of gas chromatography – mass spectrometry fingerprints analyses

Tamires Oliveira Melo; Luziane Franciscon; George Brown; Joachim Kopka; Luis Cunha; Federico Martinez-Seidel; Luiz Augusto Dos Santos Madureira; Fabricio Augusto Hansel; null TPI Network

doi:10.1016/j.cdc.2021.100719

Univariate statistical analysis of gas chromatography – mass spectrometry fingerprints analyses

Tamires Oliveira Melo, Luziane Franciscon, George Brown, Joachim Kopka, Luis Cunha, Federico Martinez-Seidel, Luiz Augusto Dos Santos Madureira, Fabricio Augusto Hansel, TPI Network

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Abstract

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.

Original language	English
Article number	100719
Pages (from-to)	100719
Journal	Chemical Data Collections
Volume	33
Issue number	00
DOIs	https://doi.org/10.1016/j.cdc.2021.100719
Publication status	Published - 8 May 2021

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1-s2.0-S2405830021000732-mainAccepted author manuscript, 1.38 MBLicence: Unspecified

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title = "Univariate statistical analysis of gas chromatography – mass spectrometry fingerprints analyses",

abstract = "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.",

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doi = "10.1016/j.cdc.2021.100719",

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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.

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JF - Chemical Data Collections

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Univariate statistical analysis of gas chromatography – mass spectrometry fingerprints analyses

Abstract

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