Literature DB >> 28189260

Multivariate analysis of variance of designed chromatographic data. A case study involving fermentation of rooibos tea.

Federico Marini1, Dalene de Beer2, Nico A Walters2, André de Villiers3, Elizabeth Joubert2, Beata Walczak4.   

Abstract

An ultimate goal of investigations of rooibos plant material subjected to different stages of fermentation is to identify the chemical changes taking place in the phenolic composition, using an untargeted approach and chromatographic fingerprints. Realization of this goal requires, among others, identification of the main components of the plant material involved in chemical reactions during the fermentation process. Quantitative chromatographic data for the compounds for extracts of green, semi-fermented and fermented rooibos form the basis of preliminary study following a targeted approach. The aim is to estimate whether treatment has a significant effect based on all quantified compounds and to identify the compounds, which contribute significantly to it. Analysis of variance is performed using modern multivariate methods such as ANOVA-Simultaneous Component Analysis, ANOVA - Target Projection and regularized MANOVA. This study is the first one in which all three approaches are compared and evaluated. For the data studied, all tree methods reveal the same significance of the fermentation effect on the extract compositions, but they lead to its different interpretation.
Copyright © 2017 Elsevier B.V. All rights reserved.

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Keywords:  ANOVA simultaneous component analysis (ASCA); ANOVA-target projection (ANOVA-TP); Regularized MANOVA (rMANOVA); Rooibos tea

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Year:  2017        PMID: 28189260     DOI: 10.1016/j.chroma.2017.02.007

Source DB:  PubMed          Journal:  J Chromatogr A        ISSN: 0021-9673            Impact factor:   4.759


  2 in total

1.  Antioxidant and Cytoprotective Effects of Tibetan Tea and Its Phenolic Components.

Authors:  Hong Xie; Xican Li; Zhenxing Ren; Weimin Qiu; Jianlan Chen; Qian Jiang; Ban Chen; Dongfeng Chen
Journal:  Molecules       Date:  2018-01-24       Impact factor: 4.411

2.  Application of Sebum Lipidomics to Biomarkers Discovery in Neurodegenerative Diseases.

Authors:  Stefania Briganti; Mauro Truglio; Antonella Angiolillo; Salvatore Lombardo; Deborah Leccese; Emanuela Camera; Mauro Picardo; Alfonso Di Costanzo
Journal:  Metabolites       Date:  2021-11-29
  2 in total

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