Application of multivariate data analysis for food quality investigations: An example-based review. (January 2022)

• Record Type: Journal Article
• Authors: Buvé, Carolien; Saeys, Wouter; Rasmussen, Morten Arendt; Neckebroeck, Bram; Hendrickx, Marc; Grauwet, Tara; Van Loey, Ann
• Is Part Of: Food research international. Volume 151(2022)
• Publication Date: 2022-01
• Subjects: Example-based review -- Food quality assessment -- Multivariate data analysis -- Large data sets -- Quality changes -- Chemometrics -- Omics approaches; ANOVA Analysis of Variance -- ASCA ANOVA Simultaneous Component Analysis -- GC-MS Gas Chromatography – Mass Spectrometry -- LC-TOF-MS Liquid Chromatography - Time of Flight – Mass Spectrometry -- LV(s) Latent Variable(s) -- MANOVA Multivariate Analysis of Variance -- MVDA Multivariate data analysis -- PARAFAC Parallel Factor Analysis -- PC(s) Principal Component(s) -- PCA Principal Component Analysis -- PLS Partial Least Squares Regression -- PLS-DA Partial Least Squares-Discriminant Analysis -- RMSECV Root Mean Squared Error of Cross Validation -- SCA Simultaneous Component Analysis -- VID Variable IDentification -- VIP Variable Importance in Projection; Food -- Analysis -- Periodicals; Food industry and trade -- Periodicals; Food industry and trade -- Canada -- Periodicals; Food Technology -- Periodicals; Food -- Periodicals; Food-Processing Industry -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Industrie et commerce -- Canada -- Périodiques; Aliments -- Recherche -- Périodiques; Food industry and trade; Canada; Periodicals; Electronic journals; 664.005
• DOI: 10.1016/j.foodres.2021.110878
• Languages: English
• ISSNs: 0963-9969

Abstract

Graphical abstract: Highlights: The principles of four methods for food quality analysis are discussed. Different MVDA methods are illustrated on a common data set as an example-based review. The most suitable MVDA method depends on the nature of the data set and the objective of the research. The potential of more advanced multivariate methods should be further explored. Abstract: These days, large multivariate data sets are common in the food research area. This is not surprising as food quality, which is important for consumers, and its changes are the result of a complex interplay of multiple compounds and reactions. In order to comprehensively extract information from these data sets, proper data analysis tools should be applied. The application of multivariate data analysis (MVDA) is therefore highly recommended. However, at present the use of MVDA for food quality investigations is not yet fully explored. This paper focusses on a number of MVDA methods (PCA (Principal Component Analysis), PLS (Partial Least Squares Regression), PARAFAC (Parallel Factor Analysis) and ASCA (ANOVA Simultaneous Component Analysis)) useful for food quality investigations. The terminology, main steps and the theoretical basis of each method will be explained. As this is an example-based review, each method was applied on the same experimental data set to give the reader an idea about each selected MVDA method and to make a comparison between the outcomes. Numerous MVDA methods are available in literature. Which method to select depends on the data set and objective. PCA should be the first choice for data exploration of two-dimensional data. For predictive purposes, PLS is the most appropriate method. Given an underlying experimental design, ASCA takes into account both the relation between the different variables and the design factors. In case of a multi-way data set, PARAFAC can be used for data exploration. While these methods have already proven their value in research, there is a need to further explore their potential to investigate the complex interplay of compounds and reactions contributing to food quality. With this work we would like to encourage food scientists with no or limited knowledge of MVDA to get some first insights into the selected methods.