TY - JOUR

T1 - Application of multivariate data analysis for food quality investigations

T2 - An example-based review

AU - Buvé, Carolien

AU - Saeys, Wouter

AU - Rasmussen, Morten Arendt

AU - Neckebroeck, Bram

AU - Hendrickx, Marc

AU - Grauwet, Tara

AU - Van Loey, Ann

N1 - Funding Information: This work was supported by the Research Foundation Flanders (FWO) [Project G.OA76.15N ]. Bram Neckebroeck has a doctoral scholarship of the Research Foundation Flanders (FWO) [grant 1S14717N ]. Publisher Copyright: © 2021 Elsevier Ltd

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Chemometrics

KW - Example-based review

KW - Food quality assessment

KW - Large data sets

KW - Multivariate data analysis

KW - Omics approaches

KW - Quality changes

U2 - 10.1016/j.foodres.2021.110878

DO - 10.1016/j.foodres.2021.110878

M3 - Review

C2 - 34980408

AN - SCOPUS:85121269472

VL - 151

JO - Food Research International

JF - Food Research International

SN - 0963-9969

M1 - 110878

ER -