Chapter 12: Chemometrics in Antioxidant Activity Measurements

In antioxidant studies we are deluged by an incomprehensible exponential growth in literature and data: in the metabolomics era, quantitative big data are the new reality. How can the maximum value be extracted from this data? Chemometrics offers new means of sorting out masses of reactions and products in complex systems. This chapter examines the reality of big data and explores the fundamentals of chemometrics as they apply to antioxidants. It examines both aspects of chemometrics; what it is and equally as important what it is not. Good quality data is essential to meaningful data-driven modeling and this relates back to Chapter 2 and the need for quality sampling. This chapter will show how to use real data to obtain useful answers and will include examples of how chemometrics has provided answers about antioxidant action. Exploratory multivariate methods, namely principal component analysis, PCA, and hierarchical cluster analysis, HCA, are explained in terms of usability and drawbacks. Partial least squares regression is also described using published data. Similarly, supervised multivariate techniques, namely linear and quadratic discriminant analysis, LDA/QDA, partial least squares-discriminant analysis, PLS-DA, and soft independent modeling of class analogy, SIMCA, are explained, exemplified with real life cases.