Data evaluation for soft drink quality control using principal component analysis and back-propagation neural networks

Abstract

This work describes an alternative for chemical data research, with the aim of evaluating finished product quality. Analytical data for additives in soft drinks are interpreted by the use of multivariate data analysis: principal component analysis (PCA), factor analysis, cluster analysis, and artificial neural networks. Taking into account various chemical components like sorbic, benzoic, and ascorbic acids; saccharose; caffeine; Na, K, Ca, Mg, Fe, Zn, Cu, P, and B, soft drinks were characterized and classified. The ratios of Na, K, Ca + Mg, P, and K/Na have been studied. The application of PCA, cluster analysis, and artificial neural networks showed that combination of these chemometric tools offers effective means for modeling and classifying soft drinks in accordance with their contents in additives and heavy metals.