Assessing the use of different chemometric techniques to discriminate low-fat and full-fat yogurts

Abstract

This study proposes a new approach to discriminate low and full-fat yogurts using instrumental analysis and chemometric techniques. One hundred twenty six strawberry flavored yogurts were subjected to instrumental analysis of pH, color and firmness. Exploratory methods, such as Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), and supervised classification methods, such as K-nearest neighbors (KNN), soft independent modeling of class analogy (SIMCA), and Partial Least Square Discriminant Analysis (PLSDA) were used for assessing the data. The results showed that low- and full-fat yogurts presented different with regard to all the variables analyzed. It was not possible to obtain total separation between the samples using PCA and HCA. KMN and PLSDA presented excellent performance toward the full-fat category, with 100% correct prediction which suggests only low-fat yogurts to be subjected to the traditional fat content determination methods. This approach can be incentivized by the health agencies aimed to optimize materials and financial resources.