Classification of Kashar Cheeses Based on Their Chemical, Color and Instrumental Textural Characteristics Using Principal Component and Hierarchical Cluster Analysis

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Eroglu A., DOĞAN M., TOKER Ö. S., YILMAZ M. T.

INTERNATIONAL JOURNAL OF FOOD PROPERTIES, vol.18, no.4, pp.909-921, 2015 (SCI-Expanded, Scopus)

Abstract

Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied to investigate physicochemical and instrumental textural properties of fresh kashar cheese. Four different PCs sufficiently explained the variability in the cheese samples. In addition, hierarchical cluster analysis (HCA) was performed to group the kashar cheese samples regarding physicochemical and instrumental textural properties. Instrumental textural properties indicated greater variability than chemical composition of cheese samples. PCA revealed that color parameters were positively correlated with textural and chemical parameters. The results of this study revealed that other parameters rather than chemical composition would be effective on the instrumental textural properties. It was proved that PCA was a very effective statistical tool to determine quality of cheese samples. According to the PCA and HCA results, the attributes defining the kashar cheese samples were determined to be primarily the TPA parameters. 

Principal component analysis and hierarchical cluster analysis were applied to investigate physicochemical and instrumental textural properties of fresh kashar cheese. Four different principal components sufficiently explained the variability in the cheese samples. In addition, hierarchical cluster analysis was performed to group the kashar cheese samples regarding physicochemical and instrumental textural properties. Instrumental textural properties indicated greater variability than chemical composition of cheese samples. Principal component analysis revealed that color parameters were positively correlated with textural and chemical parameters. The results of this study revealed that other parameters rather than chemical composition would be effective on the instrumental textural properties. It was proved that principal component analysis was a very effective statistical tool to determine quality of cheese samples. According to the principal component analysis and hierarchical cluster analysis results, the attributes defining the kashar cheese samples were determined to be primarily the texture profile analysis parameters.