Colour and image texture analysis in classification of commercial potato chips

The images of commercial potato chips were evaluated for various colour and textural features to characterize and classify the appearance and to model the quality preferences of a group of consumers. Features derived from the image texture contained better information than colour features to discriminate both the quality categories of chips and consumers’ preferences. Entropy of a¹ and V and energy of b¹ from images of the total chip surface, average and variance of H and correlation of V from the images of spots and/or defects (if they are present), and average of L¹ from clean images (chips free of spots and/or defects) showed the best correspondence with the four proposed appearance quality groups (A: ‘pale chips’, B: ‘slightly dark chips’, C: ‘chips with brown spots’, and D: ‘chips with natural defects’), giving classification rates of 95.8% for training data and 90% for validation data when linear discriminant analysis (LDA) was used as a selection criterion. The inclusion of independent colour and textural features from images of brown spots and/or defects and their clean regions of chips improved the resolution of the classification model and in particular to predict ‘chips with natural defects’. Consumers’ preferences showed that in spite of the ‘moderate’ agreement among raters (Kappa-value = 0.51), textural features have potential to model consumer behaviour in the respect of visual preferences of potato chips. A stepwise logistic regression model was able to explain 86.2% of the preferences variability when classified into acceptable and non-acceptable chips.