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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 a* and V and energy of b* from imaees 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 L* 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-acccptable chips. (c) 2007 Elsevier Ltd. All rights reserved. (Less)

An important goal for characterization of texture appearance is the quantification of spatial patterns. The objective was to investigate the potential usefulness of two fractal metrics based on fast Fourier transform and gliding box lacunarity as descriptors of visual texture in ham slices. Images were acquired from three qualities of sliced pork ham, typically consumed in Ireland (200 slices/quality). Unexpected characteristics in textural pattern were revealed; the values of fractal dimension were larger for the smoothest surface. Alternatively, the decreasing trend of the power spectrum intercept towards the smoother premium quality ham showed that it correlates well with the overall magnitude of visual roughness. The results of lacunarity suggest that it has a discriminating power among the three ham qualities and its behaviour resembles the one of an exponential decay function. Results showed that Fourier analysis dimension, power spectrum intercept and lacunarity are important fractal parameters and useful quantitative descriptors that capture information embedded in the spatial structure of the underlying image texture of hams.

Blooming or the migration of fat to the surface of chocolate results in color changes and development of non-uniform color patterns. These phenomena were assessed during storage of milk chocolate tablets (cycling temp. between 16 and 28 °C for 52 days) by a computer vision system and image analysis. Eight features were extracted from images (L*, a* and b* values, whiteness index, chroma, hue, % bloom and energy of Fourier). Major changes occurred after day 36 of storage, coincidental with visual perception. Initially, white specks emerged on the brown background but were superseded by the development of a whitish color extending over most of the surface. L*, whiteness index, a* and chroma correlated well with values taken with a commercial colorimeter (R2 > 0.70). Changes in image texture (energy of Fourier) followed a similar trend as color changes. The sequential forward selection strategy allowed correct classification of 97.8% of samples into four classes with only five features. The computer vision system has the capability to quantify overall changes as well as particular features over the whole chocolate surface thus enabling customization and standardization for quality assessment.