Application of Image Analysis Combined with Computational Expert Approaches for Shrimp Freshness Evaluation

This study was aimed to evaluate the freshness and quality of cultured shrimp (litopenaeus vannamei) during 9 days of storage on ice (i.e., at a temperature of 0°C) using image processing technique. A lighting chamber was used to provide uniform conditions for illumination. The shrimp freshness was evaluated using computer vision technique through color changes of head, legs and tail of the harvested shrimps. Thirty-six color parameters of the images such as mean and variance of red (r), green (g), blue (b), lightness hue (h), saturation (s), value (v), luma information (i and y), the luma component (y), chroma component (cr), lightness (L*), redness (a*), yellowness (b*), chroma (c), and hue (h) were analyzed. Some parameters, such as b*, from side pictures and r mean, b variance, v mean, y mean, b* mean and (L*) mean from top pictures changed with a rather similar trend during the storage period. Different computational expert approaches such as linear discriminant analysis, quadratic discriminant analysis, K nearest neighbors, and discriminant partial least squares regression were examined for shrimp freshness classification. For this, all the variables and the subsets of variables were selected by means of stepwise linear discriminant analysis, stepwise orthogonalization, classification and regression trees. The shrimp freshness was characterized with a high classification accuracy of 90%. Freshness evaluation using image processing is proposed as a potential technique to the food industry.