Computer vision systems (CVS) for moisture content estimation in dehydrated shrimp

Abstract

This paper presents a method based on computer vision systems (CVS) to estimate shrimp dehydration level by analyzing color during drying process. Since the most commonly used color space in food industry is L* a* b, transformation of RGB digital images to L* a* b units was carried out using direct two steps model with γ factor. Experimental data obtained from images captured at different drying temperatures (100–130 °C) and several time intervals (15–180 min) were analyzed with a complete randomized block design (CRBD), and the means were compared with Duncan's multi-range test. Multiple linear regression (MLR) and artificial neural networks (ANN) were applied for correlating the color features to moisture content of dried shrimp determined chemically. Results obtained with these two models lead to 0.80 and 0.86 correlation coefficients in MLR and ANN models, respectively. While there is no statistical difference at p < 0.05 between the two modeling approaches, both approaches indicate successful prediction of shrimp dehydration with high correlation to those found by the more expensive and intrusive chemical method. The automated vision based system, therefore, has the advantage over conventional subjective methods and instrumental ones for being objective, fast, non-invasive, inexpensive and precise.