Development and Evaluation of Integrated Non-Destructive Ultrasound, Resistance and Colorimetric System for Determination of Texture Properties of Rainbow Trout Fillet (Oncorhynchus mykiss) During Refrigeration

In this research, the ability of the integrated non-destructive system including ultrasound, electrical resistance measurements and colorimetric assays to predict the textural properties (hardness, brittleness, viscosity, elasticity, guminess and chewiness) of trout fillets were evaluated. For this purpose, fillets were stored for 12 days at refrigerator temperature and then examined at interval times. At the same time physical, mechanical, chemical and sensory tests were performed on the fillets. The performance of neural network and support vector machine methods for predicting and modeling tissue properties were compared. In each model, physical properties were considered as inputs and textural properties as outputs and modeling was performed. The results showed that in the prediction of hardness, fragility and elasticity of the support vector machine and in the viscous, resin and chewability indices, the neural network method had more capability to model the texture properties, so that the root mean square error (RMSE) Hardness, fragility, viscosity, elasticity, gum and chewability indices were equal to 0.144, 0.025, 0.015, 0.015, 0.044 and 0.171 respectively and their correlation coefficients were equal 0.993, 0.985, 0.992, 0.961, 0.995 and 0.995. Therefore, the proposed non-destructive system in combination with artificial intelligence methods showed promises as a non-destructive and efficient tool for monitoring and quality control during trout fillet storage.