Prediction of food quality parameters in fish burgers by partial least square models using RGB pattern of digital images.

ABSTRACT

Rancid taste, pH, and TBARS are important quality parameters of food oxidation, analyzed in a time-consuming and destructive way. Non-destructive characterization of food can be achieved correlating this data with computational vision. Thus, the present study aimed to use RGB digital images to predict sensory rancid taste, pH, and TBARS results in fish burgers. A mobile obtained the digital images, in a controlled environment, and 768 grayscales were performed using RGB histograms. The pH, showed a peak at 21st day of storage, which PCA confirmed by isolating the 21st samples, corroborated by HCA grouping 21st day samples. PLS models from RGB digital images and sensory rancidity, pH and TBARS data, using mean center method and SIMPLS algorithm found models with > 0.97 R. Thus, any digital image of this batch of burgers, inserted into the model to predict rancid taste, pH and TBARS has high confidence level of prediction.