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A method to detect water-injected pork based on bioelectrical impedance technique

Leng, Yue, Sun, Yonghai, Wang, Xiaodan, Hou, Jumin, Bai, Xue, Wang, Minghui
Journal of food measurement & characterization 2019 v.13 no.2 pp. 1341-1348
artificial intelligence, bioelectrical impedance, color, cooking quality, dielectric spectroscopy, least squares, loins, physicochemical properties, pork, pork industry, pressing, texture
The objective of this study was to detect water-injected pork using electrical impedance spectroscopy (EIS) and an artificial intelligence model in a rapid, accurate, and minimally destructive method. Pork loins were injected with water (0%, 3.5%, 7%, 10.5%, 14% and 17.5%, respectively) and physicochemical measurements including cooking loss, pressing loss, color, and textural properties were evaluated after injection. Results indicated that injection of water negatively affected physicochemical characteristics of meat samples (Pā€‰<ā€‰0.05). Water-injected pork had increased cooking loss and pressing loss. There were also significant differences found in color and textural properties. In addition, prediction models for the correlation between impedance magnitude and water injection rate of meat samples were established using support vector regression (SVR) and partial least square regression (PLSR). The results showed that both models performed well at different water injection rate. The model based on SVR obtained optimal performance. From the analysis of meat samples, the accuracy of SVR, which yielded 85.3%, was superior to that of PLSR with accuracy of 84.0%. EIS could be a potential technique for detecting water-injected porcine meat produced in the pork industry.