Abstract
The effectiveness of hyperspectral imaging (400-1000 nm) was proved as a nondestructive method to detect, classify, and quantify plant- and animal-based adulterants in minced beef and pork. Machine learning techniques were implemented to build classification and prediction models. Samples were first classified into adulterated (1 class) or pure (5 classes). The type of adulterant (6 classes) was then evaluated. Finally, the level of each adulterant was estimated using partial least squares regression. The optimal classification models based on selected wavelengths of test set yielded classification rates of 75-100% and 100% for pure and adulterated samples, respectively. Whereas, the rates were 83-100% depending on adulterant type. Prediction models for adulterant level yielded correlation coefficient, r, and ratio of performance to prediction, RPD, of 0.69(1.41) for beef adulterated with pork and textured vegetable protein (TVP), and 0.93(2.82) for beef adulterated with TVP. Improvement in results may be achieved with larger sample size.