Assessing different processed meats for adulterants using visible-near-infrared spectroscopy.

The main objective of this study was to investigate the use of spectroscopic systems in the range of 400-1000nm (visible/near-infrared or Vis-NIR) and 900-1700nm (NIR) to assess and estimate plant and animal proteins as potential adulterants in minced beef and pork. Multiple machine learning techniques were used for classification, adulterant prediction, and wavelength selection. Samples were first evaluated for the presence or absence of adulterants (6 classes), and secondly for adulterant type (6 classes) and level. Selected wavelengths models generally resulted in better classification and prediction outputs than full wavelengths. The first stage classification rates were 96% and 100% for pure/unadulterated and adulterated samples, respectively. Whereas, the second stage had classification rates of 69-100%. The optimal models for predicting adulterant levels yielded correlation coefficient, r of 0.78-0.86 and ratio of performance to deviation, RPD, of 1.19-1.98. The results from this study illustrate potential application of spectroscopic technology to rapidly and accurately detect adulterants in minced beef and pork.