The main objective of this study was to investigate the use of spectroscopic systems in the range of 400–1000 nm (visible/near-infrared or Vis-NIR) and 900–1700 nm (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.

这项研究的主要目的是研究光谱系统在400–1000 nm（可见/近红外或Vis-NIR）和900–1700 nm（NIR）范围内的使用，以评估和估算植物和动物蛋白的含量。牛肉和猪肉末可能掺假。多种机器学习技术用于分类，掺假预测和波长选择。首先评估样品中掺假品的存在与否（6类），其次评估掺假品的类型（6类）和水平。选定的波长模型通常比全波长具有更好的分类和预测输出。纯净/纯净和and杂样品的第一阶段分类率分别为96％和100％。而第二阶段的分类率为69–100％。r为0.78-0.86，性能与偏差之比RPD为1.19-1.98。这项研究的结果说明了光谱技术在快速准确地检测牛肉和猪肉碎中掺假品方面的潜在应用。