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.

中文翻译：

---

高光谱成像和机器学习方法在肉末掺假中的检测和定量应用

摘要

高光谱成像（400-1000 nm）的有效性被证明是一种用于检测，分类和定量碎牛肉和猪肉中基于植物和动物的掺假物的非破坏性方法。实施了机器学习技术以建立分类和预测模型。首先将样品分为掺假（1级）或纯净（5级）。然后评估掺假的类型（6类）。最后，使用偏最小二乘回归估计每种掺杂物的水平。基于所选测试集波长的最佳分类模型得出的纯样品和掺假样品的分类率分别为75–100％和100％。而根据掺假类型，该比率为83-100％。掺假水平的预测模型产生相关系数r，掺假猪肉和有组织的植物蛋白（TVP）的牛肉的性能与预测RPD的比率为0.69（1.41），掺假TVP的牛肉的性能与预测的RPD的比率为0.93（2.82）。使用更大的样本量可以提高结果。