This study aimed to develop a fast analytical method, combining near infrared reflectance spectroscopy and multivariate analysis, for detection and quantification of pork meat in other meat samples. A total of 5952 mixture samples from 39 types of meat were prepared in triplicate, with the inclusion of pork at 0%, 1%, 5%, 10%, 30%, 50%, 70%, 90% and 100%. Each sample was scanned using an FT-NIR spectrophotometer in the reflection mode. Spectra were collected in the wavenumber range from 10,000 to 4000 cm⁻¹, at a resolution of 2 cm⁻¹ and a total path length of 0.5 mm. Principal Component Analysis (PCA) revealed the similarities and differences among the various types of meat samples and Partial Least-Squares Discriminant Analysis (PLS-DA) showed a good discrimination between pure and pork-spiked meat samples. A Partial Least-Squares Regression (PLSR) model was built to predict the pork meat contents in other meats, which provided the R² value of 0.9774 and RMSECV value of 1.08%. Additionally, an external validation was carried out using a test set, providing a rather good prediction error, with an RMSEP value of 1.84%.

这项研究旨在开发一种结合近红外反射光谱和多元分析的快速分析方法，以检测和定量其他肉类样品中的猪肉。一式三份，准备了来自39种肉类的5952种混合物样品，其中猪肉的含量为0％，1％，5％，10％，30％，50％，70％，90％和100％。使用FT-NIR分光光度计以反射模式扫描每个样品。在10,000至4000 cm ^-1的波数范围内收集光谱，分辨率为2 cm ^-1总路径长度为0.5毫米 主成分分析（PCA）显示了各种类型的肉样品之间的异同，偏最小二乘判别分析（PLS-DA）显示出纯猪肉样品和猪肉加标肉样品之间的良好区别。建立了偏最小二乘回归（PLSR）模型来预测其他肉类中的猪肉含量，其R ²值为0.9774，RMSECV值为1.08％。此外，使用测试集进行了外部验证，提供了相当不错的预测误差，RMSEP值为1.84％。