Peng Dan, Xu Rui, Zhou Qi, Yue Jinxia, Su Min, Zheng Shaoshuai, Li Jun
College of Food Science and Engineering, Henan University of Technology, Zhengzhou 450001, China.
School of International Education, Henan University of Technology, Zhengzhou 450001, China.
Molecules. 2023 Jul 28;28(15):5728. doi: 10.3390/molecules28155728.
Milk is one of the preferred beverages in modern healthy diets, and its freshness is of great significance for product sales and applications. By combining the two-dimensional (2D) correlation spectroscopy technique and chemometrics, a new method based on visible/near-infrared (Vis/NIR) spectroscopy was proposed to discriminate the freshness of milk. To clarify the relationship be-tween the freshness of milk and the spectra, the changes in the physicochemical indicators of milk during storage were analyzed as well as the Vis/NIR spectra and the 2D-Vis/NIR correlation spectra. The threshold-value method, linear discriminant analysis (LDA) method, and support vector machine (SVM) method were used to construct the discriminant models of milk freshness, and the parameters of the SVM-based models were optimized by the grid search method and particle swarm optimization algorithm. The results showed that with the prolongation of storage time, the absorbance of the Vis/NIR spectra of milk gradually increased, and the intensity of autocorrelation peaks and cross peaks in synchronous 2D-Vis/NIR spectra also increased significantly. Compared with the SVM-based models using Vis/NIR spectra, the SVM-based model using 2D-Vis/NIR spectra had a >15% higher prediction accuracy. Under the same conditions, the prediction performances of the SVM-based models were better than those of the threshold-value-based or LDA-based models. In addition, the accuracy rate of the SVM-based model using the synchronous 2D-Vis/NIR autocorrelation spectra was >97%. This work indicates that the 2D-Vis/NIR correlation spectra coupled with chemometrics is a great pattern to rapidly discriminate the freshness of milk, which provides technical support for improving the evaluation system of milk quality and maintaining the safety of milk product quality.
牛奶是现代健康饮食中首选的饮品之一，其新鲜度对于产品销售和应用具有重要意义。通过将二维（2D）相关光谱技术与化学计量学相结合，提出了一种基于可见/近红外（Vis/NIR）光谱的牛奶新鲜度判别新方法。为了阐明牛奶新鲜度与光谱之间的关系，分析了牛奶在储存过程中的理化指标变化以及Vis/NIR光谱和二维Vis/NIR相关光谱。采用阈值法、线性判别分析（LDA）法和支持向量机（SVM）法构建牛奶新鲜度判别模型，并通过网格搜索法和粒子群优化算法对基于SVM的模型参数进行优化。结果表明，随着储存时间的延长，牛奶Vis/NIR光谱的吸光度逐渐增加，同步二维Vis/NIR光谱中的自相关峰和交叉峰强度也显著增加。与使用Vis/NIR光谱的基于SVM的模型相比，使用二维Vis/NIR光谱的基于SVM的模型预测准确率高出15%以上。在相同条件下，基于SVM的模型的预测性能优于基于阈值或基于LDA的模型。此外，使用同步二维Vis/NIR自相关光谱的基于SVM的模型的准确率大于97%。这项工作表明，二维Vis/NIR相关光谱与化学计量学相结合是快速判别牛奶新鲜度的良好模式，为完善牛奶质量评价体系和保障奶制品质量安全提供了技术支持。
