牛奶蛋白质的分析和监测是奶制品行业中不可或缺的环节利用可见光/近红外反射光谱(350～2 500 nm)进行纯牛奶中真蛋白质含量的快速定量反演。 分别通过ASD地物光谱仪和CEM真蛋白质测定仪采集牛奶样本的反射光谱数据以及蛋白质含量数据, 对比分析不同的光谱预处理方法和波段筛选方法, 得到特征波段, 最后利用主成分回归(PCR)和最小二乘支持向量机(LS-SVM)模型建立牛奶反射光谱和蛋白质含量之间的定量校正模型, 并对其预测能力进行比较, 从而确定最优的牛奶中真蛋白质含量反演模型。 实验结果证明: (1)比较不同光谱预处理方法, 发现多元散射校正与二阶微分联合使用效果较好; (2)相对于全光谱建模, 适当的特征变量优选有助于提高建模精度, 缩短建模时间; (3)PCR的验证集决定系数R2P为0.952 2, 验证集均方根误差RMSEP为0.048 7, 而LS-SVM的R2P为0.958 0, RMSEP为0.048 2, 其预测精度要优于PCR。 研究表明, 可见光/近红外高光谱反射率数据可以为牛奶真蛋白质含量的检测提供一种快速、 无损的新方法。

As an indispensable drink of people’s daily life, milk’s quality has been also increasingly concerned by consumers. Rapid and accurate detection of milk and its products is the indispensable step for improving the quality of milk and daily products in production. However, traditional methods cannot meet the need. In this paper, rapid quantitative detection of true protein in pure milk was studied by using visible/near-infrared (VIS/NIR) reflectance spectroscopy (350～2 500 nm). The spectral data and the protein content data of the pure milk samples were collected by ASD spectrometer and CEM rapid protein analyzer, respectively. Based on the analysis and comparison of different spectrum preprocessing methods and band selection methods, the feature bands were determined. Finally, using the Principle Component Regression (PCR) and Least Squares Support Vector Machine (LS-SVM) model, the regression models between the reflectance spectroscopy and the protein content in milk were presented for pure milk samples and the predictive ability was also analyzed. In this way, the optimal inversion model for true protein content in milk was established. The results were shown as follows: (1) In the process of spectral pretreatment, the combination of multiple scatter correction and second derivative achieved a better result; (2) Compared with the modeling of whole spectral, appropriate variable optimization models had the ability to improve the accuracy of the inversion results and reduce the modeling time; (3) The analysis results between PCR model and LS-SVM model demonstrated that the prediction accuracy of LS-SVM model was better than PCR model. The coefficient of determination (R2P) of PCR and LS-SVM were 0.952 2 and 0.958 0 respectively, and the root mean square error of prediction (RMSEP) of PCR and LS-SVM were 0.048 7 and 0.048 2 respectively. The result of this research is expected to provide a novel method for nondestructive and rapid detection of true protein in milk.