Liang Q, Xia Y F, Che J K, Liu Y, Zhang H, Guo J C, Xu Q, Xue H N
College of Mechanical and Electronic Engineering, Tarim University, Alaer 843300, China; Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, Alaer 843300, China; Xinjiang Production and Construction Corps Key Laboratory of Utilization and Equipment of Special Agricultural and Forestry Products in Southern Xinjiang, Alaer 843300, China.
College of Mechanical and Electronic Engineering, Tarim University, Alaer 843300, China; Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, Alaer 843300, China; Xinjiang Production and Construction Corps Key Laboratory of Utilization and Equipment of Special Agricultural and Forestry Products in Southern Xinjiang, Alaer 843300, China.
J Dairy Sci. 2025 Jul;108(7):6852-6866. doi: 10.3168/jds.2025-26631. Epub 2025 May 12.
Milk is a nutrient-rich food, and water adulteration in milk can reduce its quality and increase food safety risks. Nondestructive and efficient detection of milk adulteration levels is crucial to addressing this issue. This study employed a portable near-infrared spectrometer to measure and analyze the absorbance of milk samples within the wavelength range of 900 to 1,800 nm. Based on the original spectra, models such as soft independent modeling of class analogy (SIMCA), naive Bayes, k-nearest neighbors, and support vector machine were constructed for the discrimination of water-adulterated milk. Preprocessing methods including Savitzky-Golay convolutional smoothing, Savitzky-Golay filtered derivative, multiplicative scatter correction, standard normal variate, vector normalization (VN), and min-max normalization were applied to the original spectra. Additionally, models such as one-dimensional convolutional neural network, partial least squares regression, and support vector regression (SVR) were established for the prediction of water content in milk. The results showed that the SIMCA model achieved 100% discrimination accuracy = 1, sensitivity = 1, specificity = 1, precision = 1, F-score = 1 in the analysis of water-adulterated milk. In the prediction of milk water content, the VN-SVR model performed the best (coefficient of determination for the training set R = 0.9996, root mean square error for the training set = 0.0477%, coefficient of determination for the test set R = 0.9992, root mean square error for the test set [RMSEP] = 0.066%), and also exhibited the best performance in model validation (R = 0.9760, RMSEP = 0.2554%). This study provides theoretical references and technical support for dairy product quality control and food safety regulation.
牛奶是一种营养丰富的食品，牛奶中的水分掺假会降低其品质并增加食品安全风险。无损且高效地检测牛奶掺假水平对于解决这一问题至关重要。本研究采用便携式近红外光谱仪测量和分析900至1800nm波长范围内牛奶样品的吸光度。基于原始光谱，构建了类软独立建模（SIMCA）、朴素贝叶斯、k近邻和支持向量机等模型用于鉴别掺水牛奶。对原始光谱应用了包括Savitzky-Golay卷积平滑、Savitzky-Golay滤波导数、多元散射校正、标准正态变量变换、向量归一化（VN）和最小-最大归一化等预处理方法。此外，还建立了一维卷积神经网络、偏最小二乘回归和支持向量回归（SVR）等模型用于预测牛奶中的水分含量。结果表明，SIMCA模型在掺水牛奶分析中实现了100%的鉴别准确率（判别率 = 1，灵敏度 = 1，特异性 = 1，精度 = 1，F值 = 1）。在牛奶水分含量预测中，VN-SVR模型表现最佳（训练集决定系数R = 0.9996，训练集均方根误差 = 0.0477%，测试集决定系数R = 0.9992，测试集均方根误差[RMSEP] = 0.066%），并且在模型验证中也表现出最佳性能（R = 0.9760，RMSEP = 0.2554%）。本研究为乳制品质量控制和食品安全监管提供了理论参考和技术支持。
