Milk is one of the most vulnerable food items for fraudulent activities such as dilution with water. Such malpractices have the potential to reduce the role of milk for food and nutrition security unless monitored and corrected. Research on simple and rapid techniques that allow users to diagnose adulteration in milk has the potential to ease the diagnosis of adulterated milk. This study proposed a procedure based on digital image analysis coupled with machine learning techniques for the detection of water adulteration in milk. Among the compared machine learning tools, SVM-based class prediction model performed best in classifying adulterated milk samples based on the amount of added water with 94% of total accuracy and 97% precision. In an attempt to the quantitative determination of the amount of added water, XGB-based regression model was able to achieve the best prediction performance compared to PCR, PLSR, SVMR, and ANNR, with R²(P) and RMSEP of 0.83 and 5.94, respectively. The proposed technique can be used as an alternative approach for the rapid determination of milk adulteration with water.

牛奶是欺诈活动（例如用水稀释）最容易受到攻击的食品之一。除非进行监测和纠正，否则此类不当行为可能会降低牛奶在食品和营养安全方面的作用。对允许用户诊断牛奶掺假的简单快速技术的研究有可能简化掺假牛奶的诊断。本研究提出了一种基于数字图像分析和机器学习技术的牛奶中掺水检测程序。在比较的机器学习工具中，基于支持向量机的类别预测模型在根据添加的水量对掺假牛奶样本进行分类方面表现最好，总准确率为 94%，准确率为 97%。为了定量测定添加的水量，R ² ( P ) 和 RMSEP 分别为 0.83 和 5.94。所提出的技术可用作快速测定牛奶掺水的替代方法。