Computer Vision Based Detection and Quantification of Extraneous Water in Raw Milk

Abstract A rapid method based on digital image analysis and machine learning technique is proposed for the detection of milk adulteration with water. Several machine learning algorithms were compared, and SVM performed best with 89.48 % of total accuracy and 95.10 % precision. An increase in the classification performance was observed in extreme classes. Better quantitative determination of the extraneous water was achieved using SVMR with R2(CV) and R2(P) of 0.65 and 0.71 respectively. The proposed technique can be used to screen raw milk based on the level of added extraneous water without the necessity of any additional reagent.