Quantitative Dried droplet Morphology and Image Analysis for Screening Adulteration in Milk

Abstract

Adulteration of milk with water, urea, calcium compounds and starch is still a widespread food safety problem, especially in areas where there is no access to laboratory based chemical testing, and is a global threat to human food safety and security, especially for children and the elderly. We present the development of a reagent free screening method, based on droplet evaporative deposition method, optical microscopy, and quantitative image analysis, for consistent detection and classification of milk adulteration. Droplets of Single Toned ST, 3% fat, and Double Toned DT, 1.5% fat milk samples, adulterated with water, urea, calcium, and starch, respectively, at different concentrations were tested. Deposition patterns were characterized by image processing using radial intensity profile descriptors area under the curve, and edge decay slope and gray level co occurrence matrix GLCM texture features contrast, correlation, energy, homogeneity, and entropy. The descriptors exhibit consistent adulterant specific trends: water and urea adulteration resulted in increasingly smooth, more homogeneous deposits decreasing contrast, increasing homogeneity, while calcium and starch adulteration resulted in structurally rougher deposits increasing contrast, decreasing homogeneity. Urea was further distinguished in the two groups by a significant increase in homogeneity and entropy collapse at higher concentrations, whereas calcium and starch were distinguished by diverging area under curve AUC trends. Milk type ST vs. DT was resolved by a combined multivariate signature of the descriptors at baseline. Our findings show that a simple, two level feature based framework: first resolving milk type, then adulterant family, then specific adulterant identity can be realized entirely from optical microscopy data without additional chemical reagents.