定量干滴形态学与图像分析用于牛奶掺假筛查

中文摘要 AI 辅助

牛奶中掺入水、尿素、钙化合物和淀粉仍然是一个普遍存在的食品安全问题，尤其是在无法进行实验室化学检测的地区，并且对人类食品安全和保障构成全球性威胁，特别是对儿童和老年人。我们提出了一种基于液滴蒸发沉积法、光学显微镜和定量图像分析的无试剂筛查方法的开发，用于一致地检测和分类牛奶掺假。分别测试了掺入不同浓度水、尿素、钙和淀粉的Single Toned（ST，3%脂肪）和Double Toned（DT，1.5%脂肪）牛奶样品的液滴。通过图像处理表征沉积图案，使用径向强度剖面描述符（曲线下面积）和边缘衰减斜率，以及灰度共生矩阵（GLCM）纹理特征（对比度、相关性、能量、同质性和熵）。描述符表现出一致的掺假物特定趋势：水和尿素掺假导致沉积物越来越光滑、更均匀，对比度降低，同质性增加，而钙和淀粉掺假导致结构更粗糙的沉积物，对比度增加，同质性降低。尿素在两组中通过在高浓度下同质性显著增加和熵崩溃进一步区分，而钙和淀粉通过曲线下面积（AUC）趋势的分歧来区分。牛奶类型ST与DT通过基线处描述符的组合多变量特征得以分辨。我们的研究结果表明，一个简单的、基于两级特征的框架：首先分辨牛奶类型，然后分辨掺假物家族，再分辨具体掺假物身份，可以完全从光学显微镜数据中实现，无需额外化学试剂。

英文摘要

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.

发表机构

• Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）

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