Application of residual neural networks to detect and quantify milk adulterations

In order to develop a milk adulteration detection system, a method based on optimized convolutional neural networks has been developed. First, milks of different animal origins and lactose content have been artificially adulterated with water within the 1–25 ppm range to prepare samples that will lead to a broad image database. The milks chosen for this work were from cow, goat, sheep, as well as lactose-free cow milk. The samples were photographed in two light conditions (1/30 s for bright and 1/500 s for dark), resulting in a total of 10,400 images. As for the mathematical algorithm, two residual neural networks based on transfer learning were trained to perform the classification of the milk samples, leading to accuracies above 93 %. These results open the door to the design of prototypes for fast, easy-to-use, and reliable adulterant detection in the dairy industry by means of deep learning-based image classification. •Cow’s milk mixed with water and other milks to prepare image database.•Photographs at varying the shutter speeds to evaluate lighting conditions.•ResNet50 models trained for each shutter speed to classify milk samples.•Fast, inexpensive, and reliable technique to quantify milk adulterations.•Blind testing for both models led to accuracies above 93 %.