Fast identification of powdered milk adulteration by generalized regression neural network algorithm

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In this paper we propose a generalized regression neural network (GRNN) algorithm to rapidly identify adulterants of milk powder. Milk, milk powder and other dairy products are rich in nutrients such as protein, carbohydrates, and fats. The demand for dairy products are increasing persistently. However, incidents of adulteration of dairy products occurs a lot. To obtain huge profits, many unscrupulous merchants add melamine, starch and sugar instead of nutrients to the milk powder, leading to the mendaciously increasing content of protein in the powder. Conventional dairy product content detection methods, which have devices with large weight and volume, need to take a long time to measure the content of adulteration, and thus they are not suitable for rapid on-site detection. Therefore, we use a GRNN model to rapidly identify the adulteration by measuring the Raman spectra of adulterated substances in dairy products. The GRNN model can be trained for classifying the Raman spectra of adulterated substances. The result shows that the model can achieve 100% accuracy in identifying milk powder, melamine, wheat flour and corn flour, and the identification time of algorithm is seconds-level, laying the foundation for the development of rapid detection technology for adulterated dairy products.