Purity detection of goat milk based on electronic tongue and improved artificial fish swarm optimized extreme learning machine

Abstract

The nutritional value of goat milk is higher than that of cow milk, which is scarce and expensive. Therefore, many bad credit enterprises mix cow’s milk into goat milk and sell it as pure goat milk, which infringes on the rights and interests of consumers. The adulteration of dairy products has a long history. Many techniques have been applied for the detection of dairy products’ adulteration, but no obvious results have been achieved. The electronic tongue system, based on a virtual instrument, can provide fast detection of goat milk’s quality. The qualitative identification of different purities of goat milk by kernel principal component analysis (KPCA) has achieved 100% accuracy. By comparing the four algorithms of the optimal extreme learning machine (ELM), it was determined that the quantitative model for the prediction of adulterated goat milk, established by the improved artificial fish swarm optimized ELM method, had high prediction accuracy. The prediction set coefficient R2 was 0.998, the average absolute error (MAE) was 0.083, and the root mean square error (MSE) was 0.011. According to its unique advantages, the electronic tongue provides a new idea and method for the detection of food adulteration. As a modern intelligent sensory instrument, the electronic tongue has great potential for brand identification as well as the identification of goat milk and goat milk powder adulteration.