Predictive modeling of milk adulteration with urea content using the gray wolf optimization algorithm and long and short-term memory network model

Traditional methods for the determining of urea contaminants in milk, including chromatography, spectrophotometry, and electrochemical processes, have drawbacks such as long testing times and sample destruction. However, hyperspectral imaging technology provides a fast, easy-to-operate, and real-time alternative. In this study, five preprocessing methods are employed, including standard scalar, standard normal variational, first derivative, multivariate scattering correction algorithm, and Savitzky-Golay smoothing. To further enhance the accuracy of predicting urea content in milk, a prediction optimization method based on the gray wolf optimization algorithm for the long short-term memory network was proposed, using a competitive adaptive reweighted sampling algorithm feature wavelength selection algorithm. In the optimized model in the test set experiments, the decision coefficient was improved by 0.56% to 0.9906, and root mean square error was reduced by 7.69% to 0.4996 compared to the long short-term memory network model. This study not only provides a theoretical basis but also presents a fast and nondestructive detection method for accurately predicting urea content in milk.