Classification of different liquid milk by near-infrared spectroscopy and ensemble modeling

Dairy products are necessary components of a healthy diet for human and nowadays, liquid milk become very popular because of its convenience. The identification of a brand of liquid milk is of importance. In this study, near-infrared (NIR) spectroscopy is used for rapid and objective classification of different brands of liquid milk. Chemometric methods including extreme learning machine (ELM) and its ensemble version (EELM) are investigated and compared. A dataset containing 144 samples from 6 brands are collected for experiment. A model-independent filter algorithm, i.e., relief-based feature selection, was used for variable reduction. Principal component analysis (PCA) is used as a tool of exploratory analysis for visualizing the difference among liquid milk samples of different brands. All samples were divided into three subsets, i.e., the training set, validation set and test set, for constructing, optimizing and testing the model, respectively. The model developed by the EELM procedure achieved 100% of classification accuracy, indicating that NIR spectroscopy combined with variable reduction and the EELM algorithm is feasible for classifying the brands of liquid milk.