The application of R language in the selection of characteristic bands for the prediction of protein content in milk powder by Near Infrared Spectroscopy

Abstract

Milk powder is an important food, suitable for preservation and transportation. Protein is an important nutritional component in milk powder. At present, the routine physical and chemical analysis method is usually used in the detection of components of milk powder, which is time-consuming and labor-consuming. Therefore, it is very important to carry out the rapid non-destructive detection of milk powder quality. The application of near infrared spectroscopy (NIRS) technology in the rapid detection of milk powder quality is increasingly mature, but many analytical techniques are not perfect. In this study, the models of prediction protein in milk powder were established by R language with NIRS, and the characteristic bands were selected by recursive feature extraction (RFE) in R language, and the selected bands were screened one by one to determine their importance. Finally, using Partial least squares (PLS), generalize linear model (GLM), support vector machine (SVM), least angle regression (LARS), linear model (LM) and other methods to build the prediction models with 8 characteristic bands, the R-squared of the models can be increased by 0.1 to 0.7, the model robustness greatly improved.