Rapid Prediction of Multiple Quality Parameters in Milk Powder by Ultraviolet Spectrometry Combined with Chemometric Method

Abstract

AbstractThe composition of milk powder (powdered milk) determines its quality and nutritional value. Currently, the standard or traditional methods that measure content of main components of milk powder have some disadvantages. In this study, ultraviolet (UV) spectroscopy combined with multivariate calibration/regression model was used to simultaneously predict the value of four main quality parameters including protein, fat, carbohydrate and energy rather than single component content in milk powder. Partial least squares (PLS) was chosen to establish regression model with the optimized number of principal factor. Without component separation/purification in the measurement with UV spectroscopy and pretreatment process in PLS modeling, good prediction results of multi-parameters were obtained with low root mean square error of prediction (RMSEP), high correlation coefficients (>0.98) and high RPD (Residual predictive deviation). By comparison, the results obtained by directly using work curve method were not satisfactory. Furthermore, PLS model acquired accurate and robust results than those of multivariate linear regression (MLR) model. It indicates that with the help of PLS, UV spectrometry is an effective, fast and simple “green” technique to simultaneously detect content of main parameters in milk powder. The proposed method could be applied to the quality control of milk powder, and be studied further to extend to quantitative analysis of milk liquid and even other food.KeywordsUltraviolet spectraChemometricsMilk powderQuantitative analysis