Milk adulteration is a common phenomenon in many countries, which draws extensive attention from humans due to health hazards that might result in some fatal diseases. In this study, a portable near-infrared (NIR) spectrometer combined with multivariate analysis was used to detect and quantify milk adulteration. Fresh cow milk samples were collected from eight dairy farms in Beijing and Hebei province of China. Water, urea, starch and goat milk were used to adulterate milk at 11 different concentrations. The data driven soft independent modeling of class analogy (DD-SIMCA) method was employed for qualitative analysis. Partial least squares regression (PLSR) was applied for statistical analysis of the obtained NIR spectral data. The results showed that the DD-SIMCA approach achieved satisfactory classification. By the PLSR model, standard error of prediction (SEP) values of 4.35, 0.34, 4.74 and 5.56 g/L were obtained for water, urea, starch and goat milk, respectively. These results demonstrated the feasibility and reliability of NIR spectroscopy combined with multivariate analysis in the prediction of the total contents of the investigated adulterants in cow milk.
Key words: Portable near-infrared (NIR)-spectroscopy, milk adulteration, DD-SIMCA, Partial least squares (PLS) regression.
NIR, Near-infrared spectroscopy; PCA, principal component analysis; DD-SIMCA, data driven soft independent modeling of class analogy; PLSR, partial least squares regression; RMSE, root mean square error; RMSECV, root mean square error of cross validation; RMSEP, root mean square error of prediction.
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