Characterization of Near-Infrared Spectral Variance in the Authentication of Skim and Nonfat Dry Milk Powder Collection Using ANOVA-PCA, Pooled-ANOVA, and Partial Least-Squares Regression

[Image: see text] Forty-one samples of skim milk powder (SMP) and nonfat dry milk (NFDM) from 8 suppliers, 13 production sites, and 3 processing temperatures were analyzed by NIR diffuse reflectance spectrometry over a period of 3 days. NIR reflectance spectra (1700–2500 nm) were converted to pseudoabsorbance and examined using (a) analysis of variance-principal component analysis (ANOVA-PCA), (b) pooled-ANOVA based on data submatrices, and (c) partial least-squares regression (PLSR) coupled with pooled-ANOVA. ANOVA-PCA score plots showed clear separation of the samples with respect to milk class (SMP or NFDM), day of analysis, production site, processing temperature, and individual samples. Pooled-ANOVA provided statistical levels of significance for the separation of the averages, some of which were many orders of magnitude below 10(–3). PLSR showed that the correlation with Certificate of Analysis (COA) concentrations varied from a weak coefficient of determination (R(2)) of 0.32 for moisture to moderate R(2) values of 0.61 for fat and 0.78 for protein for this multinational study. In this study, pooled-ANOVA was applied for the first time to PLS modeling and demonstrated that even though the calibration models may not be precise, the contribution of the protein peaks in the NIR spectra accounted for the largest proportion of the variation despite the inherent imprecision of the COA values.