Multivariate methods for the identification of adulterated milk based on two-dimensional infrared correlation spectroscopy

Abstract

The discrimination analysis of adulterated milk has been carried out based on two-dimensional (2D) infrared correlation spectroscopy along with multivariate methods like kernel orthogonal projection to latent structure (K-OPLS), multi-way partial least squares discriminant analysis (NPLS-DA), and unfolded partial least squares discriminant analysis (PLS-DA). 2D correlation spectroscopy, due to high spectral resolution and good spectral interpretation capabilities, is suitable for the analysis of complex biological data. 64 pure milk samples and 64 adulterated milk samples were measured in the mid-infrared range of 900–1700 cm⁻¹. Then, the synchronous 2D correlation spectra of all samples were calculated in the region of between 900–1200 cm⁻¹ and 1200–1700 cm⁻¹. Finally, the K-OPLS, NPLS-DA, and unfolded PLS-DA models were developed based on the synchronous 2D correlation spectra of adulterated milk and pure milk. The classification accuracy rates of unknown samples for K-OPLS, NPLS-DA, and unfolded PLS-DA models were 95%, 92.5%, and 92.5%, respectively. The results indicated that 2D correlation infrared spectroscopy combined with multivariate methods were feasible and efficient for discrimination of adulterated milk.