Calibration models for determining moisture and fat content of processed cheese using near-infrared spectrometry

The determination of moisture and fat in processed cheese is a common and regular requirement in the manufacture of this foodstuff, and near-infrared spectrometry in the short-wavelength region (700-1200 nm) can provide the basis for a suitable on-line and off-line quantitative analytical methodology if used with a suitable calibration model. In this study, using data from a 12-filter spectrometer, several calibration models including ordinary least squares, multiple linear regression, principal component regression and partial least squares regression have been examined and evaluated for efficacy in determining moisture and fat content directly and simultaneously in grated cheese samples. Results indicate that orthogonal models using selected wavelength data offer superior predictive performance.