Prediction of Moisture, Fat and Inorganic Salts in Processed Cheese by near Infrared Reflectance Spectroscopy and Multivariate Data Analysis

Abstract

Near infrared (NIR) spectroscopy is widely used in the food industry as a quality control tool. Usage in the dairy industry is extensive, but reports of its application to processed cheese analysis are few. This work investigated the potential of NIR spectroscopy and multivariate data analysis to determine the moisture, fat and inorganic salts content of processed cheeses stored for up to four weeks. Reflectance spectra (400–2498 nm) and reference values of cheese samples ( n = 64) were collected. Calibrations to predict moisture (37.7–54.8% w/w), fat (23.7–34.2% w/w) and inorganic salts (2.0–4.7% w/w) content were developed by a partial least squares (PLS) regression procedure. Models were developed using five wavelength ranges; 400–2498 nm, 400–750 nm (visible), 400–1100 nm, 750–1100 nm (near near infrared) and 1100–2498 nm (near infrared). Spectral data were used (1) without any pre-treatment, (2) after scatter correction (standard normal variate and de-trending) and (3) the latter plus a second derivative step (10 data point gap size). For both moisture and fat, the preferred models were obtained using the latter treatment. Fat prediction used spectral data between 1100 and 2498 nm ( SECV = 0.45, R = 0.98) with five loadings. For moisture, the preferred prediction was obtained using the wavelength range between 1100 and 2498 nm ( SECV = 0.50, R = 0.99) using four loadings. For inorganic salts calibration, preference was for the model obtained using the second option above on spectral data also in the range 1100–2500 nm ( SECV = 0.26, R = 0.90 with seven loadings). These results are sufficiently accurate to recommend NIR reflectance spectroscopy for off-line quality assessment of processed cheese.