Feasibility of pocket-sized near-infrared spectrometer for the prediction of cheese quality traits

This study aimed to evaluate the feasibility of a low-cost pocket-sized near-infrared spectrometer (NIR; 740-1070 nm) to predict cheese total nitrogen (%), soluble nitrogen (%), ripening index (%), major minerals (g/100 g of cheese), and fatty acids (g/100 of cheese). A total of 104 ground cheeses samples were scanned 5 times with a pocket-sized NIR and matched with the reference data to develop the prediction models. Modified partial least squares regressions were developed and parameters were optimized through a leave one out cross-validation. Models were validated using 13-fold nested cross validation. The best models were obtained for total fatty acids (ratio of prediction to deviation, RPD = 6.61) and total nitrogen (RPD = 5.95) content. Regarding mineral content, best prediction models were obtained for P (RPD = 4.04), Na (RPD = 3.12), and Ca (RPD = 2.77). Main individual fatty acids and fatty acid groups were poorly predicted, except for SFA (RPD = 2.92), C16:0 (RPD = 2.83), and C4:0 (RPD = 2.51). However, the accuracy of the models was similar to those obtained with benchtop infrared devices. Thus, these results demonstrated that miniaturization of the NIR instruments represents an opportunity in the dairy industry.

Automated summary

This study evaluated the feasibility of using a low-cost pocket-sized near-infrared spectrometer to predict cheese composition, achieving good results for fatty acid and nitrogen content. Although mineral content prediction was less accurate, the study showed the potential of miniaturized NIR instruments in the dairy industry.