Accuracy and biases in predicting the chemical and physical traits of many types of cheeses using different visible and near-infrared spectroscopic techniques and spectrum intervals

Abstract

Near-infrared spectroscopy (NIRS) has been widely used to determine various composition traits of many dairy products in the industry. In the last few years, near-infrared (NIR) instruments have become more and more accessible, and now, portable devices can be easily used in the field, allowing the direct measurement of important quality traits. However, the comparison of the predictive performances of different NIR instruments is not simple, and the literature is lacking. These instruments may use different wavelength intervals and calibration procedures, making it difficult to establish whether differences are due to the spectral interval, the chemometric approach, or the instrument's technology. Hence, the aims of this study were (1) to evaluate the prediction accuracy of chemical contents (5 traits), pH, texture (2 traits), and color (5 traits) of 37 categories of cheese; (2) to compare 3 instruments [2 benchtop, working in reflectance (R) and transmittance (T) mode (NIRS-R and NIRS-T, respectively) and 1 portable device (VisNIRS-R)], using their entire spectral ranges (1100-2498, 850-1048, and 350-1830 nm, respectively, for NIRS-R, NIRS-T and VisNIRS-R); (3) to examine different wavelength intervals of the spectrum within instrument, comparing also the common intervals among the 3 instruments; and (4) to determine the presence of bias in predicted traits for specific cheese categories. A Bayesian approach was used to develop 8 calibration models for each of 13 traits. This study confirmed that NIR spectroscopy can be used to predict the chemical composition of a large number of different cheeses, whereas pH and texture traits were poorly predicted. Color showed variable predictability, according to the trait considered, the instrument used, and, within in-strument, according to the wavelength intervals. The predictive performance of the VisNIRS-R portable device was generally better than the 2 laboratory NIRS instruments, whether with the entire spectrum or selected intervals. The VisNIRS-R was found suitable for analyzing chemical composition in real time, without the need for sample uptake and processing. Our results also indicated that instrument technology is much more important than the NIR spectral range for accurate prediction equations, but the visible range is useful when predicting color traits, other than lightness. Specifically for certain categories (i.e., caprine, moldy, and fresh cheeses), dedicated calibrations seem to be needed to obtain unbiased and more accurate results.

Predicting the Content of 20 Minerals in Beef by Different Portable Near-Infrared (NIR) Spectrometers

“…However, P could also be indirectly detected through its linkage with other organic complexes, chelates, and pigments. As Li et al [ 33 ] pointed out, the phosphorylation process changes the meat color and pH. In support of this conjecture, we found a highly significant correlation in these same beef samples between the lab-measured P content and the redness, yellowness, chroma, and hue color indices of the cross-sectional surface of the meat.…”

NIRS to assess chemical composition of sheep and goat cheese

“…The best models in terms of R 2 cv were obtained for moisture while ash, protein and fat showed values from 0.71 to 0.77 (Table 2). Results obtained by Stocco et al (2019) on the freshly cut cheese surface and concerning a whole spectrum of different cheeses, achieved R 2 comparable to our results for moisture and protein even if our errors in cross-validation were slightly higher probably due to the lower number of samples. Ash and lipid R 2 of our study were better than those reported by Stocco et al (2019), even if those authors showed better results when the region from 350 to 1,830 nm, namely visible area in reflectance mode, was considered.…”

“…Results obtained by Stocco et al (2019) on the freshly cut cheese surface and concerning a whole spectrum of different cheeses, achieved R 2 comparable to our results for moisture and protein even if our errors in cross-validation were slightly higher probably due to the lower number of samples. Ash and lipid R 2 of our study were better than those reported by Stocco et al (2019), even if those authors showed better results when the region from 350 to 1,830 nm, namely visible area in reflectance mode, was considered. Considering the near infrared region, Karoui et al (2006) obtained higher R 2 cv (0.94 and 0.86) both for lipids and protein but respect to our study, their study concerned only a single type of product (Emmental cheese) manufactured by different farms.…”

Influence of starter culture on nitrogen fraction and volatile compounds in Beaten cow's milk cheese

“…One terpene was detected in BS beaten cheese being at highest concentration on the 1 st day of ripening, but afterward, this compound was not detected at the end of ripening. All these components are important for the characterization of cheese products and are useful especially for the evaluation of food quality within the infrared spectroscopy techniques (Stocco, Cipolat‐Gotet, Ferragina, Berzaghi, & Bittante, 2019).…”