Feasibility of near infrared transmittance spectroscopy to predict fatty acid composition of commercial processed meat.

BACKGROUND: The new European Regulation 1169/2011 concerning nutrition declaration of food products compels the addition of saturated fatty acids, whereas the declaration of monounsaturated and polyunsaturated fatty acids remains voluntary. Therefore, the industry is interested in a more rapid, easy and less cost-effective analysis method for accomplishing this labelling regulation. The present study aimed to evaluate the ability of near infrared transmittance spectroscopy (wavelengths between 850 and 1050 nm) to predict the fatty acid (FA) composition of commercial processed meat samples (n = 310). RESULTS: Good predictions were achieved for the absolute content of saturated, unsaturated, monounsaturated and polyunsaturated FA, as well as ω-6 groups, and also for a few individual FA (C16:0, C18:0, C18:1n9, C18:2n6 and 18:1n7), with the coefficient of determination in cross-validation being > 0.90 and the residual prediction deviation being > 3.15. Unsatisfactory models were obtained for the relative content of FA. CONCLUSION: Near infrared transmittance spectroscopy can be considered as a reliable method for predicting the main groups of FA in processed meat products, whereas predictions of individual FA are less reliable. © 2017 Society of Chemical Industry.

Prediction of sodium content in commercial processed meat products using near infrared spectroscopy.

The present study evaluated the ability of near infrared transmittance (NIT) spectroscopy (FoodScan, 850-1050nm) to predict sodium (Na) content in commercial processed meat products (n=310) as intact and ground samples. Prediction models were built with all samples spectra and with spectra divided in 5 categories according to the manufacturing meat process. Sodium content (%) was determined using inductively coupled plasma optical emission spectrometry. Modified partial least squares regression for the overall samples showed satisfactory predictive ability for intact (coefficient of determination in cross-validation, R2CV=0.93) and ground samples (R2CV=0.95). Despite the low number of samples, good specific prediction models were developed for each commercial meat category. In conclusion, NIT is really promising for at-line application to predict Na in processed meat products which could help industry to accomplish the new labelling regulation.