Predictive ability of FT-NIRS in the assessment of chemical composition of pork seasoned products

Abstract

Conventional chemical analyses of meat cured products are time-consuming, expensive and destructive. Advantages of NIR spectroscopy are its speed, simplicity, low-cost and the possibility to determine a large number of different parameters simultaneously in a large number of samples. The aim of this study is to assess the ability of FT-NIRs to predict chemical composition of seasoned pig products. One hundred and two seasoned products were sampled (43 "Cuore di spalla", 26 Tos-cano Ham and 33 "Capocollo") and the following chemical components were determined: protein, intramuscular fat, ashes and fatty acid composition. NIR spectra were collected using a Thermo-Fisher Antaris II instrument. Partial least squares (PLS) regression was applied in the calibration and the validation models; the models were fully cross-validated using the “leave-one-out” method. Calibration and cross validation models were developed both for each product separately and grouping all the samples. Calibration correlation coefficients show satisfying values (minimum R2=0.73), while cross-validation correlation coefficients, despite being generally acceptable, show lower values (minimum R2=0.42). Best R2 was found for fat content (cross-validation R2= 0.95). Results, even if obtained on a reduced sample, show how FT-NIRs could be used in routine analyses of pig seasoned products.