Contribution of Fourier transform infrared (FTIR) spectroscopy data on the quantitative determination of minced pork meat spoilage

The aim of this work was to investigate the feasibility of Fourier transform infrared (FTIR) spectroscopy to quantify biochemical changes occurring in fresh minced pork meat in the attempt to monitor spoilage. For this reason, partial least squares (PLS) models were constructed to correlate spectral data from FTIR with minced pork meat spoilage during aerobic storage of meat samples at different storage temperatures (0, 5, 10, and 15 °C). Spectral data were collected from the surface of meat samples in parallel with microbiological analysis to enumerate the population of total viable counts, Pseudomonas spp., Brochothrix thermosphacta, lactic acid bacteria and Enterobacteriaceae. Qualitative interpretation of spectral data was based on sensory evaluation, using a three point hedonic scale, discriminating meat samples in three quality classes, namely fresh, semi-fresh and spoiled. The purpose of the developed models was to classify minced pork samples in the respective quality class, and also to correlate the population dynamics of the microbial association with FTIR spectra. The obtained results demonstrated good performance in classifying meat samples in one of the three pre-defined sensory classes. The overall correct classification rate for the three sensory classes was 94.0% and 88.1% during model calibration and validation, respectively. Furthermore, PLS regression models were also employed to provide quantitative estimations of microbial counts during meat storage. The performance was based on graphical plots and statistical indices (bias factor, accuracy factor, standard error of calibration, standard error of prediction, and correlation coefficient). The values of the bias factor were close to unity for all microbial groups indicating no systematic bias of the models. Moreover, the calculated values of the accuracy factor showed that the average deviation between predictions and observations was 7.5% and 7.9% for total viable counts and Pseudomonas spp. and 10.7% and 11.3% for lactic acid bacteria and B. thermosphacta. Finally, correlations above 0.80 between observed and estimated counts were observed for both training and test data sets.     

Differentiation of perirenal and omental fat quality of suckling lambs according to the rearing system from Fourier transforms mid-infrared spectra using partial least squares and artificial neural networks analysis

Fourier transform mid-infrared (FT-IR) spectroscopy was evaluated as a tool to discriminate between carcasses of suckling lambs according to the rearing system. Fat samples (39 perirenal and 67 omental) were collected from carcasses of lambs from up to three sheep dairy farms, reared on either ewes milk (EM) or milk replacer (MR). Fatty acid composition of the samples from each fat deposit was first analyzed and, when discriminant-partial least squares regression (PLS) was applied, a perfect discrimination between rearing systems could be established. Additionally, FT-IR spectra of fat samples were obtained and discriminant-PLS and artificial neural network (ANN) based analysis were applied to data sets, the latter using principal component analysis (PCA) or support vector machines (SVM) as processing procedure. Perirenal fat samples were perfectly discriminated from their FT-IR spectra. However, analysis of omental fat showed misclassification rates of 9–13%, with the ANN approach showing a higher discrimination power.