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The objective of the present study was the evaluation of Fourier transform infrared (FTIR) spectroscopy and multispectral imaging (MSI), in tandem with multivariate data analysis, as means of estimating the microbiological quality of sea bream. Farmed whole ungutted fish were stored aerobically at 0, 4 and 8 °C. At regular time intervals, fish samples (i.e. cut portions) were analysed microbiologically, while FTIR and MSI measurements also were acquired at both the skin and flesh sides of the samples. Partial least squares regression (PLSR) models were calibrated to provide quantitative estimations of the microbiological status of fish based on spectral data, in a temperature-independent manner. The PLSR model based on the FTIR data of fish skin exhibited good performance when externally validated, with the coefficient of determination (R 2 ) and the root mean square error (RMSE) being 0.727 and 0.717, respectively. Hence, FTIR spectroscopy appears to be promising for the rapid and non-invasive monitoring of the microbiological spoilage of whole sea bream. Contrarily, the MSI models' performance was unsatisfactory, delimitating their potential exploitation in whole fish quality assessment. Model optimization results concerning fish flesh indicated that MSI may be propitious in skinned fish products, with its definite competence warranting further investigation., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The aim of the present study was to investigate the potential use of Fourier transform infrared (FTIR) spectroscopy to quantify biochemical changes occurring in ham slices packed with probiotic supplemented edible films and treated with High Pressure Processing (HPP), in monitoring spoilage. Details regarding the data collection and experimental procedure were presented by Pavli et al. (2017). A series of Partial Least Squares (PLS) models were developed to correlate spectral data from FTIR analysis with ham spoilage during storage under vacuum at different temperatures (4, 8 and 12°C). FTIR spectra were collected from the surface of the ham samples in parallel with microbiological analysis of total viable counts (TVC) and lactic acid bacteria (LAB). Qualitative interpretation of spectral data was based on a sensory evaluation, using a hedonic scale, classifying the samples in three quality classes, fresh, semi-fresh and spoiled. The scope of the modeling approach was to discriminate the ham slices in their respective quality class and additionally to predict the microbial population directly from spectral data. The results obtained demonstrated that the processing of the samples affected the performance of classification in the sensory classes, with better results observed in the case of for ham slices packed with probiotic supplemented (PS) edible films and of control samples without HPP. The performance of PLS regression models on providing quantitative estimations of microbial counts were based on specific figures of merit (bias factor, accuracy factor, root mean square error, percentage of prediction error). Bias and accuracy factors were close to unity for both microbial groups tested for samples without HPP, whereas for HPP treated samples the values of these indices ranged from 0.963 to 1.332, depending on the case and indice. The results of this study demonstrated for the first time that although FTIR can be used reliably for the rapid assessment of sliced ham, additional processes such as HPP can affect its performance., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

In recent years, fraud detection has become a major priority for food authorities, as fraudulent practices can have various economic and safety consequences. This work explores ways of identifying frozen-then-thawed minced beef labeled as fresh in a rapid, large-scale and cost-effective way. For this reason, freshly-ground beef was purchased from seven separate shops at different times, divided in fifteen portions and placed in Petri dishes. Multi-spectral images and FTIR spectra of the first five were immediately acquired while the remaining were frozen (-20°C) and stored for 7 and 32days (5 samples for each time interval). Samples were thawed and subsequently subjected to similar data acquisition. In total, 105 multispectral images and FTIR spectra were collected which were further analyzed using partial least-squares discriminant analysis and support vector machines. Two meat batches (30 samples) were reserved for independent validation and the remaining five batches were divided in training and test set (75 samples). Results showed 100% overall correct classification for test and external validation MSI data, while FTIR data yielded 93.3 and 96.7% overall correct classification for FTIR test set and external validation set respectively., (Copyright © 2017 Elsevier Ltd. All rights reserved.)