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

Supercritical Fluid Extraction Kinetics of Cherry Seed Oil: Kinetics Modeling and ANN Optimization

This study was primarily focused on the supercritical fluid extraction (SFE) of cherry seed oil and the optimization of the process using sequential extraction kinetics modeling and artificial neural networks (ANN). The SFE study was organized according to Box-Behnken design of experiment, with additional runs. Pressure, temperature and flow rate were chosen as independent variables. Five well known empirical kinetic models and three mass-transfer kinetics models based on the Sovová’s solution of SFE equations were successfully applied for kinetics modeling. The developed mass-transfer models exhibited better fit of experimental data, according to the calculated statistical tests (R², SSE and AARD). The initial slope of the SFE curve was evaluated as an output variable in the ANN optimization. The obtained results suggested that it is advisable to lead SFE process at an increased pressure and CO₂ flow rate with lower temperature and particle size values to reach a maximal initial slope.

Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods

Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

Antimicrobial Activity of Oregano Essential Oil Incorporated in Sodium Alginate Edible Films: Control of Listeria monocytogenes and Spoilage in Ham Slices Treated with High Pressure Processing

The aim of the study was to evaluate the efficacy of oregano essential oil (OEO) incorporated in Na-alginate edible films when applied to sliced ham inoculated with a cocktail of Listeria monocytogenes strains, with or without pretreatment by high pressure processing (HPP). Microbiological, physicochemical and sensory analyses (in Listeria-free slices) were performed, while, the presence/absence and the relative abundance of each Listeria strain, was monitored by pulsed field gel electrophoresis (PFGE). The OEO incorporation in the films, caused approximately 1.5 log reduction in Listeria population at 8 and 12 °C at the end of the storage period, and almost 2.5 log reduction at 4 °C. The HPP treatment caused 1 log reduction to the initial Listeria population, while levels kept on decreasing throughout the storage for all the tested temperatures. The pH of the samples was higher in the cases where HPP was involved, and the samples were evaluated as less spoiled. Furthermore, the presence of OEO in the films resulted in color differences compared to the control samples, whilst the aroma of these samples was improved. In conclusion, the combined application of HPP and OEO edible films on the slices, led to a significant reduction or absence of the pathogen.

Use of Fourier transform infrared spectroscopy for monitoring the shelf life of ham slices packed with probiotic supplemented edible films after treatment with high pressure processing

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.

Identification of cattle, llama and horse meat by near infrared reflectance or transflectance spectroscopy

Visible and near infrared reflectance spectroscopy (VIS-NIRS) was used to discriminate meat and meat juices from three livestock species. In a first trial, samples of Longissimus lumborum muscle, corresponding to beef (31) llamas (21) and horses (27), were homogenised and their spectra collected in reflectance (NIRSystems 6500 scanning monochromator, in the range of 400-2500 nm). In the second trial, samples of meat juice (same muscle) from the same species (20 beef, 19 llama and 19 horse) were scanned in folded transmission (transflectance). Discriminating models (PLS regression) were developed against "dummy" variables, testing different mathematical treatments of the spectra. Best models indentified the species of almost all samples by their meat (reflectance) or meat juice (transflectance) spectra. A few (three of beef and one of llama, for meat samples; one of beef and one of horse, for juice samples) were classified as uncertain. It is concluded that NIRS is an effective tool to recognise meat and meat juice from beef, llama and horses.

Evaluation of the effect of a maternal rearing system on the odour profile of meat from suckling lamb

The aim was to evaluate the effect of maternal rearing system on the chemical composition and odour profile of meat from suckling lamb. Twenty-four single male suckling lambs raised exclusively on maternal milk came from two rearing systems (extensive and intensive), and from Castellana and Churra breeds. The fatty acid composition of lamb meat from both breeds was affected by the rearing system, and Churra lamb reared under the extensive system had noticeable changes in odour attributes as detected by panellists and an electronic nose. Using partial least squares-discriminant analysis, the electronic nose was able to discriminate accurately between samples of Churra and Castellana meat according to their production system. However, it was not able to correctly classify meat samples from the extensive and intensive systems when both breeds were analysed together. The results suggest a significant effect of the rearing system and breed on suckling lamb meat quality traits.