Identification of meat spoilage by FTIR spectroscopy and neural networks

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid determination of meat spoilage, Fourier transform infrared (FTIR) spectroscopy technique, with the help of advanced learning-based methods, was attempted in this work. FTIR spectra were obtained from the surface of beef samples during aerobic storage at various temperatures, while a microbiological analysis had identified the population of Total viable counts. A fuzzy principal component algorithm has been also developed to reduce the dimensionality of the spectral data. The results confirmed the superiority of the adopted scheme compared to the partial least squares technique, currently used in food microbiology.

A Fuzzy-Wavelet Neural Network Model for the Detection of Meat Spoilage using an Electronic Nose

Food product safety is one of the most promising areas for the application of electronic noses. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). This paper proposes a fuzzy-wavelet neural network model which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modeling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results indicated that the proposed modeling scheme could be considered as a valuable detection methodology in food microbiology

A Rapid Detection of Meat Spoilage using an Electronic Nose and Fuzzy-Wavelet systems

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid detection of meat spoilage microorganisms during aerobic or modified atmosphere storage, an electronic nose with the aid of fuzzy wavelet network has been considered in this research. The proposed model incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results against neural networks and neurofuzzy systems indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology

Neuro-Fuzzy based Identification of Meat Spoilage using an Electronic Nose

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). An adaptive fuzzy logic system model that utilizes a prototype defuzzification scheme has been developed to classify beef samples in their respective quality class and to predict their associated microbiological population directly from volatile compounds fingerprints. Results confirmed the superiority of the adopted methodology and indicated that volatile information in combination with an efficient choice of a modeling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage

Reduction of trimethylamine N-oxide to trimethylamine by the human gut microbiota: supporting evidence for ‘metabolic retroversion’

Dietary sources of methylamines such as choline, trimethylamine (TMA), trimethylamine N-oxide (TMAO), phosphatidylcholine (PC) and carnitine are present in a number of foodstuffs, including meat, fish, nuts and eggs. It is recognized that the gut microbiota is able to convert choline to TMA in a fermentation-like process. Similarly, PC and carnitine are converted to TMA by the gut microbiota. It has been suggested that TMAO is subject to ‘metabolic retroversion’ in the gut (i.e. it is reduced to TMA by the gut microbiota, with this TMA being oxidized to produce TMAO in the liver). Sixty-six strains of human faecal and caecal bacteria were screened on solid and liquid media for their ability to utilize trimethylamine N-oxide (TMAO), with metabolites in spent media profiled by Proton Nuclear Magnetic Resonance (1H NMR) spectroscopy. Enterobacteriaceae produced mostly TMA from TMAO, with caecal/small intestinal isolates of Escherichia coli producing more TMA than their faecal counterparts. Lactic acid bacteria (enterococci, streptococci, bifidobacteria) produced increased amounts of lactate when grown in the presence of TMAO, but did not produce large amounts of TMA from TMAO. The presence of TMAO in media increased the growth rate of Enterobacteriaceae; while it did not affect the growth rate of lactic acid bacteria, TMAO increased the biomass of these bacteria. The positive influence of TMAO on Enterobacteriaceae was confirmed in anaerobic, stirred, pH-controlled batch culture fermentation systems inoculated with human faeces, where this was the only bacterial population whose growth was significantly stimulated by the presence of TMAO in the medium. We hypothesize that dietary TMAO is used as an alternative electron acceptor by the gut microbiota in the small intestine/proximal colon, and contributes to microbial population dynamics upon its utilization and retroversion to TMA, prior to absorption and secondary conversion to TMAO by hepatic flavin-containing monooxygenases. Our findings support the idea that oral TMAO supplementation is a physiologically-stable microbiota-mediated strategy to deliver TMA at the gut barrier.