An Extended NRBF Model for the Detection of Meat Spoilage

A fast non-invasive detection of spoilage microorganisms in meat, using Fourier transform infrared spectroscopy (FTIR) and Extended Normalized Radial Basis Function neural networks has been proposed in this paper. The aim is to associate spectral data with microbiological data (log counts), for Total Viable Counts, Pseudomonas spp., Lactic Acid Bacteria and Enterobacteriaceae by predicting their micro-biological population from FTIR spectra. The dimensionality reduction of spectral data has been explored by the implementation of a fuzzy principal component algorithm, while produced results confirmed the superiority of the proposed method compared to multilayer perceptron neural networks used recently in the area of food microbiology

An Asymmetric Neuro-Fuzzy Model for the Detection of Meat Spoilage

In food industry, quality and safety parameters are direct related with consumers’ health condition. There is a growing interest in developing non-invasive sensorial techniques that have the capability of predicting quality attributes in realtime operation. Among other detection methodologies, Fourier transform infrared (FTIR) spectroscopy has been widely used for rapid inspection of various food products. In this paper, an advanced clustering-based neurofuzzy identification model has been developed to detect meat spoilage microorganisms during aerobic storage at various temperatures, utilizing FTIR spectra. A clustering scheme has been utilized as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been used in the fuzzification part of the model. The proposed model not only classifies meat samples in their respective quality class (i.e. fresh, semi-fresh and spoiled), but also predicts their associated microbiological population directly from FTIR spectra. Results verified the superiority of the proposed scheme against the adaptive neurofuzzy inference system, multilayer perceptron and partial least squares in terms of prediction accuracy

Neuro-Fuzzy Based Intelligent Approaches to Nonlinear System Identification and Forecasting

Nearly three decades back nonlinear system identification consisted of several ad-hoc approaches, which were restricted to a very limited class of systems. However, with the advent of the various soft computing methodologies like neural networks and the fuzzy logic combined with optimization techniques, a wider class of systems can be handled at present. Complex systems may be of diverse characteristics and nature. These systems may be linear or nonlinear, continuous or discrete, time varying or time invariant, static or dynamic, short term or long term, central or distributed, predictable or unpredictable, ill or well defined. Neurofuzzy hybrid modelling approaches have been developed as an ideal technique for utilising linguistic values and numerical data. This Thesis is focused on the development of advanced neurofuzzy modelling architectures and their application to real case studies. Three potential requirements have been identified as desirable characteristics for such design: A model needs to have minimum number of rules; a model needs to be generic acting either as Multi-Input-Single-Output (MISO) or Multi-Input-Multi-Output (MIMO) identification model; a model needs to have a versatile nonlinear membership function. Initially, a MIMO Adaptive Fuzzy Logic System (AFLS) model which incorporates a prototype defuzzification scheme, while utilising an efficient, compared to the Takagi–Sugeno–Kang (TSK) based systems, fuzzification layer has been developed for the detection of meat spoilage using Fourier transform infrared (FTIR) spectroscopy. The identification strategy involved not only the classification of beef fillet samples in their respective quality class (i.e. fresh, semi-fresh and spoiled), but also the simultaneous prediction of their associated microbiological population directly from FTIR spectra. In the case of AFLS, the number of memberships for each input variable was directly associated to the number of rules, hence, the “curse of dimensionality” problem was significantly reduced. Results confirmed the advantage of the proposed scheme against Adaptive Neurofuzzy Inference System (ANFIS), Multilayer Perceptron (MLP) and Partial Least Squares (PLS) techniques used in the same case study. In the case of MISO systems, the TSK based structure, has been utilized in many neurofuzzy systems, like ANFIS. At the next stage of research, an Adaptive Fuzzy Inference Neural Network (AFINN) has been developed for the monitoring the spoilage of minced beef utilising multispectral imaging information. This model, which follows the TSK structure, incorporates a clustering pre-processing stage for the definition of fuzzy rules, while its final fuzzy rule base is determined by competitive learning. In this specific case study, AFINN model was also able to predict for the first time in the literature, the beef’s temperature directly from imaging information. Results again proved the superiority of the adopted model. By extending the line of research and adopting specific design concepts from the previous case studies, the Asymmetric Gaussian Fuzzy Inference Neural Network (AGFINN) architecture has been developed. This architecture has been designed based on the above design principles. A clustering preprocessing scheme has been applied to minimise the number of fuzzy rules. AGFINN incorporates features from the AFLS concept, by having the same number of rules as well as fuzzy memberships. In spite of the extensive use of the standard symmetric Gaussian membership functions, AGFINN utilizes an asymmetric function acting as input linguistic node. Since the asymmetric Gaussian membership function’s variability and flexibility are higher than the traditional one, it can partition the input space more effectively. AGFINN can be built either as an MISO or as an MIMO system. In the MISO case, a TSK defuzzification scheme has been implemented, while two different learning algorithms have been implemented. AGFINN has been tested on real datasets related to electricity price forecasting for the ISO New England Power Distribution System. Its performance was compared against a number of alternative models, including ANFIS, AFLS, MLP and Wavelet Neural Network (WNN), and proved to be superior. The concept of asymmetric functions proved to be a valid hypothesis and certainly it can find application to other architectures, such as in Fuzzy Wavelet Neural Network models, by designing a suitable flexible wavelet membership function. AGFINN’s MIMO characteristics also make the proposed architecture suitable for a larger range of applications/problems

Applications of Infrared and Raman Spectroscopies to Probiotic Investigation

In this review, we overview the most important contributions of vibrational spectroscopy based techniques in the study of probiotics and lactic acid bacteria. First, we briefly introduce the fundamentals of these techniques, together with the main multivariate analytical tools used for spectral interpretation. Then, four main groups of applications are reported: (a) bacterial taxonomy (Subsection 4.1); (b) bacterial preservation (Subsection 4.2); (c) monitoring processes involving lactic acid bacteria and probiotics (Subsection 4.3); (d) imaging-based applications (Subsection 4.4). A final conclusion, underlying the potentialities of these techniques, is presented

Applications of Infrared and Raman Spectroscopies to Probiotic Investigation

In this review, we overview the most important contributions of vibrational spectroscopy based techniques in the study of probiotics and lactic acid bacteria. First, we briefly introduce the fundamentals of these techniques, together with the main multivariate analytical tools used for spectral interpretation. Then, four main groups of applications are reported: (a) bacterial taxonomy (Subsection 4.1); (b) bacterial preservation (Subsection 4.2); (c) monitoring processes involving lactic acid bacteria and probiotics (Subsection 4.3); (d) imaging-based applications (Subsection 4.4). A final conclusion, underlying the potentialities of these techniques, is presented.Facultad de Ciencias Exacta

Recent development in electronic nose data processing for beef quality assessment

Beef is kind of perishable food that easily to decay. Hence, a rapid system for beef quality assessment is needed to guarantee the quality of beef. In the last few years, electronic nose (e-nose) is developed for beef spoilage detection. In this paper, we discuss the challenges of e-nose application to beef quality assessment, especially in e-nose data processing. We also provide a summary of our previous studies that explains several methods to deal with gas sensor noise, sensor array optimization problem, beef quality classification, and prediction of the microbial population in beef sample. This paper might be useful for researchers and practitioners to understand the challenges and methods of e-nose data processing for beef quality assessment

Authentication and quality assessment of meat products by fourier-transform infrared (FTIR) Spectroscopy

These days, food safety is getting more attention than in the recent past due to consumer awareness, regulations, and industrial competition to offer best quality products. Meat and meat products are very valuable but highly perishable. There is a need for reliable assessment techniques to ensure the safety and quality of these products throughout their shelf life. Classical analytical methods have been replaced with alternative, rapid, simple, and noninvasive methods to enhance productivity and profitability in the meat supply chain. Fourier-transform infrared (FTIR) spectroscopy has become a valuable analytical technique for structural or functional studies related to foods as a rapid, nondestructive, cost-efficient, and sensitive physicochemical fingerprinting method. This technique is readily applicable for routine quality control or industrial applications with a high degree of confidence. FTIR spectroscopy coupled with chemometrics has drawn attention to quality control, safety assessment, and authentication purposes in the meat and meat products domain. This review covers fundamental knowledge on FTIR spectroscopy coupled with chemometric techniques, as well as major applications of this robust method in meat science and technology for adulteration detection, monitoring biochemical and microbiological spoilage and shelf life, determining changes in chemical components such as proteins and lipids

An Intelligent Decision Support System for the Detection of Meat Spoilage using Multispectral Images

In food industry, quality and safety are considered important issues worldwide that are directly related to health and social progress. The use of vision technology for quality testing of food production has the obvious advantage of being able to continuously monitor a production using non-destructive methods, thus increasing the quality and minimizing cost. The performance of an intelligent decision support system has been evaluated in monitoring the spoilage of minced beef stored either aerobically or under modified atmosphere packaging, at different storage temperatures (0, 5, 10, and 15 °C) utilising multispectral imaging information. This paper utilises a neuro-fuzzy model which incorporates a clustering pre-processing stage for the definition of fuzzy rules, while its final fuzzy rule base is determined by competitive learning. Initially, meat samples are classified according to their storage conditions, while identification models are then utilised for the prediction of the Total Viable Counts of bacteria. The innovation of the proposed approach is further extended to the identification of the temperature used for storage, utilizing only imaging spectral information. Results indicated that spectral information in combination with the proposed modelling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage

Quantifying meat spoilage with an array of biochemical indicators

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. It is crucial to validate and establish new rapid methods for the accurate detection of microbial spoilage of meats. In the current thesis, the microbial association of meat was monitored in parallel with the chemical changes, pH measurements and sensory analysis. Several chemical analytical techniques were applied to explore their dynamics on quantifying spoilage indicators and evaluate the shelf life of meat products. The applied analytical methods used were Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy, image analysis, high performance liquid chromatography (HPLC) and gas chromatography/mass spectroscopy (GC/MS). The first component of the study was designed to evaluate the potential of FTIR spectroscopy as a rapid, reagent-less and non-destructive analytical technique in estimating the freshness and shelf life of beef. For this reason, minced beef samples survey from the Greek market), beef fillet samples stored aerobically (0, 5, 10, 15 and 20ºC) and minced beef samples stored aerobically, under modified atmosphere packaging (MAP) and active packaging (0, 5, 10, and 15ºC), were analysed with FTIR. The statistical analysis from the survey revealed that the impact of the market type, the packaging type, the day and the season of purchase had a significant effect on the microbial association of mince. Furthermore, the Principal Components Analysis (PCA) and Factorial Discriminant Analysis (FDA), applied to the FTIR spectral data, showed discrimination of the samples based on freshness, packaging type, the day and season of purchase. The validated overall classification accuracies VCA) were 61.7% for the freshness, 79.2% for the packaging 80.5% for the season and 61.7% for the day of purchase. The shelf life of beef fillets and minced beef was evaluated and correlated with FTIR spectral data. This analysis revealed discrimination of the samples regarding their freshness (VCA 81.6% for the fillets, 76.34% for the mince), their storage temperature (VCA 55.3% and 88.1% for the fillets and mince, respectively) and the packaging type (VCA 92.5% for the mince). Moreover, estimations of the different microbial populations using Partial Least Squares Regression (PLS-R) were demonstrated (e.g. Total viable counts-TVC: RMSE 1.34 for the beef fillets and 0.72 for the mince). Cont/d.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

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 microbiolog