Neural-network-integrated electronic nose system for identification of spoiled beef

On-Chip Annealing Using Embedded Micro-Heater for Highly Sensitive and Selective Gas Detection

The demand for gas sensing systems that enable fast and precise gas recognition is growing rapidly. However, substantial challenges arise from the complex fabrication process of sensor arrays, time-consuming data transmission to an external processor, and high energy consumption in multi-stage data processing. In this study, a gas sensing system using on-chip annealing for fast and power-efficient gas detection is proposed. By utilizing a micro-heater embedded in the gas sensor, the sensing material of adjacent sensors in the same substrate can be easily varied without further fabrication steps. The response to oxidizing gas is constrained in metal oxide (MOX) sensing material with small grain sizes, as the depletion width of grain cannot extend beyond the grain size during the gas reaction. On the other hand, the response to reducing gases and humidity, which decrease the depletion width, is less affected by grain sizes. A readout circuit integrating a differential amplifier and dual FET-type gas sensors effectively emphasizes the response to oxidizing gases by canceling the response to reducing gases and humidity. The selective on-chip annealing method is applicable to various MOX sensing materials, demonstrating its potential for application in commercial fields due to its simplicity and expandability.

Recent advances and challenges in the analysis of natural toxins

Natural toxins (NTs) are poisonous secondary metabolites produced by living organisms developed to ward off predators. Especially low molecular weight NTs (MW<∼1 kDa), such as mycotoxins, phycotoxins, and plant toxins, are considered an important and growing food safety concern. Therefore, accurate risk assessment of food and feed for the presence of NTs is crucial. Currently, the analysis of NTs is predominantly performed with targeted high pressure liquid chromatography tandem mass spectrometry (HPLC-MS/MS) methods. Although these methods are highly sensitive and accurate, they are relatively expensive and time-consuming, while unknown or unexpected NTs will be missed. To overcome this, novel on-site screening methods and non-targeted HPLC high resolution mass spectrometry (HRMS) methods have been developed. On-site screening methods can give non-specialists the possibility for broad "scanning" of potential geographical regions of interest, while also providing sensitive and specific analysis at the point-of-need. Non-targeted chromatography-HRMS methods can detect unexpected as well as unknown NTs and their metabolites in a lab-based approach. The aim of this chapter is to provide an insight in the recent advances, challenges, and perspectives in the field of NTs analysis both from the on-site and the laboratory perspective.

Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types

Ensuring safe food supplies has recently become a serious problem all over the world. Controlling the quality, spoilage, and standing time for products with a short shelf life is a quite difficult problem. However, electronic noses can make all these controls possible. In this study, which aims to develop a different approach to the solution of this problem, electronic nose data obtained from 12 different beef cuts were classified. In the dataset, there are four classes (1: excellent, 2: good, 3: acceptable, and 4: spoiled) indicating beef quality. The classifications were performed separately for each cut and all cut shapes. The ANOVA method was used to determine the active features in the dataset with data for 12 features. The same classification processes were carried out by using the three active features selected by the ANOVA method. Three different machine learning methods, Artificial Neural Network, K Nearest Neighbor, and Logistic Regression, which are frequently used in the literature, were used in classifications. In the experimental studies, a classification accuracy of 100% was obtained as a result of the classification performed with ANN using the data obtained by combining all the tables in the dataset.

Bioactive Attributes and Analysis of Electronic Nose Feature Signals of Colombian Stingless Bees Propolis

The volatile and non-volatile chemical composition and bioactivity of propolis from the species Apis mellifera has been widely studied, but there is very little knowledge regarding propolis of other bee species, which ultimately hinders their differentiation and potential use. In this work, 53 propolis samples of A. mellifera and four stingless bee species (Frieseomielitta sp., Melipona eburnea, Melipona sp., and Trigona sp.) were collected in Colombia. An electronic nose with 10 metal oxide semiconductor sensors (MOS) was used to generate a pattern of the representative volatile compounds of the samples. Ethanolic extracts were obtained to assess their antioxidant activity towards DPPH radical and ABTS radical cation, total phenolics, and color (CIELAB space). The results showed an overall similarity of the aromatic profiles between species. The antioxidant activity of Frieseomielitta sp. propolis was higher than that of A. mellifera and the other species, in correspondence with a higher phenolic content. CIELAB color parameter b* was the most differentiating variable among samples, indicating a variation of propolis colors between red and yellow. By combining the data from physico-chemical analysis and aromatic profile, it was possible to differentiate the propolis from each bee species, with the exception of those from Melipona sp. and Trigona sp., indicating their similarity. These results have practical significance since they are a starting point to recognizing and valuing native stingless bee propolis and their bioactive potential, which, in addition to geographical differentiation and further quality parameters evaluation, will enhance their commercial exploitation.

Artificial olfactory sensor technology that mimics the olfactory mechanism: a comprehensive review

Artificial olfactory sensors that recognize patterns transmitted by olfactory receptors are emerging as a technology for monitoring volatile organic compounds. Advances in statistical processing methods and data processing technology have made it possible to classify patterns in sensor arrays. Moreover, biomimetic olfactory recognition sensors in the form of pattern recognition have been developed. Deep learning and artificial intelligence technologies have enabled the classification of pattern data from more sensor arrays, and improved artificial olfactory sensor technology is being developed with the introduction of artificial neural networks. An example of an artificial olfactory sensor is the electronic nose. It is an array of various types of sensors, such as metal oxides, electrochemical sensors, surface acoustic waves, quartz crystal microbalances, organic dyes, colorimetric sensors, conductive polymers, and mass spectrometers. It can be tailored depending on the operating environment and the performance requirements of the artificial olfactory sensor. This review compiles artificial olfactory sensor technology based on olfactory mechanisms. We introduce the mechanisms of artificial olfactory sensors and examples used in food quality and stability assessment, environmental monitoring, and diagnostics. Although current artificial olfactory sensor technology has several limitations and there is limited commercialization owing to reliability and standardization issues, there is considerable potential for developing this technology. Artificial olfactory sensors are expected to be widely used in advanced pattern recognition and learning technologies, along with advanced sensor technology in the future.

A Comprehensive Insight on the Health Benefits and Phytoconstituents of Camellia sinensis and Recent Approaches for Its Quality Control

Tea, Camellia sinensis, which belongs to the family Theaceae, is a shrub or evergreen tree up to 16 m in height. Green tea is very popular because of its marked health benefits comprising its anticancer, anti-oxidant, and antimicrobial activities, as well as its effectiveness in reducing body weight. Additionally, it was recognized by Chinese people as an effective traditional drink required for the prophylaxis against many health ailments. This is due to the complex chemical composition of green tea, which comprises different classes of chemical compounds, such as polyphenols, alkaloids, proteins, minerals, vitamins, amino acids, and others. The beneficial health effects of green tea ultimately led to its great consumption and increase its liability to be adulterated by either low-quality or non-green tea products with concomitant decrease in activity. Thus, in this review, green tea was selected to highlight its health benefits and phytoconstituents, as well as recent approaches for its quality-control monitoring that guarantee its incorporation in many pharmaceutical industries. More research is needed to find out other more biological activities, active constituents, and other simple and cheap techniques for its quality assurance that ascertain the prevention of its adulteration.

Meat and Fish Freshness Assessment by a Portable and Simplified Electronic Nose System (Mastersense)

The evaluation of meat and fish quality is crucial to ensure that products are safe and meet the consumers’ expectation. The present work aims at developing a new low-cost, portable, and simplified electronic nose system, named Mastersense, to assess meat and fish freshness. Four metal oxide semiconductor sensors were selected by principal component analysis and were inserted in an “ad hoc” designed measuring chamber. The Mastersense system was used to test beef and poultry slices, and plaice and salmon fillets during their shelf life at 4 °C, from the day of packaging and beyond the expiration date. The same samples were tested for Total Viable Count, and the microbial results were used to define freshness classes to develop classification models by the K-Nearest Neighbours’ algorithm and Partial Least Square–Discriminant Analysis. All the obtained models gave global sensitivity and specificity with prediction higher than 83.3% and 84.0%, respectively. Moreover, a McNemar’s test was performed to compare the prediction ability of the two classification algorithms, which resulted in comparable values (p > 0.05). Thus, the Mastersense prototype implemented with the K-Nearest Neighbours’ model is considered the most convenient strategy to assess meat and fish freshness.

Freshness Evaluation of Three Kinds of Meats Based on the Electronic Nose

The aim of this study was to use an electronic nose set up in our lab to detect and predict the freshness of pork, beef and mutton. Three kinds of freshness, including fresh, sub-fresh and putrid, was established by human sensory evaluation and was used as a reference for the electronic nose’s discriminant factor analysis. The principal component analysis results showed the electronic nose could distinguish well pork, beef and mutton samples with different storage times. In the PCA figures, three kinds of meats samples all presented an approximate parabola trend during 7 days’ storage time. The discriminant factor analysis showed electronic nose could distinguish and judge well the freshness of samples (accuracy was 89.5%, 84.2% and 94.7% for pork, beef and mutton, respectively). Therefore, the electronic nose is promising for meat fresh detection application.

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.

Application of Electronic Nose for Measuring Total Volatile Basic Nitrogen and Total Viable Counts in Packaged Pork During Refrigerated Storage

The objective of this study was to predict the total viable counts (TVC) and total volatile basic nitrogen (TVB-N) in pork using an electronic nose (E-nose), and to assess the freshness of chilled pork during storage using different packaging methods, including pallet packaging (PP), vacuum packaging (VP), and modified atmosphere packaging (MAP, 40% O2 /40% CO2 /20% N2 ). Principal component analysis (PCA) was used to analyze the E-nose signals, and the results showed that the relationships between the freshness of chilled pork and E-nose signals could be distinguished in the loadings plots, and the freshness of chilled pork could be distributed along 2 first principal components. Multiple linear regression (MLR) was used to correlate TVC and TVB-N to E-nose signals. High F and R2 values were obtained in the MLR output of TVB-N (F = 32.1, 21.6, and 24.2 for PP [R2 = 0.93], VP [R2 = 0.94], and MAP [R2 = 0.95], respectively) and TVC (F = 34.2, 46.4, and 7.8 for PP [R2 = 0.98], VP [R2 = 0.89], and MAP [R2 = 0.85], respectively). The results of this study suggest that it is possible to use the E-nose technology to predict TVB-N and TVC for assessing the freshness of chilled pork during storage.

Responsive Food Packaging: Recent Progress and Technological Prospects

Responsive food packaging is an emerging field in food packaging research and the food industry. Unlike active packaging, responsive packaging systems react to stimuli in the food or the environment to enable real time food quality and food safety monitoring or remediation. This review attempts to define and clarify the different classes of food packaging technologies. Special emphasis is given to the description of responsive food packaging including its technical requirements, the state of the art in research and the current expanding market. The development and promises of stimuli responsive materials in responsive food packaging are addressed, along with current challenges and future directions to help translate research developments into commercial products.

Application of an electronic nose instrument to fast classification of Polish honey types

The paper presents practical utilization of an electronic nose prototype, based on the FIGARO semiconductor sensors, in fast classification of Polish honey types-acacia flower, linden flower, rape, buckwheat and honeydew ones. A set of thermostating modules of the prototype provided gradient temperature characteristics of barbotage-prepared gas mixtures and stable measurement conditions. Three chemometric data analysis methods were employed for the honey samples classification: principal component analysis (PCA), linear discriminant analysis (LDA) and cluster analysis (CA) with the furthest neighbour method. The investigation confirmed usefulness of this type of instrument in correct classification of all aforementioned honey types. In order to provide optimum measurement conditions during honey samples classification the following parameters were selected: volumetric flow rate of carrier gas-15 L/h, barbotage temperature-35 °C, time of sensor signal acquisition since barbotage process onset-60 s. Chemometric analysis allowed discrimination of three honey types using PCA and CA and all five honey types with LDA. The reproducibility of 96% of the results was within the range 4.9%-8.6% CV.

Advances in artificial olfaction: sensors and applications

The artificial olfaction, based on electronic systems (electronic noses), includes three basic functions that operate on an odorant: a sample handler, an array of gas sensors, and a signal-processing method. The response of these artificial systems can be the identity of the odorant, an estimate concentration of the odorant, or characteristic properties of the odour as might be perceived by a human. These electronic noses are bio inspired instruments that mimic the sense of smell. The complexity of most odorants makes characterisation difficult with conventional analysis techniques, such as gas chromatography. Sensory analysis by a panel of experts is a costly process since it requires trained people who can work for only relatively short periods of time. The electronic noses are easy to build, provide short analysis times, in real time and on-line, and show high sensitivity and selectivity to the tested odorants. These systems are non-destructive techniques used to characterise odorants in diverse applications linked with the quality of life such as: control of foods, environmental quality, citizen security or clinical diagnostics. However, there is much research still to be done especially with regard to new materials and sensors technology, data processing, interpretation and validation of results. This work examines the main features of modern electronic noses and their most important applications in the environmental, and security fields. The above mentioned main components of an electronic nose (sample handling system, more advanced materials and methods for sensing, and data processing system) are described. Finally, some interesting remarks concerning the strengths and weaknesses of electronic noses in the different applications are also mentioned.

The detection of foodborne bacteria on beef: the application of the electronic nose

This study aims to investigate the application of a fast electronic nose system (Cyranose 320) for detecting foodborne bacteria. The system proved to be very efficient in detecting microbes in beef and sausage samples. In the first part of the study, the total viable counts (TVC) from fresh and frozen beef samples were determined using the standard microbiological method and by the application of the electronic nose. The second part applied the electronic nose to beef before and after contamination with different bacterial pathogens separately: E. coli O157: H7, Salmonellatyphimurium 857, Staphylococcus aureus 29213 and Pseudomonas aeruginosa 27853. The results revealed that the Cyranose 320 can detect the TVC in different beef and sausage samples and quantify the volatile organic compounds produced at concentrations from 50 ppb to > 350 ppb. The concentrations of gases collected from the samples before and after separate contamination with these pathogenic bacteria were highly significantly correlated (P < 0.005). From this study one can conclude that the electronic nose system is a rapid way for detecting volatile organic compounds produced by foodborne bacteria that contaminate beef.

Electronic Nose Based on Nanomaterials: Issues, Challenges, and Prospects

The ability to precisely control the morphology and dimension coupled with the tunable surface reactivity has led to the widespread investigation of nanomaterials for various device applications. The associated high surface area to volume ratio implies that large numbers of atom are residing on the surface and are available for interaction. Accordingly, nanomaterials have demonstrated the potential to realize sensors with ultrahigh sensitivities and fast response kinetics. The smaller size further provides the possibility of miniaturization and integration of large number of devices. All these properties makes them an attractive candidate for the fabrication of electronic nose or e-nose. E-nose is an intelligent chemical-array sensor system that mimics the mammalian olfactory system. The present paper critically reviews the recent development in the field of nanomaterials based e-nose devices. In particular, this paper is focused on the description of nanomaterials for e-nose application, specifically on the promising approaches that are going to contribute towards the further development of this field. Various issues related to successful utilization of different nanomaterials for commercial application are discussed, taking help from the literature. The review concludes by briefing the important steps taken towards the commercialization and highlighting the loopholes that are still to be addressed.

Performance evaluation of multilayer perceptrons for discriminating and quantifying multiple kinds of odors with an electronic nose

This paper studies several types and arrangements of perceptron modules to discriminate and quantify multiple odors with an electronic nose. We evaluate the following types of multilayer perceptron. (A) A single multi-output (SMO) perceptron both for discrimination and for quantification. (B) An SMO perceptron for discrimination followed by multiple multi-output (MMO) perceptrons for quantification. (C) An SMO perceptron for discrimination followed by multiple single-output (MSO) perceptrons for quantification. (D) MSO perceptrons for discrimination followed by MSO perceptrons for quantification, called the MSO-MSO perceptron model, under the following conditions: (D1) using a simple one-against-all (OAA) decomposition method; (D2) adopting a simple OAA decomposition method and virtual balance step; and (D3) employing a local OAA decomposition method, virtual balance step and local generalization strategy all together. The experimental results for 12 kinds of volatile organic compounds at 85 concentration levels in the training set and 155 concentration levels in the test set show that the MSO-MSO perceptron model with the D3 learning procedure is the most effective of those tested for discrimination and quantification of many kinds of odors.

Electronic Nose for Microbiological Quality Control of Food Products

Electronic noses (ENs) have recently emerged as valuable candidates in various areas of food quality control and traceability, including microbial contamination diagnosis. In this paper, the EN technology for microbiological screening of food products is reviewed. Four paradigmatic and diverse case studies are presented: (a)Alicyclobacillusspp. spoilage of fruit juices, (b) early detection of microbial contamination in processed tomatoes, (c) screening of fungal and fumonisin contamination of maize grains, and (d) fungal contamination on green coffee beans. Despite many successful results, the high intrinsic variability of food samples together with persisting limits of the sensor technology still impairs ENs trustful applications at the industrial scale. Both advantages and drawbacks of sensor technology in food quality control are discussed. Finally, recent trends and future directions are illustrated.

Olfactory receptor-based polypeptide sensor for acetic acid VOC detection

Rapid detection of food-borne pathogens in packaged food products can prevent the spread of infectious diseases. This study investigates the application of novel sensing material that is sensitive to specific indicator volatile organic compound (VOC) related to Salmonella contamination in packaged meat. Specifically, the objective was to develop an olfactory receptor-based synthetic polypeptide sensor for the detecting acetic acid in low concentrations and at room temperature. Synthetic polypeptide was deposited on a quartz crystal microbalance (QCM) electrode and was evaluated for detecting acetic acid at 10-100 ppm. Developed sensor exhibited repeatable response to a particular concentration of acetic acid and displayed reproducibility among multiple sensors during acetic acid detection. Mean estimated lower detection limits of these sensors were about 1-3 ppm and linear calibration models showed linear relationships. Thus, the QCM sensors exhibited a great potential for detecting low concentrations of acetic acid at room temperature and can be used in the sensor array for packaged meat spoilage and contamination detection.

Invited review article: an odor-sensing system--powerful technique for foodstuff studies

This work examines gas sensor array technology combined with multivariate data processing methods and demonstrates a promising potential for rapid, non-destructive analysis of food. Main attention is focused on detailed description of sensor used in e-nose instruments, construction, and principle of operation of these systems. Moreover, this paper briefly reviews the progress in the field of artificial olfaction and future trends in electronic nose technology, namely, e-nose based on mass spectrometry. Further discussion concerns a comparison of artificial nose with gas chromatography-olfactometry and the application of e-nose instruments in different areas of food industry.

A bio-inspired two-layer multiple-walled carbon nanotube-polymer composite sensor array and a bio-inspired fast-adaptive readout circuit for a portable electronic nose

We report a fully integrated, portable, battery-operated electronic nose system comprising a bio-inspired two-layer multiple-walled carbon nanotube (MWNT)-polymer composite sensor array, a bio-inspired fast-adaptive readout circuit, and a microprocessor embedded with a pattern recognition algorithm. The two-layer MWNT-polymer composite sensor is simple to operate, and the membrane quality can be easily controlled. These two-layer membranes have improved sensitivity and stability. The fast-adaptive readout circuit responds to the sensor response, while tuning out the long-term constant background humidity, temperature, and odors. This portable electronic nose system successfully classified four complex alcohol samples 40 times for each sample; these samples were sake, sorghum liquor, medical liquor, and whisky.

Nondestructive methods for quality evaluation of livestock products

The muscles derived from livestock are highly perishable. Rapid and nondestructive methods are essential for quality assurance of such products. Potential nondestructive methods, which can supplement or replace many of traditional time consuming destructive methods, include colour and computer image analysis, NIR spectroscopy, NMRI, electronic nose, ultrasound, X-ray imaging and biosensors. These methods are briefly described and the research work involving them for products derived from livestock is reviewed. These methods will be helpful in rapid screening of large number of samples, monitoring distribution networks, quick product recall and enhance traceability in the value chain of livestock products. With new developments in the areas of basic science related to these methods, colour, image processing, NIR spectroscopy, biosensors and ultrasonic analysis are expected to be widespread and cost effective for large scale meat quality evaluation in near future.

Odorant binding protein based biomimetic sensors for detection of alcohols associated with Salmonella contamination in packaged beef

Detection of food-borne bacteria present in the food products is critical to prevent the spread of infectious diseases. Intelligent quality sensors are being developed for detecting bacterial pathogens such as Salmonella in beef. One of our research thrusts was to develop novel sensing materials sensitive to specific indicator alcohols at low concentrations. Present work focuses on developing olfactory sensors mimicking insect odorant binding protein to detect alcohols in low concentrations at room temperature. A quartz crystal microbalance (QCM) based sensor in conjunction with synthetic peptide was developed to detect volatile organic compounds indicative to Salmonella contamination in packaged beef. The peptide sequence used as sensing materials was derived from the amino acids sequence of Drosophila odorant binding protein, LUSH. The sensors were used to detect alcohols: 3-methyl-1-butanol and 1-hexanol. The sensors were sensitive to alcohols with estimated lower detection limits of <5 ppm. Thus, the LUSH-derived QCM sensors exhibited potential to detect alcohols at low ppm concentrations.

Identification of tea storage times by linear discrimination analysis and back-propagation neural network techniques based on the eigenvalues of principal components analysis of e-nose sensor signals

An electronic nose (E-nose) was employed to detect the aroma of green tea after different storage times. Longjing green tea dry leaves, beverages and residues were detected with an E-nose, respectively. In order to decrease the data dimensionality and optimize the feature vector, the E-nose sensor response data were analyzed by principal components analysis (PCA) and the five main principal components values were extracted as the input for the discrimination analysis. The storage time (0, 60, 120, 180 and 240 days) was better discriminated by linear discrimination analysis (LDA) and was predicted by the back-propagation neural network (BPNN) method. The results showed that the discrimination and testing results based on the tea leaves were better than those based on tea beverages and tea residues. The mean errors of the tea leaf data were 9, 2.73, 3.93, 6.33 and 6.8 days, respectively.

Conducting polymers in chemical sensors and arrays

The review covers main applications of conducting polymers in chemical sensors and biosensors. The first part is focused on intrinsic and induced receptor properties of conducting polymers, such as pH sensitivity, sensitivity to inorganic ions and organic molecules as well as sensitivity to gases. Induced receptor properties can be also formed by molecularly imprinted polymerization or by immobilization of biological receptors. Immobilization strategies are reviewed in the second part. The third part is focused on applications of conducting polymers as transducers and includes usual optical (fluorescence, SPR, etc.) and electrical (conductometric, amperometric, potentiometric, etc.) transducing techniques as well as organic chemosensitive semiconductor devices. An assembly of stable sensing structures requires strong binding of conducting polymers to solid supports. These aspects are discussed in the next part. Finally, an application of combinatorial synthesis and high-throughput analysis to the development and optimization of sensing materials is described.

Assessment of bioinspired models for pattern recognition in biomimetic systems

The increasing complexity of the artificial implementations of biological systems, such as the so-called electronic noses (e-noses) and tongues (e-tongues), poses issues in sensory feature extraction and fusion, drift compensation and pattern recognition, especially when high reliability is required. In particular, in order to achieve effective results, the pattern recognition system must be carefully designed. In order to investigate a novel biomimetic approach for the pattern recognition module of such systems, the classification capabilities of an artificial model inspired by the mammalian cortex, a cortical-based artificial neural network (CANN), are compared with several artificial neural networks present in the e-nose and e-tongue literature, a multilayer perceptron (MLP), a Kohonen self-organizing map (KSOM) and a fuzzy Kohonen self-organizing map (FKSOM). Each network was tested with large datasets coming from a conducting polymer-sensor-based e-nose and a composite array-based e-tongue. The comparison of results showed that the CANN model is able to strongly enhance the performances of both systems.

Electronic Nose Based on Metal Oxide Semiconductor Sensors as an Alternative Technique for the Spoilage Classification of Red Meat

The aim of the present study was to develop an electronic nose for the quality control of red meat. Electronic nose and bacteriological measurements are performed to analyse samples of beef and sheep meat stored at 4°C for up to 15 days. Principal component analysis (PCA) and support vector machine (SVM) based classification techniques are used to investigate the performance of the electronic nose system in the spoilage classification of red meats. The bacteriological method was selected as the reference method to consistently train the electronic nose system. The SVM models built classified meat samples based on the total microbial population into "unspoiled" (microbial counts < 6 log10 cfu/g) and "spoiled" (microbial counts ≥ 6 log10 cfu/g). The preliminary results obtained by the bacteria total viable counts (TVC) show that the shelf-life of beef and sheep meats stored at 4 °C are 7 and 5 days, respectively. The electronic nose system coupled to SVM could discriminate between unspoiled/ spoiled beef or sheep meats with a success rate of 98.81 or 96.43 %, respectively. To investigate whether the results of the electronic nose correlated well with the results of the bacteriological analysis, partial least squares (PLS) calibration models were built and validated. Good correlation coefficients between the electronic nose signals and bacteriological data were obtained, a clear indication that the electronic nose system can become a simple and rapid technique for the quality control of red meats.