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

Detection of deltamethrin remains in mint with an electronic device coupled to chemometric methods

This article describes the possibility of an electronic device coupled with chemometric methods to detect and discriminate between mint treated with an insecticide containing deltamethrin and the untreated mint. A multisensor system is designed and realized mainly by a commercial metal oxide (MOS) gas sensors array, a data acquisition board, and a personal computer coupled with chemometric methods to achieve the objective. In each experiment, data were collected for 510 s using the multi-sensor system. Then, the principal component analysis (PCA) statistical data projection method and the support vector machine (SVM) machine learning method were exploited to prove the ability of our laboratory prototype to differentiate untreated mint from deltamethrin mint treated. The data projection with principal component analysis algorithm indicates that this method can classify the data with 98% of the variance by the first three main components (PC1, PC2, and PC3) with remarkable separation between mint groups while that the machine support vector (SVM) method was able to distinguish samples with a success rate of 95%. As such, this work offers the ability to identify the mint treated from untreated one using a simple, fast, and inexpensive multi-sensor system

Performance evaluation of machine learning algorithms for meat freshness assessment

In meat industry, a non-destructive evaluation and prediction of meat quality attributes is highly required. Artificial vision technology is a powerful and widely used tool for meat quality evaluation because of reliability, reproducibility, non-invasiveness, and non-destructiveness. Machine learning methods are a fundamental and crucial part of artificial vision technology. Their choice is critical in determining successfully the quality of meat. The goal of this paper was to compare the performance of three artificial intelligence-based methods to evaluate the beef meat freshness. In this research, a dataset of beef meat samples images was used to extract the color and texture features. Different methods including the support vector machines (SVM), k-nearest neighbor (KNN), and naïve Bayes (NB) algorithms were applied to determine the freshness of samples. The accuracy rates of KNN, SVM and NB algorithms were obtained about 92.59%, 90.12% and 87.65%, respectively. The results show that the KNN provides the highest classification rates against SVM and NB algorithms

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). 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

Monitoring the Freshness of Moroccan Sardines with a Neural-Network Based Electronic Nose

An electronic nose was developed and used as a rapid technique to classify thefreshness of sardine samples according to the number of days spent under cold storage (4 ±1°C, in air). The volatile compounds present in the headspace of weighted sardine sampleswere introduced into a sensor chamber and the response signals of the sensors wererecorded as a function of time. Commercially available gas sensors based on metal oxidesemiconductors were used and both static and dynamic features from the sensorconductance response were input to the pattern recognition engine. Data analysis wasperformed by three different pattern recognition methods such as probabilistic neuralnetworks (PNN), fuzzy ARTMAP neural networks (FANN) and support vector machines(SVM). The objective of this study was to find, among these three pattern recognitionmethods, the most suitable one for accurately identifying the days of cold storage undergoneby sardine samples. The results show that the electronic nose can monitor the freshness ofsardine samples stored at 4°C, and that the best classification and prediction are obtainedwith SVM neural network. The SVM approach shows improved classificationperformances, reducing the amount of misclassified samples down to 3.75 %

Full Paper Monitoring the Freshness of Moroccan Sardines with a Neural-Network Based Electronic Nose

Abstract: An electronic nose was developed and used as a rapid technique to classify the freshness of sardine samples according to the number of days spent under cold storage (4 ± 1°C, in air). The volatile compounds present in the headspace of weighted sardine samples were introduced into a sensor chamber and the response signals of the sensors were recorded as a function of time. Commercially available gas sensors based on metal oxide semiconductors were used and both static and dynamic features from the sensor conductance response were input to the pattern recognition engine. Data analysis was performed by three different pattern recognition methods such as probabilistic neural networks (PNN), fuzzy ARTMAP neural networks (FANN) and support vector machines (SVM). The objective of this study was to find, among these three pattern recognition methods, the most suitable one for accurately identifying the days of cold storage undergone by sardine samples. The results show that the electronic nose can monitor the freshness of sardine samples stored at 4°C, and that the best classification and prediction are obtained with SVM neural network. The SVM approach shows improved classificatio

Selectivity Enhancement in Multisensor Systems Using Flow Modulation Techniques

sensor