An Electronic Nose for Reliable Measurement and Correct Classification of Beverages

This paper reports the design of an electronic nose (E-nose) prototype for reliable measurement and correct classification of beverages. The prototype was developed and fabricated in the laboratory using commercially available metal oxide gas sensors and a temperature sensor. The repeatability, reproducibility and discriminative ability of the developed E-nose prototype were tested on odors emanating from different beverages such as blackcurrant juice, mango juice and orange juice, respectively. Repeated measurements of three beverages showed very high correlation (r > 0.97) between the same beverages to verify the repeatability. The prototype also produced highly correlated patterns (r > 0.97) in the measurement of beverages using different sensor batches to verify its reproducibility. The E-nose prototype also possessed good discriminative ability whereby it was able to produce different patterns for different beverages, different milk heat treatments (ultra high temperature, pasteurization) and fresh and spoiled milks. The discriminative ability of the E-nose was evaluated using Principal Component Analysis and a Multi Layer Perception Neural Network, with both methods showing good classification results

Sensor characterization for multisensor odor-discrimination system

In recent years, with the advent of new and cheaper sensors, the use of olfactory systems in homes, industries, and hospitals has a new start. Multisensor systems can improve the ability to distinguish between complex mixtures of volatile substances. To develop multisensor systems that are accurate and reliable, it is important to take into account the anomalies that may arise because of electronic instabilities, types of sensors, and air flow. In this approach, 32 metal oxide semiconductor sensors of 7 different types and operating at different temperatures have been used to develop a multisensor olfactory system. Each type of sensor has been characterized to select the most suitable temperature combinations. In addition, a prechamber has been designed to ensure a good air flow from the sample to the sensing area. The multisensor system has been tested with good results to perform multidimensional information detection of two fruits, based on obtaining sensor matrix data, extracting three features parameters from each sensor curve and using these parameters as the input to a pattern recognition system. (C) 2012 Elsevier B.V. All rights reserved.Cueto Belchí, AD.; Rothpfeffer, N.; Pelegrí Sebastiá, J.; Chilo, J.; García Rodríguez, D.; Sogorb Devesa, TC. (2013). Sensor characterization for multisensor odor-discrimination system. Sensors and Actuators A: Physical. 191:68-72. doi:10.1016/j.sna.2012.11.039S687219

A Wireless Electronic Nose System Using a Fe2O3 Gas Sensing Array and Least Squares Support Vector Regression

This paper describes the design and implementation of a wireless electronic nose (WEN) system which can online detect the combustible gases methane and hydrogen (CH4/H2) and estimate their concentrations, either singly or in mixtures. The system is composed of two wireless sensor nodes—a slave node and a master node. The former comprises a Fe2O3 gas sensing array for the combustible gas detection, a digital signal processor (DSP) system for real-time sampling and processing the sensor array data and a wireless transceiver unit (WTU) by which the detection results can be transmitted to the master node connected with a computer. A type of Fe2O3 gas sensor insensitive to humidity is developed for resistance to environmental influences. A threshold-based least square support vector regression (LS-SVR)estimator is implemented on a DSP for classification and concentration measurements. Experimental results confirm that LS-SVR produces higher accuracy compared with artificial neural networks (ANNs) and a faster convergence rate than the standard support vector regression (SVR). The designed WEN system effectively achieves gas mixture analysis in a real-time process

Metal Oxide Sensors for Electronic Noses and Their Application to Food Analysis

Electronic noses (E-noses) use various types of electronic gas sensors that have partial specificity. This review focuses on commercial and experimental E-noses that use metal oxide semi-conductors. The review covers quality control applications to food and beverages, including determination of freshness and identification of contaminants or adulteration. Applications of E-noses to a wide range of foods and beverages are considered, including: meat, fish, grains, alcoholic drinks, non-alcoholic drinks, fruits, milk and dairy products, olive oils, nuts, fresh vegetables and eggs

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

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