Search query: ann in electronic noses

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abstract: Electronic Nose and Tongue in Food Science describes the electronic products of advanced chemical and physical sciences combined with intuitive integration of microprocessors, advanced bioinformatics and statistics. These include, for example, voltammetric, bio electronic, piezoelectric platforms made from a variety of components including, nanoparticles, enzyme biosensors, heavy metals, graphite epoxy composites, metal oxide semiconductors, microelectrodes, microfluidic channels, pre manufactured gas sensors, redox enzymes and others and is an ideal resource for understanding and utilizing their power in Food Science settings. Devices used to analyse one particular food item can theoretically be adapted for other food items or components. This does not just mean the re deploying the physical platforms but also the mode of bioinformatic and statistical analysis. This includes artificial neural networks (ANN) linear discriminant analysis (LDA) partial least squares (PLS) principal component analysis (PCA) etc. In other words, there is cross transference of chemistry, physics, concepts, techniques, findings and approaches from one food to another. Electronic noses and tongues are two of these devices but are advancing in application and importance. This book provides examples of the use of electronic noses and tongues to characterise components that contribute to sensory or compositional profiles, from ripening to harvesting and from storage of raw materials to packaging and consumption. These devises are suitable for high throughput analysis, quality control or to determine the nature and extent of spoilage and adulteration, and have also been used to ascertain the geographical origins of food and mixtures.Presents latest developments in the application of electronic nose and tongue technologies to a variety of food specific needsIncludes both electronic nose, electronic tongue and combined technology insightsEach chapter has sections on: The physical and chemical platforms; Analysis of specific foods; Applications to other foods and areas of food science
author_names: Rodriguez Mendez

Mariane dos Santos Aguiar Luz
title: Electronic Noses and Tongues in Food Science
year: 2016

abstract: In this paper, a hybrid electronic noses' system (HENS) based on MOS SAW detection units intended for lung cancer diagnosis is proposed. The MOS gas sensors are used to detect the VOC molecules with low molecular weight (LMW) and the SAW sensors are adopted for the detection of VOC with high molecular weight (HMW) Thus, the novel combination of these two kinds of gas sensors provides higher sensitivities to more of VOC species in breath than that of using only a single kind of sensor. The signals from MOS SAW detection units are then recognized by a multi model diagnosis method. Applying four algorithms, six models were established for diagnosis and tested by leave one out cross validation method. The model by artificial neural network (ANN) was selected as the best model to analyze breath samples. 89 clinical samples were tested with MOS SAW ANN diagnostic model, which takes the features derived from both the MOS and SAW sensors. It shows the highest sensitivity of 93.62% and the highest selectivity o.
author_names: Di Wang

Kai Yu

Yishan Wang

Yan-jie Hu

Cong Zhao

Lin Wang

Kejing Ying

Ping Wang
title: A HYBRID ELECTRONIC NOSES' SYSTEM BASED ON MOS SAW DETECTION UNITS INTENDED FOR LUNG CANCER DIAGNOSIS
year: 2012

abstract: In this paper our three recent achievements of electronic noses are reviewed with the emphasis on Artificial Neural Networks (ANNs) The back propagation algorithm has been used for identifying aromas of alcoholic beverages, and Fuzzy Learning Vector Quantization algorithm has been developed and is found promising for odour discrimination. The third is the analog ANN hardware which can be used in a compact odour sensing system. Those algorithms and a hardware system are discussed with respects to odour or gas identification capability.
author_names: Toyosaka Moriizumi

Takamichi Nakamoto

Yuichi Sakuraba
title: Pattern Recognition in Electronic Noses by Artificial Neural Network Models
year: 1992

abstract: This work studies the changes along days of the aroma released from a flavour encapsulated in a polysaccharide gel matrix using the electronic nose methodology. The purpose is to explore the capacity of the sensor array to assign a pattern of aroma to the corresponding release day within a total period of five days. Different procedures of data treatment and analysis are compared in order to achieve the maximum of information of the system under study in conditions where the number of measurements is limited. Raw and normalized sensor signals are processed using various unsupervised and supervised data analysis algorithms such as Principal Component Analysis, Kohonen Self Organizing Maps, Cluster Analysis, Multiple Discriminant Analysis and two types of Artificial Neural Networks (BP ANN and RBF ANN) Accurate assignation of the number of release days is obtained with a successful classification up to four classes associated to samples at increasing days of aroma release. The relative advantages and drawbacks of the different procedures and data manipulations are discussed.
author_names: Silvio D Rodriguez

Maria Eugenia Monge

Alejandro Cesar Olivieri

R Martin Negri

Delia L Bernik
title: Time dependence of the aroma pattern emitted by an encapsulated essence studied by means of electronic noses and chemometric analysis
year: 2010

abstract: Abstract Background An e nose or an e tongue is a group of gas sensors or chemical sensors that simulate human nose or human tongue. Both e nose and e tongue have shown great promise and utility in improving assessments of food quality characteristics compared with traditional detection methods. Scope and approach This review summarizes the application of e nose and e tongue in determining the quality related properties of foods. The working principles, applications, and limitations of the sensors employed by electronic noses and electronic tongues were introduced and compared. Widely employed pattern recognition algorithms, including artificial neural network (ANN) convolutional neural network (CNN) principal component analysis (PCA) partial least square regression (PLS) and support vector machine (SVM) were introduced and compared in this review. Key findings and conclusions Overall, e nose or e tongue combining pattern recognition algorithms are very powerful analytical tools, which are relatively low cost, rapid, and accurate. E nose and e tongue are also suitable for both in line and off line measurements, which are very useful in monitoring food processing and detecting the end product quality. The user of e nose and e tongue need to strictly control sample preparation, sampling, and data processing.
author_names: Juzhong Tan

Jie Xu
title: Applications of electronic nose (e nose) and electronic tongue (e tongue) in food quality related properties determination: A review
year: 2020

abstract: Abstract This paper reports a quantitative artificial neural network (ANN) to implement an electronic nose (enose) A new approach was proposed by the combination of ANN with fundamental aspects of analytical chemistry, especially with the concept of relative error (RE) in quantitative analysis. Thus, both the qualitative and quantitative requirements for ANN in implementing enose can be satisfied. Converging criterion while training the ANN can be set according to RE function (RE Func) designed in this work. Fast converging speed and good prediction accuracy could be promised with the use of RE Func. In addition, transform functions in logarithmic, sigmoid and their combined forms to pre process training data sets were evaluated. Also training methods, such as order of training data magnitude and treatment of data passed the RE requirement checking in last iteration, were optimized. The enose was constructed to response quantitatively towards alcohol vapor within concentration range of 0.001 1 mg/l in the presence of petroleum gas and water vapor. The prediction error was
author_names: Yu Lu

Li-Fang Bian

Pengyuan Yang
title: Quantitative artificial neural network for electronic noses
year: 2000

abstract: Abstract An electronic nose device developed in our laboratory was used to follow the flavour release of a multi component essence encapsulated in pectin gels. Gelation kinetics of high methoxylated pectin was followed by dynamic rheological measurements in presence and absence of flavour. A transition from a non stabilized structure to a fully stabilized gel was observed at about 100 min since mixing the gel components. Flavour release through different days after encapsulation was analyzed following the time evolution of the individual gas sensor's signals of the e nose and also the modification of the fingerprints associated to the whole response of the sensors array. The correlation with rheological measurements indicate that the e nose is able to detect the gel formation and to follow the flavour release changes through aging of the material using principal component analysis (PCA) Artificial neural networks (ANN) analysis was also a successful approach to classify the samples in three categories related with the flavour composition and concentration in the gas phase.
author_names: Maria Eugenia Monge

Donatella Bulone

Daniela Giacomazza

Delia L Bernik

R Martin Negri
title: Detection of flavour release from pectin gels using electronic noses
year: 2004

abstract: In this study we propose a practical approach to increase the performance of electronic noses (E noses) in cigarette brand identification. A portable E nose was employed to collect and classify aroma signals from different brands of cigarettes. Artificial neural networks (ANN) were employed and trained with raw data and extracted features from the data collected by the E nose to identify the cigarettes. The Chinese cigarette industry is losing millions of dollars per year due to counterfeit cigarettes. Detecting illegal cigarettes in the field is difficult, but may be possible using portable E noses. However, the differences between odours from counterfeit and genuine cigarettes are small and detection may prove difficult. This preliminary investigation succeeded in identifying four different types of cigarettes in the laboratory. The identification results obtained from the intelligent E noise trained with an ANN using the extracted parameters were better than the ones obtained directly from the E nose.
author_names: Dehan Luo
title: Cigarette Brand Identification Using Intelligent Electronic Noses
year: 2003

abstract: The present paper covers a new type of electronic nose (e nose) with a four sensor array, which has been applied to detecting gases quantitatively in the presence of interference. This e nose has adapted fundamental aspects of relative error (RE) in changing quantitative analysis into the artificial neural network (ANN) Thus, both the quantitative and the qualitative requirements for ANN in implementing e nose can be satisfied. In addition, the e nose uses only 4 sensors in the sensor array, and can be designed for different usages simply by changing one or two sensor(s) Various gases were tested by this kind of e nose, including alcohol vapor, CO, iiquefied petrol gas and CO2. Satisfactory quantitative results were obtained and no qualitative mistake in prediction was observed for the samples being mixed with interference gases.
title: Electronic Noses Using Quantitative Artificial Neural Network
year: 2001

abstract: Electronic nose (e nose) devices have received considerable attention in the field of sensor technology because of their many potential uses such as in identification of toxic wastes, monitoring air quality, examining odors in infected wounds and in inspection of food. Notwithstanding the vast amount of literature on the usage of e noses for specific purposes, the technology originally and ultimately aims to mimic the capability of mammals to discriminate odors from all sorts of objects. This study demonstrates the theoretical and practical feasibility of designing an e nose towards general odor classification. A multi sensor array hardware unit was carefully constructed for data collection and odor detection. Important hardware design considerations such as sensor calibration, aeration, circuit protection, and voltage/current requirements were satisfied. A highly fine tuned artificial neural network (ANN) was integrated to the hardware to interpret and relate the data to a target odor class from a set of 10 primary odors identified in a previous study. Various network architecture considerations, such as neuron count, number of layers and activation function, as well as various data treatment methods, such as normalization, and data partitioning, were investigated. The results showed that careful hardware integration with an ANN having sufficiently deep internal structure can yield accurate classification to at least half of the ten primary odor classes, namely fragrant (96% fruity (98% chemical (99% peppermint (98% and popcorn (90% The results demonstrate the feasibility of making e noses for general odor classification, which could lead to further broadening of e nose applications.
author_names: Marylin Roa

Proceso L Fernandez
title: Development of an Electronic Nose for Olfactory System Modelling using Artificial Neural Network
year: 2018