Advances in gas sensors and electronic nose technologies for agricultural cycle applications

Lung cancer is characterized by a tremendously high mortality rate and a low 5-year survival rate when diagnosed at a late stage. Early diagnosis of lung cancer drastically reduces its mortality rate and improves survival. Exhaled breath analysis could offer a tool to clinicians to improve the ability to detect lung cancer at an early stage, thus leading to a reduction in the associated survival rate. In this paper, we present an electronic nose for the automatic analysis of exhaled breath. A total of five a-specific gas sensors were embedded in the electronic nose, making it sensitive to different volatile organic compounds (VOCs) contained in exhaled breath. Nine features were extracted from each gas sensor response to exhaled breath, identifying the subject breathprint. We tested the electronic nose on a cohort of 80 subjects, equally split between lung cancer and at-risk control subjects. Including gas sensor features and clinical features in a classification model, recall, precision, and accuracy of 78%, 80%, and 77% were reached using a fourfold cross-validation approach. The addition of other a-specific gas sensors, or of sensors specific to certain compounds, could improve the classification accuracy, therefore allowing for the development of a clinical tool to be integrated in the clinical pipeline for exhaled breath analysis and lung cancer early diagnosis.

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Electronic nose sensor development using ANN backpropagation for Lombok Agarwood classification

Research in Agricultural Engineering ◽

10.17221/26/2020-rae ◽

2020 ◽

Vol 66 (No. 3) ◽

pp. 97-103

Author(s):

Farel Ahadyatulakbar Aditama ◽

Lalu Zulfikri ◽

Laili Mardiana ◽

Tri Mulyaningsih ◽

Nurul Qomariyah ◽

...

Keyword(s):

Electronic Nose ◽

Gas Sensors ◽

Poor Quality ◽

Backpropagation Algorithm ◽

The Poor ◽

Quality Classification ◽

Electronic Nose System ◽

System Prototype

The aim of the present study is the development of an electronic nose system prototype for the classification of Gyrinops versteegii agarwood. The prototype consists of three gas sensors, i.e., TGS822, TGS2620, and TGS2610. The data acquisition and quality classification of the nose system are controlled by the Artificial Neural Network backpropagation algorithm in the Arduino Mega2650 microcontroller module. The testing result shows that an electronic nose can distinguish the quality of Gyrinops versteegii agarwood. The good-quality agarwood has an output of [1 –1], while the poor-quality agarwood has an output of [–1 1].

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Electronic Nose Technologies in Monitoring Black Tea Manufacturing Process

Journal of Sensors ◽

10.1155/2020/3073104 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Tharaga Sharmilan ◽

Iresha Premarathne ◽

Indika Wanniarachchi ◽

Sandya Kumari ◽

Dakshika Wanniarachchi

Keyword(s):

Neural Network ◽

Electronic Nose ◽

Gas Sensors ◽

Black Tea ◽

Quality Parameters ◽

Polyphenolic Compounds ◽

Metal Oxide Semiconductor ◽

Oxide Semiconductor ◽

Optimum Level ◽

Fermentation Stage

“Tea” is a beverage which has a unique taste and aroma. The conventional method of tea manufacturing involves several stages. These are plucking, withering, rolling, fermentation, and finally firing. The quality parameters of tea (color, taste, and aroma) are developed during the fermentation stage where polyphenolic compounds are oxidized when exposed to air. Thus, controlling the fermentation stage will result in more consistent production of quality tea. The level of fermentation is often detected by humans as “first” and “second” noses as two distinct smell peaks appear during fermentation. The detection of the “second” aroma peak at the optimum fermentation is less consistent when decided by humans. Thus, an electronic nose is introduced to find the optimum level of fermentation detecting the variation in the aroma level. In this review, it is found that the systems developed are capable of detecting variation of the aroma level using an array of metal oxide semiconductor (MOS) gas sensors using different statistical and neural network techniques (SVD, 2-NM, MDM, PCA, SVM, RBF, SOM, PNN, and Recurrent Elman) successfully.

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Development of a Tuneable NDIR Optical Electronic Nose

Sensors ◽

10.3390/s20236875 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6875

Author(s):

Siavash Esfahani ◽

Akira Tiele ◽

Samuel O. Agbroko ◽

James A. Covington

Keyword(s):

Electronic Nose ◽

Gas Sensors ◽

Low Cost ◽

Medical Diagnostics ◽

Good Discrimination ◽

Humidity Effects ◽

Sensor Drift ◽

Optical Gas Sensor ◽

Series Of Experiments ◽

Increasing Demand

Electronic nose (E-nose) technology provides an easy and inexpensive way to analyse chemical samples. In recent years, there has been increasing demand for E-noses in applications such as food safety, environmental monitoring and medical diagnostics. Currently, the majority of E-noses utilise an array of metal oxide (MOX) or conducting polymer (CP) gas sensors. However, these sensing technologies can suffer from sensor drift, poor repeatability and temperature and humidity effects. Optical gas sensors have the potential to overcome these issues. This paper reports on the development of an optical non-dispersive infrared (NDIR) E-nose, which consists of an array of four tuneable detectors, able to scan a range of wavelengths (3.1–10.5 μm). The functionality of the device was demonstrated in a series of experiments, involving gas rig tests for individual chemicals (CO2 and CH4), at different concentrations, and discriminating between chemical standards and complex mixtures. The optical gas sensor responses were shown to be linear to polynomial for different concentrations of CO2 and CH4. Good discrimination was achieved between sample groups. Optical E-nose technology therefore demonstrates significant potential as a portable and low-cost solution for a number of E-nose applications.

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A Prototype to Detect the Alcohol Content of Beers Based on an Electronic Nose

Sensors ◽

10.3390/s19112646 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2646 ◽

Cited By ~ 8

Author(s):

Henike Guilherme Jordan Voss ◽

José Jair Alves Mendes Júnior ◽

Murilo Eduardo Farinelli ◽

Sergio Luiz Stevan

Keyword(s):

Metal Oxide ◽

Electronic Nose ◽

Gas Sensors ◽

Metal Oxide Semiconductor ◽

Oxide Semiconductor ◽

Alcohol Content ◽

Electronic Noses

Due to the emergence of new microbreweries in the Brazilian market, there is a need to construct equipment to quickly and accurately identify the alcohol content in beverages, together with a reduced marketing cost. Towards this purpose, the electronic noses prove to be the most suitable equipment for this situation. In this work, a prototype was developed to detect the concentration of ethanol in a high spectrum of beers presents in the market. It was used cheap and easy-to-acquire 13 gas sensors made with a metal oxide semiconductor (MOS). Samples with 15 predetermined alcohol contents were used for the training and construction of the models. For validation, seven different commercial beverages were used. The correlation (R2) of 0.888 for the MLR (RMSE = 0.45) and the error of 5.47% for the ELM (RMSE = 0.33) demonstrate that the equipment can be an effective tool for detecting the levels of alcohol contained in beverages.

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Electronic Nose-Based Technique for Rapid Detection and Recognition of Moldy Apples

10.20944/preprints201903.0008.v1 ◽

2019 ◽

Author(s):

Wenshen Jia ◽

Gang Liang ◽

Hui Tian ◽

Jing Sun ◽

Cihui Wan

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Electronic Nose ◽

Gas Sensors ◽

Penicillium Expansum ◽

Support Vector ◽

Linear Discriminant ◽

Pattern Recognition Methods ◽

Group A ◽

Group B

In this paper, PEN3 electronic nose was used to detect and recognize fresh and moldy apples (inoculated with Penicillium expansum and Aspergillusniger) taken Golden Delicious apples as model subject. Firstly, the apples were divided into two groups: apples only inoculated with different molds (Group A) and mixed apples of inoculated apples with fresh apples (Group B). Then the characteristic gas sensors of the PEN3 electronic nose that were most closely correlated with the flavor information of the moldy apples were optimized and determined, which can simplify the analysis process and improve the accuracy of results. Four pattern recognition methods, including linear discriminant analysis (LDA), backpropagation neural network (BPNN), support vector machines (SVM) and radial basis function neural network (RBFNN), were then applied to analyze the data obtained from the characteristic sensors, respectively, aiming at establishing the prediction model of flavor information and fresh/moldy apples. The results showed that only the gas sensors of W1S, W2S, W5S, W1W and W2W in the PEN3 electronic nose exhibited strong signal response to the flavor information, indicating were most closely correlated with the characteristic flavor of apples and thus the data obtained from these characteristic sensors was used for modeling. The results of the four pattern recognition methods showed that BPNN presented the best prediction performance for the training and validation sets for both the Group A and Group B, with prediction accuracies of 96.29% and 90.00% (Group A), 77.70% and 72.00% (Group B), respectively. Therefore, it first demonstrated that PEN3 electronic nose can not only effectively detect and recognize the fresh and moldy apples, but also can distinguish apples inoculated with different molds.

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Performance enhanced gas sensors through nanomaterial/nanostructure for functional electronic nose applications

10.14711/thesis-b1450632 ◽

2015 ◽

Author(s):

Xiaofang Pan

Keyword(s):

Electronic Nose ◽

Gas Sensors

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