Identification of multi-concentration aromatic fragrances with electronic nose technology using a support vector machine

2021 ◽  
Author(s):  
Sun-Tae Kim ◽  
Il-Hwan Choi ◽  
Hui Li
Keyword(s):  
Support Vector Machine ◽  
Electronic Nose ◽  
Concentration Effect ◽  
Support Vector ◽  
Electronic Nose Technology

The responses of an e-nose to 4 aromas are normalized to eliminate the concentration effect. The model trained by a SVM can accurately classify unknown samples.

Determination of Schizophrenia Using Electronic Nose via Support Vector Machine

2019 ◽  
Author(s):  
Rainier V. Leal ◽  
Alyssa Xyra C. Quiming ◽  
Jocelyn Flores Villaverde ◽  
Analyn N. Yumang ◽  
Noel B. Linsangan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Electronic Nose ◽  
Support Vector

scholarly journals Mining Data of Noisy Signal Patterns in Recognition of Gasoline Bio-Based Additives using Electronic Nose

2017 ◽  
Vol 24 (1) ◽  
pp. 27-44 ◽  
Author(s):  
Stanisław Osowski ◽  
Krzysztof Siwek
Keyword(s):  
Support Vector Machine ◽  
Electronic Nose ◽  
Data Clustering ◽  
Wavelet Transformation ◽  
Principal Component ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Noisy Environment ◽  
Special Stress ◽  
Sensor Signals

Abstract The paper analyses the distorted data of an electronic nose in recognizing the gasoline bio-based additives. Different tools of data mining, such as the methods of data clustering, principal component analysis, wavelet transformation, support vector machine and random forest of decision trees are applied. A special stress is put on the robustness of signal processing systems to the noise distorting the registered sensor signals. A special denoising procedure based on application of discrete wavelet transformation has been proposed. This procedure enables to reduce the error rate of recognition in a significant way. The numerical results of experiments devoted to the recognition of different blends of gasoline have shown the superiority of support vector machine in a noisy environment of measurement.

scholarly journals ELECTRONIC NOSE FOR ANCHOVY FRESHNESS MONITORING BASED ON SENSOR ARRAY AND PATTERN RECOGNITION METHODS: PRINCIPAL COMPONENTS ANALYSIS, LINEAR DISCRIMINANT ANALYSIS AND SUPPORT VECTOR MACHINE

2014 ◽  
pp. 61-67
Author(s):  
A. Amari ◽  
N. El Bari ◽  
B. Bouchikhi
Keyword(s):  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Electronic Nose ◽  
Tin Dioxide ◽  
Principal Component ◽  
Support Vector ◽  
Current Pattern ◽  
Linear Discriminant ◽  
Pattern Recognition Methods

An electronic nose based system, which employs an array of six inexpensive commercial gas sensors based on tin dioxide (Figaro Engineering Inc., Japan), has been used to analyse the freshness states of anchovies. Fresh anchovies were stored in a refrigerator at 4 ± 1°C over a period of 15 days. Electronic nose measurements need no sample preparation and the results indicated that the spoilage process of anchovies could be followed by using this technique. Conductance responses of volatile compounds produced during storage of anchovy were monitored and the result were analysed by multivariate analysis methods. In this paper principal component analysis (PCA) and linear discriminant analysis (LDA) were used to investigate whether the electronic nose was able to distinguishing among different freshness states (fresh, moderated and non-fresh samples). The loadings analysis was used to identify the sensors responsible for discrimination in the current pattern file. Therefore, the support vector machines (SVM) method was applied to the new subset, with only the selected sensors, to confirm that a subset of a few sensors can be chosen to explain all the variance. The results obtained prove that the electronic nose can discriminate successfully different freshness state using LDA analysis. Some sensors have the highest influence in the current pattern file for electronic nose. Support vector machine (SVM) model, applied to the new subset of sensors show the good performance.

Prediction of total viable counts on chilled pork using an electronic nose combined with support vector machine

Meat Science ◽  
2012 ◽  
Vol 90 (2) ◽  
pp. 373-377 ◽  
Author(s):  
Danfeng Wang ◽  
Xichang Wang ◽  
Taiang Liu ◽  
Yuan Liu
Keyword(s):  
Support Vector Machine ◽  
Electronic Nose ◽  
Support Vector ◽  
Chilled Pork

Classification of multiple indoor air contaminants by an electronic nose and a hybrid support vector machine

2012 ◽  
Vol 174 ◽  
pp. 114-125 ◽  
Author(s):  
Lei Zhang ◽  
Fengchun Tian ◽  
Hong Nie ◽  
Lijun Dang ◽  
Guorui Li ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Electronic Nose ◽  
Indoor Air ◽  
Support Vector ◽  
Air Contaminants
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