Optimization of Electronic Nose Sensor Array and its Application in the Classification of Vinegar

Five kinds of vinegars were measured by a gas sensor array composed of six TGS gas sensors. The sensor array should be optimized by the minimal Wilks statistic value, then, the four best sensor array used to detect the type of vinegars were formed, Principal Component Analysis (PCA) and Linear discriminant analysis (LDA) were applied to analyze the data of primary and optimized sensor array. The results indicated that optimization sensor array could be more adaptable to recognize the five kinds of vinegars. Thereby the given optimization method is effective.

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Performance Comparison of Linear Discriminant Analysis and Principal Component Analysis for Classification of Gases/Odors with Neural Classifier Using Thick Film Gas Sensor Array Responses

Advanced Science Engineering and Medicine ◽

10.1166/asem.2013.1390 ◽

2013 ◽

Vol 5 (10) ◽

pp. 1051-1057

Author(s):

. Sunny ◽

VishwambharN. Mishra ◽

R. Dwivedi ◽

RukminiR. Das

Keyword(s):

Principal Component Analysis ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Sensor Array ◽

Principal Component ◽

Performance Comparison ◽

Linear Discriminant ◽

Gas Sensor Array ◽

Neural Classifier

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Relevance Vector Machine Classification of Hyperspectral Data Based on Principal Component Analysis and Linear Discriminant Analysis

DEStech Transactions on Computer Science and Engineering ◽

10.12783/dtcse/aice-ncs2016/5630 ◽

2017 ◽

Author(s):

Wen-Xing BAO ◽

Bin LI ◽

Rong REN

Keyword(s):

Principal Component Analysis ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Principal Component ◽

Component Analysis ◽

Relevance Vector Machine ◽

Hyperspectral Data ◽

Linear Discriminant

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Classification of olive oils using high throughput flow 1H NMR fingerprinting with principal component analysis, linear discriminant analysis and probabilistic neural networks

Analytica Chimica Acta ◽

10.1016/j.aca.2005.07.057 ◽

2005 ◽

Vol 552 (1-2) ◽

pp. 13-24 ◽

Cited By ~ 112

Author(s):

Serge Rezzi ◽

David E. Axelson ◽

Károly Héberger ◽

Fabiano Reniero ◽

Carlo Mariani ◽

...

Keyword(s):

Neural Networks ◽

Principal Component Analysis ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

High Throughput ◽

Principal Component ◽

Probabilistic Neural Networks ◽

Linear Discriminant ◽

Olive Oils

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Application of principal component analysis and linear discriminant analysis on elastic light single-scattering spectroscopic data for classification of prostate tissue: A preliminary study

2014 18th National Biomedical Engineering Meeting ◽

10.1109/biyomut.2014.7026364 ◽

2014 ◽

Cited By ~ 1

Author(s):

Tuba Denkceken ◽

Murat Canpolat ◽

Ibrahim Bassorgun ◽

Mehmet Baykara

Keyword(s):

Principal Component Analysis ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Spectroscopic Data ◽

Principal Component ◽

Single Scattering ◽

Prostate Tissue ◽

Linear Discriminant ◽

Preliminary Study

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Comparative study on fast classification of brick samples by combination of principal component analysis and linear discriminant analysis using stand-off and table-top laser-induced breakdown spectroscopy

Spectrochimica Acta Part B Atomic Spectroscopy ◽

10.1016/j.sab.2014.08.036 ◽

2014 ◽

Vol 101 ◽

pp. 191-199 ◽

Cited By ~ 32

Author(s):

Gabriela Vítková ◽

Lubomír Prokeš ◽

Karel Novotný ◽

Pavel Pořízka ◽

Jan Novotný ◽

...

Keyword(s):

Principal Component Analysis ◽

Discriminant Analysis ◽

Comparative Study ◽

Principal Component ◽

Laser Induced Breakdown Spectroscopy ◽

Linear Discriminant ◽

Breakdown Spectroscopy ◽

Laser Induced Breakdown ◽

Fast Classification

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Face Recognition Using Principal Component Analysis-Fuzzy Linear Discriminant Analysis and Dynamic Fuzzy Neural Network

Lecture Notes in Electrical Engineering - Future Wireless Networks and Information Systems ◽

10.1007/978-3-642-27323-0_73 ◽

2012 ◽

pp. 577-586 ◽

Cited By ~ 1

Author(s):

Sai Wang ◽

Huiwen Deng

Keyword(s):

Neural Network ◽

Principal Component Analysis ◽

Face Recognition ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Fuzzy Neural Network ◽

Principal Component ◽

Component Analysis ◽

Linear Discriminant ◽

Fuzzy Neural

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An analytical strategy based on Fourier transform infrared spectroscopy, principal component analysis and linear discriminant analysis to suggest the botanical origin of resins from Bursera. Application to archaeological Aztec Samples

Journal of Cultural Heritage ◽

10.1016/j.culher.2018.02.006 ◽

2018 ◽

Vol 33 ◽

pp. 48-59 ◽

Cited By ~ 6

Author(s):

Carmen Piña-Torres ◽

Paola Lucero-Gómez ◽

Stephany Nieto ◽

Alfonsina Vázquez ◽

Lauro Bucio ◽

...

Keyword(s):

Principal Component Analysis ◽

Fourier Transform ◽

Infrared Spectroscopy ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Fourier Transform Infrared Spectroscopy ◽

Principal Component ◽

Botanical Origin ◽

Analytical Strategy ◽

Linear Discriminant

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Determination of HPLC-UV Fingerprints of Spanish Paprika (Capsicum annuum L.) for Its Classification by Linear Discriminant Analysis

Sensors ◽

10.3390/s18124479 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4479 ◽

Cited By ~ 6

Author(s):

Xavier Cetó ◽

Núria Serrano ◽

Miriam Aragó ◽

Alejandro Gámez ◽

Miquel Esteban ◽

...

Keyword(s):

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Principal Component ◽

Reversed Phase ◽

Classification Rate ◽

Phenolic Profile ◽

Linear Discriminant ◽

Sample Extraction

The development of a simple HPLC-UV method towards the evaluation of Spanish paprika’s phenolic profile and their discrimination based on the former is reported herein. The approach is based on C18 reversed-phase chromatography to generate characteristic fingerprints, in combination with linear discriminant analysis (LDA) to achieve their classification. To this aim, chromatographic conditions were optimized so as to achieve the separation of major phenolic compounds already identified in paprika. Paprika samples were subjected to a sample extraction stage by sonication and centrifugation; extracting procedure and conditions were optimized to maximize the generation of enough discriminant fingerprints. Finally, chromatograms were baseline corrected, compressed employing fast Fourier transform (FFT), and then analyzed by means of principal component analysis (PCA) and LDA to carry out the classification of paprika samples. Under the developed procedure, a total of 96 paprika samples were analyzed, achieving a classification rate of 100% for the test subset (n = 25).

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2D Image Matrix-Based Discriminator

Computational Intelligence and its Applications - Biometric Image Discrimination Technologies ◽

10.4018/978-1-59140-830-7.ch011 ◽

2006 ◽

pp. 258-286

Author(s):

David Zhang ◽

Xiao-Yuan Jing ◽

Jian Yang

Keyword(s):

Principal Component Analysis ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Principal Component ◽

Component Analysis ◽

Two Dimensional ◽

Fisher Linear Discriminant ◽

Linear Discriminant ◽

Image Projection ◽

Projection Techniques

This chapter presents two straightforward image projection techniques — two-dimensional (2D) image matrix-based principal component analysis (IMPCA, 2DPCA) and 2D image matrix-based Fisher linear discriminant analysis (IMLDA, 2DLDA). After a brief introduction, we first introduce IMPCA. Then IMLDA technology is given. As a result, we summarize some useful conclusions.

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Development of a Piezoelectric-Based Odor Reproduction System

Electronics ◽

10.3390/electronics8080870 ◽

2019 ◽

Vol 8 (8) ◽

pp. 870

Author(s):

Tengteng Wen ◽

Dehan Luo ◽

Yongjie Ji ◽

Pingzhong Zhong

Keyword(s):

Principal Component Analysis ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Electronic Nose ◽

Principal Component ◽

Component Analysis ◽

Piezoelectric Transducers ◽

Linear Discriminant ◽

Reproduction System ◽

Machine Olfaction

Odor reproduction, a branch of machine olfaction, is a technology through which a machine represents various odors by blending several odor sources in different proportions and releases them. In this paper, an odor reproduction system is proposed. The system includes an atomization-based odor dispenser using 16 micro-porous piezoelectric transducers. The authors propose the use of an electronic nose combined with a Principal Component Analysis–Linear Discriminant Analysis (PCA–LDA) model to evaluate the effectiveness of the system. The results indicate that the model can be used to evaluate the system.

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