ON THE SOLUTION OF THE IMAGE RECOGNITION PROBLEM BY A PRINCIPAL COMPONENT METHOD AND LINEAR DISCRIMINANT ANALYSIS

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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Finger-based personal authentication: a comparison of feature-extraction methods based on principal component analysis, most discriminant features and regularised-direct linear discriminant analysis

IET Signal Processing ◽

10.1049/iet-spr.2008.0149 ◽

2009 ◽

Vol 3 (4) ◽

pp. 269 ◽

Cited By ~ 8

Author(s):

N. Pavešić ◽

S. Ribarić ◽

B. Grad

Keyword(s):

Principal Component Analysis ◽

Feature Extraction ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Principal Component ◽

Component Analysis ◽

Extraction Methods ◽

Linear Discriminant ◽

Personal Authentication

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Use of Gas Chromatograms of the Essential Leaf Oils of the Genus Eucalyptus for Taxonomic Purposes

Australian Journal of Botany ◽

10.1071/bt96061 ◽

1997 ◽

Vol 45 (1) ◽

pp. 1 ◽

Cited By ~ 15

Author(s):

Peter J. Dunlop ◽

Caroline M. Bignell ◽

D. Brynn Hibbert

Keyword(s):

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Vacuum Distillation ◽

Principal Component ◽

Analytical Techniques ◽

Hierarchical Cluster ◽

Eucalyptus Species ◽

Linear Discriminant ◽

Genus Eucalyptus

Using morphological observations, botanists have classified Eucalyptus species into characteristic series. A new vacuum distillation technique has been employed to obtain the characteristic leaf oils, which are very close to their in vivo compositions, from 35 species belonging to series Tetrapterae, series Torquatae and series Rufispermae. Accurate gas chromatograms have been obtained for each species and three analytical techniques (principal component analysis (PCA), hierarchical cluster analysis (CA) and linear discriminant analysis (LDA)) have been used to process these chromatograms to see if agreement with these classifications could be achieved without using any auxiliary morphometric data. For the species chosen for the present study, linear discriminant analysis was the most successful in assigning species to their present botanic classifications. These analytical methods were also used with some success in searching for groupings within a series and within a species.

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