Evaluation of Chinese Vinegar by Electronic Nose

2014 ◽  
Vol 490-491 ◽  
pp. 1497-1502
Author(s):  
Ming Quan Huang ◽  
Jing Lin Zhang ◽  
Ji Hong Wu ◽  
Bao Guo Sun ◽  
Yu Yu Zhang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Electronic Nose ◽  
Principal Component ◽  
Linear Discriminant ◽  
Production Area ◽  
Production Areas ◽  
Sulfur Containing ◽  
Chinese Vinegar ◽  
Sulfur Containing Compounds

Twelve vinegars from different production areas in China were evaluated by the Portable Electronic Nose 3 (PEN3), and the data detected by PEN3 were analyzed by principal component analysis (PCA), linear discriminant analysis (LDA) and loadings analysis (LA). The results of PCA and LDA all showed that the electronic nose could clearly discriminate the vinegars of difference production areas, but had very little discrimination on same production area vinegars. The results of LD showed that these sensors, including W1S, W2S, W5S, W2W, W1W, W5C, were appropriate to evaluate and compare the aroma of vinegars, especially W1S, W2S, W5S. Loadings analysis also indicated that these compounds in twelve vinegars had great differences, such as acids, esters, alcohols, alkanes, while aromatics compounds, sulfur-containing compounds and alkenes had some distinctions.

scholarly journals Development of a Piezoelectric-Based Odor Reproduction System

Electronics ◽  
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.

2D Image Matrix-Based Discriminator

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.

scholarly journals Principal Component Analysis Sebagai Ekstraksi Fitur Data Microarray Untuk Deteksi Kanker Berbasis Linear Discriminant Analysis

2019 ◽  
Vol 3 (2) ◽  
pp. 72
Author(s):  
Widi Astuti ◽  
Adiwijaya Adiwijaya
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Microarray Data ◽  
Principal Component ◽  
Component Analysis ◽  
High Dimensions ◽  
Linear Discriminant ◽  
Cancer Data ◽  
Colon Cancer Data

Cancer is one of the leading causes of death globally. Early detection of cancer allows better treatment for patients. One method to detect cancer is using microarray data classification. However, microarray data has high dimensions which complicates the classification process. Linear Discriminant Analysis is a classification technique which is easy to implement and has good accuracy. However, Linear Discriminant Analysis has difficulty in handling high dimensional data. Therefore, Principal Component Analysis, a feature extraction technique is used to optimize Linear Discriminant Analysis performance. Based on the results of the study, it was found that usage of Principal Component Analysis increases the accuracy of up to 29.04% and f-1 score by 64.28% for colon cancer data.

scholarly journals Raman Microspectral Study and Classification of the Pathological Evolution of Breast Cancer Using Both Principal Component Analysis-Linear Discriminant Analysis and Principal Component Analysis-Support Vector Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Heping Li ◽  
Yu Ren ◽  
Fan Yu ◽  
Dongliang Song ◽  
Lizhe Zhu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Ductal Carcinoma ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Linear Discriminant

To facilitate the enhanced reliability of Raman-based tumor detection and analytical methodologies, an ex vivo Raman spectral investigation was conducted to identify distinct compositional information of healthy (H), ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). Then, principal component analysis-linear discriminant analysis (PCA-LDA) and principal component analysis-support vector machine (PCA-SVM) models were constructed for distinguishing spectral features among different tissue groups. Spectral analysis highlighted differences in levels of unsaturated and saturated lipids, carotenoids, protein, and nucleic acid between healthy and cancerous tissue and variations in the levels of nucleic acid, protein, and phenylalanine between DCIS and IDC. Both classification models were principal component analysis-linear discriminant analysis to be extremely efficient on discriminating tissue pathological types with 99% accuracy for PCA-LDA and 100%, 100%, and 96.7% for PCA-SVM analysis based on linear kernel, polynomial kernel, and radial basis function (RBF), respectively, while PCA-SVM algorithm greatly simplified the complexity of calculation without sacrificing performance. The present study demonstrates that Raman spectroscopy combined with multivariate analysis technology has considerable potential for improving the efficiency and performance of breast cancer diagnosis.

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