Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractDetection of COPD and Lung Cancer with electronic nose using ensemble learning methods    Next AbstractHighly Sensitive and Selective Triethylamine Sensing through High-Entropy Alloy (Ti-Zr-Cr-V-Ni) Nanoparticle-Induced Fermi Energy Control of MoS(2) Nanosheets »

Expert Rev Mol Diagn


Title:Noninvasive detection of COPD and Lung Cancer through breath analysis using MOS Sensor array based e-nose
Author(s):V AB; Subramoniam M; Mathew L;
Address:"Department of Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, India. Department of Electronics Engineering, Saintgits College of Engineering, Kottayam, India. Department of Pulmonology, Believers Church Medical College Hospital, Thiruvalla, India"
Journal Title:Expert Rev Mol Diagn
Year:2021
Volume:20210827
Issue:11
Page Number:1223 - 1233
DOI: 10.1080/14737159.2021.1971079
ISSN/ISBN:1744-8352 (Electronic) 1473-7159 (Linking)
Abstract:"INTRODUCTION: This paper describes the research work done toward the development of a breath analyzing electronic nose (e-nose), and the results obtained from testing patients with lung cancer, patients with chronic obstructive pulmonary disease (COPD), and healthy controls. Pulmonary diseases like COPD and lung cancer are detected with MOS sensor array-based e-noses. The e-nose device with the sensor array, data acquisition system, and pattern recognition can detect the variations of volatile organic compounds (VOC) present in the expelled breath of patients and healthy controls. MATERIALS AND METHODS: This work presents the e-nose equipment design, study subjects selection, breath sampling procedures, and various data analysis tools. The developed e-nose system is tested in 40 patients with lung cancer, 48 patients with COPD, and 90 healthy controls. RESULTS: In differentiating lung cancer and COPD from controls, support vector machine (SVM) with 3-fold cross-validation outperformed all other classifiers with an accuracy of 92.3% in cross-validation. In external validation, the same discrimination was achieved by k-nearest neighbors (k-NN) with 75.0% accuracy. CONCLUSION: The reported results show that the VOC analysis with an e-nose system holds exceptional possibilities in noninvasive disease diagnosis applications"
Keywords:"Breath Tests/methods Electronic Nose Humans *Lung Neoplasms/diagnosis *Pulmonary Disease, Chronic Obstructive/diagnosis *Volatile Organic Compounds/analysis *copd *breath analysis *electronic nose *lung cancer *volatile organic compounds;"
Notes:"MedlineV A, Binson Subramoniam, M Mathew, Luke eng England 2021/08/21 Expert Rev Mol Diagn. 2021 Nov; 21(11):1223-1233. doi: 10.1080/14737159.2021.1971079. Epub 2021 Aug 27"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 19-12-2024