Support vector machine classification of volatile organic compounds based on narrow-band spectroscopic data

2014 ◽  
Vol 29 (1) ◽  
pp. 38-48 ◽  
Author(s):  
Apostolos Argyris ◽  
Jean-Jacques Filippi ◽  
Dimitris Syvridis
Keyword(s):  
Support Vector Machine ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Spectroscopic Data ◽  
Narrow Band ◽  
Support Vector ◽  
Support Vector Machine Classification ◽  
Volatile Organic

scholarly journals Microcontroller Implementation of Support Vector Machine for Detecting Blood Glucose Levels Using Breath Volatile Organic Compounds

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2283 ◽  
Author(s):  
Matthew Boubin ◽  
Sudhir Shrestha
Keyword(s):  
Support Vector Machine ◽  
Blood Glucose ◽  
Volatile Organic Compounds ◽  
Embedded System ◽  
Organic Compounds ◽  
Sensor Arrays ◽  
Support Vector ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Volatile Organic

This paper presents an embedded system-based solution for sensor arrays to estimate blood glucose levels from volatile organic compounds (VOCs) in a patient’s breath. Support vector machine (SVM) was trained on a general-purpose computer using an existing SVM library. A training model, optimized to achieve the most accurate results, was implemented in a microcontroller with an ATMega microprocessor. Training and testing was conducted using artificial breath that mimics known VOC footprints of high and low blood glucose levels. The embedded solution was able to correctly categorize the corresponding glucose levels of the artificial breath samples with 97.1% accuracy. The presented results make a significant contribution toward the development of a portable device for detecting blood glucose levels from a patient’s breath.

scholarly journals Proof of concept study: Testing human volatile organic compounds as tools for age classification of films

PLoS ONE ◽  
2018 ◽  
Vol 13 (10) ◽  
pp. e0203044 ◽  
Author(s):  
C. Stönner ◽  
A. Edtbauer ◽  
B. Derstroff ◽  
E. Bourtsoukidis ◽  
T. Klüpfel ◽  
...  
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Proof Of Concept ◽  
Concept Study ◽  
Volatile Organic

scholarly journals Flexible Impedimetric Electronic Nose for High-Accurate Determination of Individual Volatile Organic Compounds by Tuning the Graphene Sensitive Properties

Chemosensors ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 360
Author(s):  
Tianqi Lu ◽  
Ammar Al-Hamry ◽  
José Mauricio Rosolen ◽  
Zheng Hu ◽  
Junfeng Hao ◽  
...  
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Electronic Nose ◽  
Accurate Determination ◽  
Identification Accuracy ◽  
Structural Defects ◽  
Support Vector ◽  
Volatile Organic ◽  
Variable Sensitivity ◽  
Relative Change

We investigated functionalized graphene materials to create highly sensitive sensors for volatile organic compounds (VOCs) such as formaldehyde, methanol, ethanol, acetone, and isopropanol. First, we prepared VOC-sensitive films consisting of mechanically exfoliated graphene (eG) and chemical graphene oxide (GO), which have different concentrations of structural defects. We deposited the films on silver interdigitated electrodes on Kapton substrate and submitted them to thermal treatment. Next, we measured the sensitive properties of the resulting sensors towards specific VOCs by impedance spectroscopy. We obtained the eG- and GO-based electronic nose composed of two eG films- and four GO film-based sensors with variable sensitivity to individual VOCs. The smallest relative change in impedance was 5% for the sensor based on eG film annealed at 180 °C toward 10 ppm formaldehyde, whereas the highest relative change was 257% for the sensor based on two-layers deposited GO film annealed at 200 °C toward 80 ppm ethanol. At 10 ppm VOC, the GO film-based sensors were sensitive enough to distinguish between individual VOCs, which implied excellent selectivity, as confirmed by Principle Component Analysis (PCA). According to a PCA-Support Vector Machine-based signal processing method, the electronic nose provided identification accuracy of 100% for individual VOCs. The proposed electronic nose can be used to detect multiple VOCs selectively because each sensor is sensitive to VOCs and has significant cross-selectivity to others.

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