Interpreting convolutional neural network for real-time volatile organic compounds detection and classification using optical emission spectroscopy of plasma
Journal
Analytica Chimica Acta
Journal Volume
1179
Date Issued
2021
Author(s)
Abstract
This study presents the investigation of optical emission spectroscopy of plasma using interpretable convolutional neural network (CNN) for real-time volatile organic compounds (VOCs) classification. A microplasma-generation platform was developed to efficiently collect 64 k spectra from various types of VOCs at different concentrations, as training and testing sets for machine learning. A CNN model was trained to classify VOCs with accuracy of 99.9%. To interpret the CNN model and its predictions, the spectral processing mechanism of the CNN was visualized by feature maps and the critical spectral features were identified by gradient-weighted class activation mapping. Such approaches brought insights on how CNN analyzes the spectra and enables the CNN operation to be explainable. Finally, the CNN model was incorporated with the microplasma platform to demonstrate the application of real-time VOC monitoring. The type of VOCs can be identified and reported via messages within 10 s once the microplasma is ignited. We believe that using CNN brings a novel route for plasma spectroscopy analysis for VOC classification and impacts the fields of plasma, spectroscopy, and environmental monitoring. ? 2021 Elsevier B.V.
Subjects
Grad-CAM
Machine learning
Microplasma
Optical emission spectroscopy
Convolution
Convolutional neural networks
Metamaterials
Photomapping
Plasma devices
Volatile organic compounds
Convolutional neural network
Machine-learning
Micro-plasmas
Neural network modelling
Optical-emission spectroscopy
Plasma spectroscopy
Real- time
Spectra's
Volatile organics
volatile organic compound
spectroscopy
Neural Networks, Computer
Spectrum Analysis
Volatile Organic Compounds
Type
journal article