[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126851-en":3,"doc-seo-126851-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126851,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Distinguishing Methane from Other Hydrocarbons Using Machine Learning and Atmospheric Pressure Plasma Optical Emission Spectroscopy","The study develops machine-learning methods integrated with atmospheric pressure plasma optical emission spectroscopy to identify gas molecules and estimate gas concentration. Algorithms are built for gas-molecule identification among three hydrocarbons (CH4, C2H2, C2H6) and for classification of concentration levels using chemometric analysis. Unique emission lines and variable-importance-on-projection signals are used as discriminative indicators across more than 1000 samples. Results target rapid, accurate, and innovative sensing, supporting potential real-time, portable, and continuous trace-gas monitoring in medical, environmental, and industrial settings.","J. Phys. D: Appl. Phys. 57 (2024) 345202 (11pp) [https://doi.org/10.1088/1361-6463/ad4f97](https://doi.org/10.1088/1361-6463/ad4f97)  \nDistinguishing methane from other hydrocarbons using machine learning and atmospheric pressure plasma optical emission spectroscopy  \nTahereh Shah Mansouri 1, ∗ 􀁂, Hui Wang2, Davide Mariotti 1 􀁂 and Paul Maguire 1 􀁂  \n1 NIBEC Engineering, Ulster University, Belfast, United Kingdom  \n2 Queens University, Belfast, United Kingdom  \nE-mail: [t.shah_mansouri@ulster.ac.uk](t.shah_mansouri@ulster.ac.uk)  \nReceived 20 December 2023, revised 11 April 2024 Accepted for publication 23 May 2024  \nPublished 4 June 2024  \nAbstract  \nThe ability to detect gas molecule and assign a concentration offers an inventive solution in the field of plasma integrated with machine learning. The most important finding of this work is firstly, to develop an algorithm for gas-molecule identification using three different hydrocarbons (CH4 , C2H2 , C2H6 ) and secondly, organize a model for detecting gas concentration (classification). For this reason, initially eight different gases evaluated. The study confirms the present of the unique emission lines as a gas indicator, i.e., a wavelength peak related to hydrocarbons identified via increasing in CxHy concentration. By means of unique variable important in projection, hydrocarbons can be distinguished. Our proposed Chemometric analysis strategy examined on > 1000 samples and results development of suitable techniques that are sufficiently rapid, accurate and innovative. This demonstrates the potential for real-time, portable, and continuous monitoring of trace gases with potential applications in medical, environmental, and industrial gas sensing.  \nKeywords: methane identification, optical emission spectroscopy (OES),  \nvariable importance in projection (VIP), unique VIP, partial least square discriminant analysis, hydrocarbons, classification  \n1. Introduction  \nThe application of supervised learning models based on partial least squares discriminant analysis (PLS-DA) was investigated for the identification of trace gases using data obtained from plasma optical emission spectroscopy [1] . As a result, the parts-per-million (ppm) classification of a single type of  \n∗  \nAuthor to whom any correspondence should be addressed.  \nOriginal content from this work maybe used under the terms  \nof the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.  \nhydrocarbon gas (methane) was achieved down to concentrations in the low ppm range. The next stage is to attempt the detection of various hydrocarbon gases and investigate the capability of the machine learning (ML) approach in more complex scenarios.  \nIn this study, we aimed to distinguish different hydrocarbon gases using the PLS-DA method and its features, such as variable importance on projection (VIP) selection. Detecting hydrocarbons such as CH4 and C2H6 can be useful in breath analysis (VOCs) or climate change monitoring applications. Carbon bonding determines the hydrocarbon type, with singlebond C forming an alkane, double-bond C forming an alkene, and triple-bond C forming an alkyne [2]. To determine the capability for general hydrocarbons (CxHy ) detection, two alkanes (CH4 and C2H6 ) and one alkyne (C2H2 ) were selected with the  \n1 © 2024 The Author(s) . Published by IOP Publishing Ltd  \naim of using them as a basis for detecting other gases that are very similar in their composition. Thus, this technique based on plasma optical emission spectra coupled with ML is fundamental for a much wider field of hydrocarbon or breath VOC detection [3] and identification applications.  \nThe miniature plasma source for generating optical emission spectral data is aimed at low-cost portable or field deployment, capable of autonomous and continuous monitoring of environmental trace gases. Plasma contains a high","cbCaitxxvoDJbB5H","https://ap.wps.com/l/cbCaitxxvoDJbB5H","pdf",2203173,1,11,"English","en",105,"# Abstract\n# Keywords\n# Introduction","[{\"question\":\"What machine-learning approach is used to distinguish methane and other hydrocarbons?\",\"answer\":\"The document uses supervised learning based on partial least squares discriminant analysis (PLS-DA) and leverages feature selection such as variable importance on projection (VIP).\"},{\"question\":\"Which hydrocarbons are included in the identification and classification study?\",\"answer\":\"The study focuses on three hydrocarbons: methane (CH4), acetylene (C2H2), and ethane (C2H6), with experiments also considering evaluation across multiple gases before final organization of the model.\"},{\"question\":\"How does the method enable detection of gas concentration from plasma measurements?\",\"answer\":\"Unique emission lines related to hydrocarbons are used as gas indicators, and a classification model maps emission spectral variables to concentration levels, validated on more than 1000 samples for rapid and accurate performance.\"}]","Distinguishing Methane from Other Hydrocarbons Using Machine Learning and Atmospheric Pressure Plasma Optical Emission Spectroscopy | 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