[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126036-en":3,"doc-seo-126036-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126036,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Metal oxide-based gas sensor array for VOCs determination in complex mixtures using machine learning","Detection of volatile organic compounds (VOCs) from breath enables early disease identification through non-invasive sensing. The study proposes a 3-component metal oxide sensor array designed for strong cross-sensitivity and machine-learning-based identification of four distinct VOCs in complex mixtures. Sensors include NiO-Au (ohmic), CuO-Au (Schottky), and ZnO–Au (Schottky) fabricated by DC reactive sputtering with 80–100 nm films. Response datasets from individual gases and pair/mix exposures (ethanol, acetone, toluene, chloroform) are evaluated using multiple algorithms. KNN and RF exceed 99% classification accuracy, while KNN regression achieves R2 > 0.99 and low LODs for concentration prediction, supporting simultaneous gas quantification for diagnosis and monitoring.","Microchimica Acta (2024) 191:196  \n[https://doi.org/10.1007/s00604-024-06258-8](https://doi.org/10.1007/s00604-024-06258-8)  \nMetal oxide‑based gas sensor array for VOCs determination in complex mixtures using machine learning  \nShivam Singh1 · Sajana S1 · Poornima Varma2 · Gajje Sreelekha3 · Chandranath Adak3 · Rajendra P. Shukla4 ·  \nVinayak B. Kamble1  \nReceived: 26 December 2023 / Accepted: 12 February 2024 / Published online: 13 March 2024 © The Author(s) 2024  \nAbstract  \nDetection of volatile organic compounds (VOCs) from the breath is becoming a viable route for the early detection of diseases non-invasively. This paper presents a sensor array of 3 component metal oxides that give maximal cross-sensitivity and can successfully use machine learning methods to identify four distinct VOCs in a mixture. The metal oxide sensor array comprises NiO-Au (ohmic), CuO-Au (Schottky), and ZnO–Au (Schottky) sensors made by the DC reactive sputtering method and having a film thickness of 80–100 nm. The NiO and CuO films have ultrafine particle sizes of \u003C 50 nm and rough surface texture, while ZnO films consist of nanoscale platelets. This array was subjected to various VOC concentrations, including ethanol, acetone, toluene, and chloroform, one by one and in a pair/mix of gases. Thus, the response values show severe interference and departure from commonly observed power law behavior. The dataset obtained from individual gases and their mixtures were analyzed using multiple machine learning algorithms, such as Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree, Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Artificial Neural Network, and Support Vector Machine. KNN and RF have shown more than 99% accuracy in classifying different varying chemicals in the gas mixtures. In regression analysis, KNN has delivered the best results with an R2 value of more than 0.99 and LOD of 0.012 ppm, 0.015 ppm, 0.014 ppm, and 0.025 ppm for predicting the concentrations of acetone, toluene, ethanol, and chloroform, respectively, in complex mixtures. Therefore, it is demonstrated that the array utilizing the provided algorithms can classify and predict the concentrations of the four gases simultaneously for disease diagnosis and treatment monitoring.  \nKeywords Gas sensor · Sensor array · Metal oxides · VOCs · Complex mixture · Machine learning  \n* Chandranath Adak [chandranath@iitp.ac.in](chandranath@iitp.ac.in)  \n* Rajendra P. Shukla [r.p.shukla@utwente.nl](r.p.shukla@utwente.nl)  \n* Vinayak B. Kamble [kbvinayak@iisertvm.ac.in](kbvinayak@iisertvm.ac.in)  \n1 School of Physics, Indian Institute of Science Education and Research, Thiruvananthapuram, Kerala 695551, India  \n2 Dept. of CSE, Indian Institute of Information Technology, Lucknow, Uttar Pradesh 226002, India  \n3 Dept. of CSE, Indian Institute of Technology, Patna, Bihar 801106, India  \n4 BIOS Lab-On-a-Chip Group, MESA+ Institute for Nanotechnology, Max Planck Center for Complex Fluid Dynamics, University of Twente, P.O. Box 217, 7500 Enschede, The Netherlands  \nIntroduction  \nWith the advent of modern technology, there is much interest in reducing surgical involvement and enhancing the early identification of illness. Since it is quicker, less intrusive, and more accessible than a traditional clinical assessment, identifying certain illnesses employing human exhaled air has garnered great interest [1] . In this context, exhaled breath is the ideal non-invasive approach since it accurately captures the metabolic processes occurring within the human body [2] . Compared to lab tests, disease identification utilizing expiratory VOCs has emerged as the preferable approach for early screening. Besides, it has another excellent relevance for continuous breath monitoring for knowing health anomalies that appear transient or periodic. Breath monitoring has several benefits [3], the most significant among them being  \na simple, quick, and straightforward samplin","cbCaivoXPbislwUg","https://ap.wps.com/l/cbCaivoXPbislwUg","pdf",7820951,6,1,20,"English","en",105,"# Abstract\n# Introduction\n## Non-invasive breath-based VOC detection\n## VOC biomarkers and influencing factors\n## Limitations of current analytical methods","[{\"question\":\"What is the main goal of the sensor array in this work?\",\"answer\":\"To detect and identify four different VOCs in complex mixtures using a metal oxide sensor array combined with machine learning for simultaneous classification and concentration prediction.\"},{\"question\":\"Which VOCs and sensing materials are used?\",\"answer\":\"The experiments use ethanol, acetone, toluene, and chloroform, and the array consists of NiO-Au, CuO-Au, and ZnO–Au metal oxide sensors fabricated by DC reactive sputtering.\"},{\"question\":\"How do the machine learning models perform for classification and regression?\",\"answer\":\"KNN and Random Forest achieve over 99% accuracy for classifying varying chemicals in gas mixtures. For concentration estimation, KNN provides the best regression results with R2 greater than 0.99 and low limits of detection for each VOC.\"}]","Metal oxide-based gas sensor array for VOCs determination in complex mixtures using machine learning | PDF",1785902660,50,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"metal-oxide-based-gas-sensor-array-for-vocs-determination-in-complex-mixtures-using-machine-learning","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/metal-oxide-based-gas-sensor-array-for-vocs-determination-in-complex-mixtures-using-machine-learning/126036/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What is the main goal of the sensor array in this work?","Question",{"text":77,"@type":78},"To detect and identify four different VOCs in complex mixtures using a metal oxide sensor array combined with machine learning for simultaneous classification and concentration prediction.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which VOCs and sensing materials are used?",{"text":82,"@type":78},"The experiments use ethanol, acetone, toluene, and chloroform, and the array consists of NiO-Au, CuO-Au, and ZnO–Au metal oxide sensors fabricated by DC reactive sputtering.",{"name":84,"@type":75,"acceptedAnswer":85},"How do the machine learning models perform for classification and regression?",{"text":86,"@type":78},"KNN and Random Forest achieve over 99% accuracy for classifying varying chemicals in gas mixtures. 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