{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T11:03:34Z","timestamp":1777892614209,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005073","name":"Agency For Defense Development","doi-asserted-by":"publisher","award":["UI220039ZD"],"award-info":[{"award-number":["UI220039ZD"]}],"id":[{"id":"10.13039\/501100005073","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Chemical warfare agents pose a serious threat due to their extreme toxicity, necessitating swift the identification of chemical gases and individual responses to the identified threats. Fourier transform infrared (FTIR) spectroscopy offers a method for remote material analysis, particularly in detecting colorless and odorless chemical agents. In this paper, we propose a deep neural network utilizing a semi-supervised autoencoder (SSAE) for the classification of chemical gases based on FTIR spectra. In contrast to traditional methods, the SSAE concurrently trains an autoencoder and a classifier attached to a latent vector of the autoencoder, enhancing feature extraction for classification. The SSAE was evaluated on laboratory-collected FTIR spectra, demonstrating a superior classification performance compared to existing methods. The efficacy of the SSAE lies in its ability to generate denser cluster distributions in latent vectors, thereby enhancing gas classification. This study established a consistent experimental environment for hyperparameter optimization, offering valuable insights into the influence of latent vectors on classification performance.<\/jats:p>","DOI":"10.3390\/s24113601","type":"journal-article","created":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T05:58:00Z","timestamp":1717394280000},"page":"3601","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Semi-Supervised Autoencoder for Chemical Gas Classification with FTIR Spectrum"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5153-908X","authenticated-orcid":false,"given":"Hee-Deok","family":"Jang","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6661-3327","authenticated-orcid":false,"given":"Seokjoon","family":"Kwon","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0601-8845","authenticated-orcid":false,"given":"Hyunwoo","family":"Nam","sequence":"additional","affiliation":[{"name":"Chem-Bio Technology Center, Advanced Defense Science and Technology Research Institute, Agency for Defense Development, Daejeon 34186, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6496-4189","authenticated-orcid":false,"given":"Dong Eui","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wu, J., Qu, G., Yan, L., Wang, R., Guo, P., Yang, Y., and Li, X. (2023). Efficient Removal of Representative Chemical Agents by Rapid and Sufficient Adsorption via Magnetic Graphene Oxide Composites. Appl. Sci., 13.","DOI":"10.3390\/app131910731"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1039\/D2CS00651K","article-title":"Recent advances in fluorescent and colorimetric chemosensors for the detection of chemical warfare agents: A legacy of the 21st century","volume":"52","author":"Kumar","year":"2023","journal-title":"Chem. Soc. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"556","DOI":"10.5302\/J.ICROS.2023.23.0074","article-title":"Deep Learning Algorithm with Residual Blocks for Chemical Gas Concentration Estimation","volume":"29","author":"Jang","year":"2023","journal-title":"J. Inst. Control. Robot. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1109\/MSP.2013.2294804","article-title":"Long-Wave Infrared Hyperspectral Remote Sensing of Chemical Clouds: A focus on signal processing approaches","volume":"31","author":"Manolakis","year":"2014","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1109\/36.298007","article-title":"Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach","volume":"32","author":"Harsanyi","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","first-page":"76730G","article-title":"Feasibility study of detection of hazardous airborne pollutants using passive open-path FTIR","volume":"Volume 7673","author":"Lieberman","year":"2010","journal-title":"Proceedings of the Advanced Environmental, Chemical, and Biological Sensing Technologies VII"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Shi, Z., Huang, M., Qian, L., Han, W., Zhang, G., and Lu, X. (2024). Unmanned Helicopter Airborne Fourier Transform Infrared Spectrometer Remote Sensing System for Hazardous Vapors Detection. Appl. Sci., 14.","DOI":"10.3390\/app14041367"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1002\/fact.1008","article-title":"Toxic cloud imaging by infrared spectrometry: A scanning FTIR system for identification and visualization","volume":"5","author":"Harig","year":"2001","journal-title":"Field Anal. Chem. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yu, H.G., Kim, W., Park, D.J., Chang, D.E., and Nam, H. (2021, January 12\u201315). Design of a Cooperative Chemical Agent (CA) Detection Algorithm with the Hyperspectral Imaging System. Proceedings of the 2021 21st International Conference on Control, Automation and Systems (ICCAS), Jeju, Republic of Korea.","DOI":"10.23919\/ICCAS52745.2021.9650013"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8649","DOI":"10.1109\/TGRS.2020.2989526","article-title":"Adaptive Detection Algorithm for Hazardous Clouds Based on Infrared Remote Sensing Spectroscopy and the LASSO Method","volume":"58","author":"Li","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2940","DOI":"10.1109\/JSTARS.2020.2998451","article-title":"Design of a hard expectation-maximization-based normalized matched filter (EM-NMF) for the detection of chemical warfare agents under background contamination","volume":"13","author":"Yu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1049\/ell2.12088","article-title":"Design of cooperative matched filter for detection of chemical agents","volume":"57","author":"Yu","year":"2021","journal-title":"Electron. Lett."},{"key":"ref_13","first-page":"75","article-title":"Intelligent detection algorithm of hazardous gases for FTIR-based hyperspectral imaging system using SVM classifier","volume":"Volume 10198","author":"Yu","year":"2017","journal-title":"Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII"},{"key":"ref_14","first-page":"107680H","article-title":"Efficient detection algorithm of chemical warfare agents for FTIR-based hyperspectral imagery using SVM classifier","volume":"Volume 10768","author":"Silny","year":"2018","journal-title":"Proceedings of the Imaging Spectrometry XXII: Applications, Sensors, and Processing"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1080\/10739149.2015.1007144","article-title":"Characterization of Hazardous Gases Using an Infrared Hyperspectral Imaging System","volume":"43","author":"Lee","year":"2015","journal-title":"Instrum. Sci. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.1109\/36.934072","article-title":"Hyperspectral subpixel target detection using the linear mixing model","volume":"39","author":"Manolakis","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1080\/10739149.2018.1524385","article-title":"Development of a radiative transfer model for the determination of toxic gases by Fourier transform\u2013infrared spectroscopy with a support vector machine algorithm","volume":"47","author":"Nam","year":"2019","journal-title":"Instrum. Sci. Technol."},{"key":"ref_18","first-page":"1043317","article-title":"Hazardous gas detection for FTIR-based hyperspectral imaging system using DNN and CNN","volume":"Volume 10433","author":"Huckridge","year":"2017","journal-title":"Proceedings of the Electro-Optical and Infrared Systems: Technology and Applications XIV"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hesham, A., Zeyad, L., ElZahraa, F., ElGamal, A., Mohammed, P., Sakr, M., and Sabry, Y.M. (2021, January 5\u20137). Deep Learning Enabling Analysis of Exhaled Breath Using Fourier Transform Spectroscopy in the Mid-Infrared. Proceedings of the 2021 Tenth International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt.","DOI":"10.1109\/ICICIS52592.2021.9694262"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, X., Yan, Y., and Xu, S. (2023, January 11\u201313). Multi-Scale Convolutional Neural Networks for the Quantitative Analysis of Multi-Component Gases. Proceedings of the 2023 2nd International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC), Mianyang, China.","DOI":"10.1109\/RAIIC59453.2023.10281089"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"C80","DOI":"10.1364\/AO.477409","article-title":"Rapid identification of breast cancer subtypes using micro-FTIR and machine learning methods","volume":"62","author":"Farooq","year":"2023","journal-title":"Appl. Opt."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"9711","DOI":"10.1021\/acs.analchem.1c00867","article-title":"Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models","volume":"93","author":"Enders","year":"2021","journal-title":"Anal. Chem."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Das, S., Paramane, A., Kumari, S., and Chatterjee, S. (2023, January 9\u201312). Deep Learning Aided Classification of Ageing Condition of Natural Ester Oils Using FTIR Analysis. Proceedings of the 2023 IEEE 3rd International Conference on Sustainable Energy and Future Electric Transportation (SEFET), Bhubaneswar, India.","DOI":"10.1109\/SeFeT57834.2023.10246005"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"127332","DOI":"10.1016\/j.foodchem.2020.127332","article-title":"Evaluation of an autoencoder as a feature extraction tool for near-infrared spectroscopic discriminant analysis","volume":"331","author":"Jo","year":"2020","journal-title":"Food Chem."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4618","DOI":"10.1039\/C9SC06240H","article-title":"Spectral deep learning for prediction and prospective validation of functional groups","volume":"11","author":"Fine","year":"2020","journal-title":"Chem. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chen, Y., Chen, Y., Feng, X., Yang, X., Zhang, J., Qiu, Z., and He, Y. (2019). Variety Identification of Orchids Using Fourier Transform Infrared Spectroscopy Combined with Stacked Sparse Auto-Encoder. Molecules, 24.","DOI":"10.3390\/molecules24132506"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"100031","DOI":"10.1016\/j.dche.2022.100031","article-title":"Predicting crude oil properties using fourier-transform infrared spectroscopy (FTIR) and data-driven methods","volume":"3","author":"Yang","year":"2022","journal-title":"Digit. Chem. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e2022GC010530","DOI":"10.1029\/2022GC010530","article-title":"SIGMA: Spectral interpretation using gaussian mixtures and autoencoder","volume":"24","author":"Tung","year":"2023","journal-title":"Geochem. Geophys. Geosyst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"De Oliveira, J.P.G., Bastos-Filho, C.J.A., and Oliveira, S.C. (2022, January 13\u201315). Chemical sample classification using autoencoder-based spectroscopy. Proceedings of the 2022 SBFoton International Optics and Photonics Conference (SBFoton IOPC), Recife, Brazil.","DOI":"10.1109\/SBFotonIOPC54450.2022.9993214"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., and Liu, D. (2016, January 16\u201321). Semi Supervised Autoencoder. Proceedings of the Neural Information Processing, Kyoto, Japan.","DOI":"10.1007\/978-3-319-46687-3"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Haiyan, W., Haomin, Y., Xueming, L., and Haijun, R. (2015, January 12\u201314). Semi-supervised autoencoder: A joint approach of representation and classification. Proceedings of the 2015 International Conference on Computational Intelligence and Communication Networks (CICN), Jabalpur, India.","DOI":"10.1109\/CICN.2015.275"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2196","DOI":"10.1109\/TBME.2016.2631620","article-title":"Semi-supervised stacked label consistent autoencoder for reconstruction and analysis of biomedical signals","volume":"64","author":"Gogna","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.asoc.2019.01.021","article-title":"A semi-supervised auto-encoder using label and sparse regularizations for classification","volume":"77","author":"Chai","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.neucom.2022.02.017","article-title":"A semi-supervised autoencoder for autism disease diagnosis","volume":"483","author":"Yin","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"107327","DOI":"10.1016\/j.ymssp.2020.107327","article-title":"A hybrid classification autoencoder for semi-supervised fault diagnosis in rotating machinery","volume":"149","author":"Wu","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"104619","DOI":"10.1016\/j.chemolab.2022.104619","article-title":"Semi-supervised deep learning framework for milk analysis using NIR spectrometers","volume":"228","author":"Said","year":"2022","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Plana Rius, F., Philipsen, M.P., Mirats Tur, J.M., Moeslund, T.B., Angulo Bah\u00f3n, C., and Casas, M. (2022). Autoencoders for Semi-Supervised Water Level Modeling in Sewer Pipes with Sparse Labeled Data. Water, 14.","DOI":"10.3390\/w14030333"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the Dimensionality of Data with Neural Networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Caterini, A.L., and Chang, D.E. (2018). Deep Neural Networks in a Mathematical Framework, Springer.","DOI":"10.1007\/978-3-319-75304-1"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.neucom.2020.07.061","article-title":"On hyperparameter optimization of machine learning algorithms: Theory and practice","volume":"415","author":"Yang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Jang, H.D., Park, J.H., Nam, H., and Chang, D.E. (December, January 27). Deep neural networks for gas concentration estimation and the effect of hyperparameter optimization on the estimation performance. Proceedings of the 2022 22nd International Conference on Control, Automation and Systems (ICCAS), Jeju, Republic of Korea.","DOI":"10.23919\/ICCAS55662.2022.10003915"},{"key":"ref_42","first-page":"26","article-title":"Hyperparameter optimization for machine learning models based on Bayesian optimization","volume":"17","author":"Wu","year":"2019","journal-title":"J. Electron. Sci. Technol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1016\/j.oregeorev.2015.01.001","article-title":"Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines","volume":"71","year":"2015","journal-title":"Ore Geol. Rev."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"23295","DOI":"10.1007\/s00521-021-05842-w","article-title":"Fine-tuned support vector regression model for stock predictions","volume":"35","author":"Dash","year":"2023","journal-title":"Neural Comput. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3601\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:52:51Z","timestamp":1760107971000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3601"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,3]]},"references-count":44,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24113601"],"URL":"https:\/\/doi.org\/10.3390\/s24113601","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,3]]}}}