{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T15:48:17Z","timestamp":1791474497975,"version":"4.3.4"},"reference-count":36,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T00:00:00Z","timestamp":1734307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62404144"],"award-info":[{"award-number":["62404144"]}]},{"name":"National Natural Science Foundation of China","award":["2023NSFSC0467"],"award-info":[{"award-number":["2023NSFSC0467"]}]},{"name":"Natural Science Foundation of Sichuan Province","award":["62404144"],"award-info":[{"award-number":["62404144"]}]},{"name":"Natural Science Foundation of Sichuan Province","award":["2023NSFSC0467"],"award-info":[{"award-number":["2023NSFSC0467"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Micromachines"],"abstract":"<jats:p>This study addresses the challenge of multi-dimensional and small gas sensor data classification using a gelatin\u2013carbon black (CB-GE) composite film sensor, achieving 91.7% accuracy in differentiating gas types (ethanol, acetone, and air). Key techniques include Principal Component Analysis (PCA) for dimensionality reduction, the Synthetic Minority Over-sampling Technique (SMOTE) for data augmentation, and the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms for classification. PCA improved KNN and SVM classification, boosting the Area Under the Curve (AUC) scores by 15.7% and 25.2%, respectively. SMOTE increased KNN\u2019s accuracy by 2.1%, preserving data structure better than polynomial fitting. The results demonstrate a scalable approach to enhancing classification accuracy under data constraints. This approach shows promise for expanding gas sensor applicability in fields where data limitations previously restricted reliability and effectiveness.<\/jats:p>","DOI":"10.3390\/mi15121501","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T10:08:53Z","timestamp":1734343733000},"page":"1501","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["An Enhanced Gas Sensor Data Classification Method Using Principal Component Analysis and Synthetic Minority Over-Sampling Technique Algorithms"],"prefix":"10.3390","volume":"15","author":[{"given":"Xianzhang","family":"Zeng","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Shahzeb","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Cheng","sequence":"additional","affiliation":[{"name":"Graduate School of International Cultural Studies, Tohoku University, Kawauchi 41, Aoba Ku, Sendai 980-8577, Miyagi, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyang","family":"Xiao","sequence":"additional","affiliation":[{"name":"Pittsburgh Institute, Sichuan University, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cao","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanlin","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7961-4006","authenticated-orcid":false,"given":"Yubo","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingfei","family":"Xu","sequence":"additional","affiliation":[{"name":"Rehabilitation Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, China"},{"name":"Key Laboratory of Rehabilitation Medicine in Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuqing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"18381","DOI":"10.1109\/JSEN.2021.3091854","article-title":"MEMS gas sensors: A review","volume":"21","author":"Asri","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1021\/acssensors.3c02043","article-title":"On-Demand Optimization of Colorimetric Gas Sensors Using a Knowledge-Aware Algorithm-Driven Robotic Experimental Platform","volume":"9","author":"Ai","year":"2024","journal-title":"ACS Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15827","DOI":"10.1109\/JSEN.2024.3386898","article-title":"A highly selective MEMS-based gas sensor with gelatin-carbon black composite film fabricated by the thin-film-needle-coating method","volume":"24","author":"Xiao","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_4","first-page":"9508909","article-title":"Rapid Recognition and Concentration Prediction of Gas Mixtures Based on SMLP","volume":"73","author":"Sun","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ahmed, T., Arefin, S., Parvez, R., Jahin, F., Sumaiya, F., and Hasan, M. (June, January 30). Advancing Mobile Sensor Data Authentication: Application of Deep Machine Learning Models. Proceedings of the 2024 IEEE International Conference on Electro Information Technology (eIT), Eau Claire, WI, USA.","DOI":"10.1109\/eIT60633.2024.10609953"},{"key":"ref_6","unstructured":"Xiong, Y., Xue, Y., Chen, Y., Wan, H., and Wang, P. A Gas Classification Algorithm of Electronic Noses Based on Convolutional Spiking Neural Network. Proceedings of the Electrochemical Society Meeting Abstracts 239."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kok, C.L., Ho, C.K., Tan, F.K., and Koh, Y.Y. (2024). Machine learning-based feature extraction and classification of emg signals for intuitive prosthetic control. Appl. Sci., 14.","DOI":"10.3390\/app14135784"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pachiyannan, P., Alsulami, M., Alsadie, D., Saudagar, A.K.J., AlKhathami, M., and Poonia, R.C. (2024). A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing. Technologies, 12.","DOI":"10.3390\/technologies12010004"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ribeiro, P., Marques, J.A.L., Pordeus, D., Zacarias, L., Leite, C.F., Sobreira-Neto, M.A., Peixoto Jr, A.A., de Oliveira, A., do Vale Madeiro, J.P., and Rodrigues, P.M. (2024). Machine learning-based cardiac activity non-linear analysis for discriminating COVID-19 patients with different degrees of severity. Biomed. Signal Process. Control, 87.","DOI":"10.1016\/j.bspc.2023.105558"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"18252","DOI":"10.1109\/ACCESS.2023.3247571","article-title":"A Research of Gas Open-Set Identification Based on Data Augmentation Algorithm","volume":"11","author":"Zhu","year":"2023","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1007\/s10208-023-09603-0","article-title":"Subexponential-time algorithms for sparse PCA","volume":"24","author":"Ding","year":"2024","journal-title":"Found. Comput. Math."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.1109\/ACCESS.2021.3133497","article-title":"FA-LSTM: A novel toxic gas concentration prediction model in pollutant environment","volume":"10","author":"Cong","year":"2021","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"114383","DOI":"10.1016\/j.measurement.2024.114383","article-title":"A novel high accuracy fast gas detection algorithm based on multi-task learning","volume":"228","author":"Wang","year":"2024","journal-title":"Measurement"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"18459","DOI":"10.1109\/JSEN.2021.3091582","article-title":"A novel gas recognition and concentration estimation model for an artificial olfactory system with a gas sensor array","volume":"21","author":"Zhang","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"135272","DOI":"10.1016\/j.snb.2024.135272","article-title":"A novel electronic nose classification prediction method based on TETCN","volume":"405","author":"Wu","year":"2024","journal-title":"Sens. Actuators B Chem."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6020","DOI":"10.1109\/JSEN.2020.2972542","article-title":"Nanowire-based sensor array for detection of cross-sensitive gases using PCA and machine learning algorithms","volume":"20","author":"Khan","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"131418","DOI":"10.1016\/j.snb.2022.131418","article-title":"An electronic nose based on carbon nanotube-titanium dioxide hybrid nanostructures for detection and discrimination of volatile organic compounds","volume":"357","author":"Shooshtari","year":"2022","journal-title":"Sens. Actuators B Chem."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"170668","DOI":"10.1109\/ACCESS.2019.2955086","article-title":"Grouped SMOTE with noise filtering mechanism for classifying imbalanced data","volume":"7","author":"Cheng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1109\/TCAD.2015.2474392","article-title":"On-chip droop-induced circuit delay prediction based on support-vector machines","volume":"35","author":"Ye","year":"2015","journal-title":"IEEE Trans. Comput. -Aided Des. Integr. Circuits Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1007\/s10553-021-01297-w","article-title":"Identification of Natural Gas Components Using the Support Vector Machine Model","volume":"57","author":"Huang","year":"2021","journal-title":"Chem. Technol. Fuels Oils"},{"key":"ref_23","unstructured":"Fix, E. (1985). Discriminatory Analysis: Nonparametric Discrimination, Consistency Properties, USAF School of Aviation Medicine."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.dib.2015.02.011","article-title":"Synthetic benchmarks for machine olfaction: Classification, segmentation and sensor damage","volume":"3","author":"Ziyatdinov","year":"2015","journal-title":"Data Brief"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chen, Y., Xia, W., Chen, D., Zhang, T., and Song, K. (2022, January 16\u201319). A mixed gas composition identification method based on sample augmentation. Proceedings of the 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Ottawa, ON, Canada.","DOI":"10.1109\/I2MTC48687.2022.9806499"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"102860","DOI":"10.1016\/j.jvcir.2020.102860","article-title":"Adaptive deep feature aggregation using Fourier transform and low-pass filtering for robust object retrieval","volume":"72","author":"Zhou","year":"2020","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2934","DOI":"10.1016\/j.jbiomech.2007.02.019","article-title":"Adaptive usage of the Butterworth digital filter","volume":"40","author":"Erer","year":"2007","journal-title":"J. Biomech."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1016\/j.snb.2014.09.001","article-title":"Bioinspired early detection through gas flow modulation in chemo-sensory systems","volume":"206","author":"Ziyatdinov","year":"2015","journal-title":"Sens. Actuators B Chem."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"306","DOI":"10.4103\/jmss.jmss_139_21","article-title":"Gas array sensors based on electronic nose for detection of tuna (euthynnus affinis) contaminated by Pseudomonas aeruginosa","volume":"12","author":"Astuti","year":"2022","journal-title":"J. Med. Signals Sens."},{"key":"ref_30","unstructured":"Kang, S.-J., Kim, J.-Y., Jeong, I.-K., Islam, M.M., Im, K., and Kim, J.-M. (2018, January 13\u201315). An Improved Gas Classification Technique Using New Features and Support Vector Machines. Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018), Porto, Portugal."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.fuel.2018.12.064","article-title":"Prediction of ash-deformation temperature based on grey-wolf algorithm and support-vector machine","volume":"241","author":"Xiao","year":"2019","journal-title":"Fuel"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez P\u00e9rez, S.N., P\u00e9rez Reynoso, F.D., Guti\u00e9rrez, C.A.G., Cos\u00edo Le\u00f3n, M.D.l.\u00c1., and Ortega Palacios, R. (2023). EOG signal classification with wavelet and supervised learning algorithms KNN, SVM and DT. Sensors, 23.","DOI":"10.3390\/s23094553"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"104465","DOI":"10.1016\/j.infrared.2022.104465","article-title":"Self-repairing infrared electronic nose based on ensemble learning and PCA fault diagnosis","volume":"127","author":"Wang","year":"2022","journal-title":"Infrared Phys. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1515\/ijb-2020-0091","article-title":"The area under the generalized receiver-operating characteristic curve","volume":"18","year":"2022","journal-title":"Int. J. Biostat."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1177\/0272989X8500500204","article-title":"An evaluation of methods for estimating the area under the receiver operating characteristic (ROC) curve","volume":"5","author":"Centor","year":"1985","journal-title":"Med. Decis. Mak."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1007\/s10618-022-00829-0","article-title":"The area under the ROC curve as a measure of clustering quality","volume":"36","author":"Jaskowiak","year":"2022","journal-title":"Data Min. Knowl. Discov."}],"container-title":["Micromachines"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-666X\/15\/12\/1501\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:53:16Z","timestamp":1760115196000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-666X\/15\/12\/1501"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,16]]},"references-count":36,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["mi15121501"],"URL":"https:\/\/doi.org\/10.3390\/mi15121501","relation":{},"ISSN":["2072-666X"],"issn-type":[{"value":"2072-666X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,16]]}}}