{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T14:03:53Z","timestamp":1765807433157,"version":"build-2065373602"},"reference-count":59,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2017,6,19]],"date-time":"2017-06-19T00:00:00Z","timestamp":1497830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.<\/jats:p>","DOI":"10.3390\/s17061434","type":"journal-article","created":{"date-parts":[[2017,6,19]],"date-time":"2017-06-19T10:29:26Z","timestamp":1497868166000},"page":"1434","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach"],"prefix":"10.3390","volume":"17","author":[{"given":"Yulin","family":"Jian","sequence":"first","affiliation":[{"name":"College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China"}]},{"given":"Daoyu","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China"}]},{"given":"Jia","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China"},{"name":"Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing 400715, China"}]},{"given":"Kun","family":"Lu","sequence":"additional","affiliation":[{"name":"High Tech Department, China International Engineering Consulting Corporation, Beijing 100048, China"}]},{"given":"Ying","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China"}]},{"given":"Tailai","family":"Wen","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China"}]},{"given":"Tanyue","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China"}]},{"given":"Shijie","family":"Zhong","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China"}]},{"given":"Qilong","family":"Xie","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.bios.2014.10.023","article-title":"Breath sensors for lung cancer diagnosis","volume":"65","author":"Adiguzel","year":"2015","journal-title":"Biosen. Bioelectron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.foodchem.2014.12.100","article-title":"Detecting internal quality of peanuts during storage using electronic nose responses combined with physicochemical methods","volume":"177","author":"Wei","year":"2015","journal-title":"Food Chem."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15954","DOI":"10.3390\/s131215954","article-title":"Quality evaluation of agricultural distillates using an electronic nose","volume":"13","author":"Dymerski","year":"2013","journal-title":"Sensors"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/S0925-4005(00)00491-3","article-title":"Electronic nose: A useful tool for monitoring environmental contamination","volume":"69","author":"Baby","year":"2000","journal-title":"Sens. Actuators B Chem."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/0925-4005(94)87085-3","article-title":"A brief history of electronic noses","volume":"18","author":"Gardner","year":"1994","journal-title":"Sens. Actuators B Chem."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s00604-006-0623-9","article-title":"Data analysis for electronic nose systems","volume":"156","author":"Scott","year":"2006","journal-title":"Microchim. Acta"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/S0003-2670(99)00604-2","article-title":"Classification of complex gas mixtures from automotive leather using an electronic nose","volume":"403","author":"Kalman","year":"2000","journal-title":"Anal. Chim. Acta"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.postharvbio.2007.09.010","article-title":"Discrimination of mango fruit maturity by volatiles using the electronic nose and gas chromatography","volume":"48","author":"Plotto","year":"2008","journal-title":"Postharvest Biol. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.foodcont.2012.10.035","article-title":"Electronic nose investigation of alicyclobacillus acidoterrestris, inoculated apple and orange juice treated by high hydrostatic pressure","volume":"32","author":"Dalmadi","year":"2013","journal-title":"Food Control"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.chemolab.2016.08.011","article-title":"Continuous chemical classification in uncontrolled environments with sliding windows","volume":"158","author":"Monroy","year":"2016","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.chemolab.2013.03.007","article-title":"Vocs classification based on the committee of classifiers coupled with single sensor signals","volume":"125","author":"Szczurek","year":"2013","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_12","first-page":"57","article-title":"Feature extraction from sensor data for detection of wound pathogen based on electronic nose","volume":"24","author":"Yan","year":"2012","journal-title":"Sens. Mater."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/0250-6874(82)80026-7","article-title":"Characteristics of semiconductor gas sensors I. Steady state gas response","volume":"3","author":"Clifford","year":"1982","journal-title":"Sens. Actuators"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1109\/10.752940","article-title":"Using neural networks and genetic algorithms to enhance performance in an electronic nose","volume":"46","author":"Kermani","year":"1999","journal-title":"IEEE Trans. Bio-Med. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/S0003-2670(01)01355-1","article-title":"Electronic nose based on metal oxide semiconductor sensors and pattern recognition techniques: Characterisation of vegetable oils","volume":"449","author":"Oliveros","year":"2001","journal-title":"Anal. Chim. Acta"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/j.chemolab.2015.07.001","article-title":"Comparison of semi-supervised and supervised approaches for classification of e-nose datasets: Case studies of tomato juices","volume":"146","author":"Hong","year":"2015","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2036","DOI":"10.1109\/JSEN.2015.2507580","article-title":"Robust bayesian inference for gas identification in electronic nose applications by using random matrix theory","volume":"16","author":"Hassan","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_18","first-page":"27","article-title":"Classification of essential oil composition in rosa damascena, mill. genotypes using an electronic nose","volume":"4","author":"Nikbakht","year":"2017","journal-title":"J. Appl. Res. Med. Aromat. Plants."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1147\/rd.83.0299","article-title":"Linear and nonlinear methods in pattern classification","volume":"8","author":"Greenberg","year":"1964","journal-title":"IBM J. Res. Dev."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1109\/TNSRE.2003.814441","article-title":"Comparison of linear, nonlinear, and feature selection methods for EEG signal classification","volume":"11","author":"Garrett","year":"2003","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_21","first-page":"83","article-title":"Classification of vegetable oils by linear discriminant analysis of electronic nose data","volume":"38","author":"Cordero","year":"1999","journal-title":"Anal. Chim. Acta"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4278","DOI":"10.1016\/j.foodchem.2013.07.009","article-title":"Rapid measuring and modelling flavour quality changes of oxidised chicken fat by electronic nose profiles through the partial least squares regression analysis","volume":"141","author":"Song","year":"2013","journal-title":"Food Chem."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"29","DOI":"10.2500\/ajr.2008.22.3126","article-title":"Use of an electronic nose for detection of biofilms","volume":"22","author":"Thaler","year":"2008","journal-title":"Am. J. Rhinol."},{"key":"ref_24","first-page":"86","article-title":"An empirical study for quantification of carcinogenic formaldehyde by integrating a probabilistic framework with spike latency patterns in an Electronic Nose","volume":"193","author":"Hassan","year":"2015","journal-title":"Sens. Transducers"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1016\/j.snb.2014.05.025","article-title":"Feature extraction of wound infection data for electronic nose based on a novel weighted KPCA","volume":"201","author":"Jia","year":"2014","journal-title":"Sens. Actuators B Chem."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1088\/0957-0233\/1\/5\/012","article-title":"Application of artificial neural networks to an electronic olfactory system","volume":"1","author":"Gardner","year":"1999","journal-title":"Meas. Sci. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1016\/j.snb.2004.12.005","article-title":"Classification of electronic nose data with support vector machines","volume":"107","author":"Pardo","year":"2005","journal-title":"Sens. Actuators B Chem."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"9179","DOI":"10.3390\/s101009179","article-title":"Development of a portable electronic nose system for the detection and classification of fruity odors","volume":"10","author":"Tang","year":"2010","journal-title":"Sensors"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/j.snb.2011.08.027","article-title":"Decision tree approach for classification and dimensionality reduction of electronic nose data","volume":"160","author":"Cho","year":"2011","journal-title":"Sens. Actuators B Chem."},{"key":"ref_30","unstructured":"Huang, G.B., Zhu, Q.Y., and Siew, C.K. (2004, January 25\u201329). Extreme learning machine: A new learning scheme of feedforward neural networks. Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, Budapest, Hungary."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.jfoodeng.2014.07.015","article-title":"Classification and regression of ELM, LVQ and SVM for e-nose data of strawberry juice","volume":"144","author":"Qiu","year":"2015","journal-title":"J. Food Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.neucom.2005.03.002","article-title":"Fully complex extreme learning machine","volume":"68","author":"Li","year":"2005","journal-title":"Neurocomputing"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1109\/TNN.2006.875977","article-title":"Universal approximation using incremental constructive feedforward networks with random hidden nodes","volume":"17","author":"Huang","year":"2006","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1016\/j.neucom.2007.07.025","article-title":"Incremental extreme learning machine with fully complex hidden nodes","volume":"71","author":"Huang","year":"2008","journal-title":"Neurocomputing"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","article-title":"Extreme learning machine for regression and multiclass classification","volume":"42","author":"Huang","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.neunet.2013.11.002","article-title":"Direct kernel perceptron (DKP): Ultra-fast kernel elm-based classification with non-iterative closed-form weight calculation","volume":"50","author":"Cernadas","year":"2014","journal-title":"Neural Netw."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/s00521-014-1568-2","article-title":"Fast detection of impact location using kernel extreme learning machine","volume":"27","author":"Fu","year":"2016","journal-title":"Neural Comput. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Peng, C., Yan, J., Duan, S.K., Wang, L.D., Jia, P.F., and Zhang, S.L. (2016). Enhancing electronic nose performance based on a Novel QPSO-KELM Model. Sensors, 16.","DOI":"10.3390\/s16040520"},{"key":"ref_39","first-page":"27","article-title":"Learning the kernel matrix with semi-definite programming","volume":"5","author":"Lanckriet","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"ref_40","first-page":"2211","article-title":"Multiple kernel learning algorithms","volume":"12","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Weiss, Y., Sch\u00f6lkopf, B., and Platt, J. (2006). A general and efficient multiple kernel learning algorithm. Advances in Neural Information Processing Systems 18, MIT Press.","DOI":"10.7551\/mitpress\/7503.001.0001"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.neucom.2013.09.072","article-title":"Multiple kernel extreme learning machine","volume":"149","author":"Liu","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2015\/178490","article-title":"Distance based multiple kernel ELM: A fast multiple kernel learning approach","volume":"2015","author":"Zhu","year":"2015","journal-title":"Math. Probl. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s00521-014-1709-7","article-title":"Multiple-kernel-learning-based extreme learning machine for classification design","volume":"27","author":"Li","year":"2016","journal-title":"Neural Comput. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s13042-011-0019-y","article-title":"Extreme learning machines: A survey","volume":"2","author":"Huang","year":"2011","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_46","first-page":"1730","article-title":"Kernel methods for pattern analysis: Properties of kernels","volume":"101","author":"Cristianini","year":"2004","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1098\/rsta.1909.0016","article-title":"Functions of positive and negative type and their connection with the theory of integral equations","volume":"209","author":"Mercer","year":"1909","journal-title":"Philos. Trans. R. Soc. Lond."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Smola, A.J., and Sch\u00f6lkopf, B. (2002). Learning with Kernels, MIT Press.","DOI":"10.7551\/mitpress\/4175.001.0001"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"952","DOI":"10.4028\/www.scientific.net\/AMM.475-476.952","article-title":"A model of classification for e-nose based on genetic algorithm","volume":"475\u2013476","author":"Jiang","year":"2013","journal-title":"Appl. Mech. Mater."},{"key":"ref_50","first-page":"124","article-title":"Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils","volume":"48","author":"Olivier","year":"2014","journal-title":"Food Chem."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1108\/SR-10-2012-710","article-title":"Improving the performance of electronic nose for wound infection detection using orthogonal signal correction and particle swarm optimization","volume":"34","author":"Feng","year":"2014","journal-title":"Sens. Rev."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1080\/10798587.2012.10643302","article-title":"Classification of electronic nose data in wound infection detection based on PSO-SVM combined with wavelet transform","volume":"18","author":"He","year":"2013","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"15198","DOI":"10.3390\/s150715198","article-title":"A novel feature extraction approach using window function capturing and QPSO-SVM for enhancing electronic nose performance","volume":"15","author":"Guo","year":"2015","journal-title":"Sensors"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1108\/SR-01-2015-0011","article-title":"Hybrid feature matrix construction and feature selection optimization-based multi-objective QPSO for electronic nose in wound infection detection","volume":"36","author":"Yan","year":"2016","journal-title":"Sens. Rev."},{"key":"ref_55","unstructured":"Sun, J., Feng, B., and Xu, W.-B. (2004, January 19\u201323). Particle swarm optimization with particles having quantum behavior. Proceedings of the 2004 Congress on Evolutionary Computation, Portland, OR, USA."},{"key":"ref_56","unstructured":"Sun, J., Xu, W.-B., and Feng, B. (2004, January 1\u20133). A global search strategy of quantum-behaved particle swarm optimization. Proceedings of the IEEE Conference on Cybernetics and Intelligent Systems, Singapore, Singapore."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Jia, P., Huang, T., Duan, S., Ge, L., Yan, J., and Wang, L. (2016). A novel semi-supervised electronic nose learning technique: M-Training. Sensors, 16.","DOI":"10.3390\/s16030370"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1016\/j.snb.2016.05.089","article-title":"Calibration transfer and drift counteraction in chemical sensor arrays using Direct Standardization","volume":"236","author":"Fonollosa","year":"2016","journal-title":"Sens. Actuators B Chem."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"27804","DOI":"10.3390\/s151127804","article-title":"Electronic nose feature extraction methods: A review","volume":"15","author":"Yan","year":"2015","journal-title":"Sensors"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/6\/1434\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:39:39Z","timestamp":1760207979000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/6\/1434"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,6,19]]},"references-count":59,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2017,6]]}},"alternative-id":["s17061434"],"URL":"https:\/\/doi.org\/10.3390\/s17061434","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2017,6,19]]}}}