{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T06:11:23Z","timestamp":1778220683603,"version":"3.51.4"},"reference-count":49,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,12]],"date-time":"2020-08-12T00:00:00Z","timestamp":1597190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Ministry of Science and Technology of the People\u00b4s Republic of China","award":["2017YFB1400100"],"award-info":[{"award-number":["2017YFB1400100"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876059"],"award-info":[{"award-number":["61876059"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The brown core is an internal disorder that significantly affects the palatability and economic value of Chinese pears. In this study, a framework that includes a back-propagation neural network (BPNN) and extreme learning machine (ELM) (BP-ELMNN) was proposed for the detection of brown core in the Chinese pear variety Huangguan. The odor data of pear were collected using a metal oxide semiconductor (MOS) electronic nose (E-nose). Principal component analysis was used to analyze the complexity of the odor emitted by pears with brown cores. The performances of several machine learning algorithms, i.e., radial basis function neural network (RBFNN), BPNN, and ELM, were compared with that of the BP-ELMNN. The experimental results showed that the proposed framework provided the best results for the test samples, with an accuracy of 0.9683, a macro-precision of 0.9688, a macro-recall of 0.9683, and a macro-F1 score of 0.9685. The results demonstrate that the use of machine learning algorithms for the analysis of E-nose data is a feasible and non-destructive method to detect brown core in pears.<\/jats:p>","DOI":"10.3390\/s20164499","type":"journal-article","created":{"date-parts":[[2020,8,12]],"date-time":"2020-08-12T09:14:49Z","timestamp":1597223689000},"page":"4499","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A Machine Learning Method for the Detection of Brown Core in the Chinese Pear Variety Huangguan Using a MOS-Based E-Nose"],"prefix":"10.3390","volume":"20","author":[{"given":"Hao","family":"Wei","sequence":"first","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0073-1383","authenticated-orcid":false,"given":"Yu","family":"Gu","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China"},{"name":"Department of Chemistry, Institute of Inorganic and Analytical Chemistry, Goethe-University, Max-von-Laue-Str. 9, 60438 Frankfurt, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1016\/j.scienta.2018.11.091","article-title":"Storage of pears","volume":"246","author":"Saquet","year":"2019","journal-title":"Sci. Hortic."},{"key":"ref_2","unstructured":"EFSA (2011). The EFSA Comprehensive European Food Consumption Database, EFSA."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.foodchem.2013.12.010","article-title":"Chemical composition and antioxidant and anti-inflammatory potential of peels and flesh from 10 different pear varieties (Pyrus spp.)","volume":"152","author":"Li","year":"2014","journal-title":"Food Chem."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s12263-018-0620-8","article-title":"Food intake biomarkers for apple, pear, and stone fruit","volume":"13","author":"Ulaszewska","year":"2018","journal-title":"Genes Nutr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s00709-014-0665-5","article-title":"Calcium content and its correlated distribution with skin browning spot in bagged Huangguan pear","volume":"252","author":"Dong","year":"2015","journal-title":"Protoplasma"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"108671","DOI":"10.1016\/j.scienta.2019.108671","article-title":"Exogenous ethylene alleviates chilling injury of \u2018Huangguan\u2019 pear by enhancing the proline content and antioxidant activity","volume":"257","author":"Wei","year":"2019","journal-title":"Sci. Hortic."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"108820","DOI":"10.1016\/j.scienta.2019.108820","article-title":"Control of brown heart in Huangguan pears with 1-methylcyclopropene microbubbles treatment","volume":"259","author":"Xu","year":"2020","journal-title":"Sci. Hortic."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"108738","DOI":"10.1016\/j.scienta.2019.108738","article-title":"Evaluation of 1-methylcyclopropene (1-MCP) and low temperature conditioning (LTC) to control brown of Huangguan pears","volume":"259","author":"Xu","year":"2020","journal-title":"Sci. Hortic."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1016\/j.lwt.2014.09.005","article-title":"The effects of modified atmosphere packaging on core browning and the expression patterns of PPO and PAL genes in \u2018Yali\u2019 pears during cold storage","volume":"60","author":"Cheng","year":"2015","journal-title":"LWT Food Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1016\/j.foodchem.2017.07.118","article-title":"Application of propyl gallate alleviates pericarp browning in harvested longan fruit by modulating metabolisms of respiration and energy","volume":"240","author":"Lin","year":"2018","journal-title":"Food Chem."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"108634","DOI":"10.1016\/j.scienta.2019.108634","article-title":"The combined effect of ultraviolet-C irradiation and lysozyme coatings treatment on control of brown heart in Huangguan pears","volume":"256","author":"Xu","year":"2019","journal-title":"Sci. Hortic."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1016\/j.scienta.2017.07.031","article-title":"Hypobaric storage reduced core browning of Yali pear fruits","volume":"225","author":"Li","year":"2017","journal-title":"Sci. Hortic."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1800","DOI":"10.1007\/s12161-016-0739-4","article-title":"Electronic Nose as a Tool for Monitoring the Authenticity of Food. A Review","volume":"10","author":"Chmielewski","year":"2017","journal-title":"Food Anal. Methods"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.foodchem.2017.03.011","article-title":"The prediction of food additives in the fruit juice based on electronic nose with chemometrics","volume":"230","author":"Qiu","year":"2017","journal-title":"Food Chem."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.foodres.2018.04.009","article-title":"An Electronic Nose for Royal Delicious Apple Quality Assessment\u2014A Tri-layer Approach","volume":"109","author":"Ezhilan","year":"2018","journal-title":"Food Res. Int."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1016\/j.foodchem.2016.11.049","article-title":"Novel method for the producing area identification of Zhongning Goji berries by electronic nose","volume":"221","author":"Li","year":"2017","journal-title":"Food Chem."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"104858","DOI":"10.1016\/j.compag.2019.104858","article-title":"Peach growth cycle monitoring using an electronic nose","volume":"163","author":"Voss","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.compag.2018.09.032","article-title":"A novel method using MOS electronic nose and ELM for predicting postharvest quality of cherry tomato fruit treated with high pressure argon","volume":"154","author":"Feng","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1016\/j.foodres.2017.11.054","article-title":"Characterization of volatile profile from ten different varieties of Chinese jujubes by HS-SPME\/GC\u2013MS coupled with E-nose","volume":"105","author":"Chen","year":"2018","journal-title":"Food Res. Int."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"827","DOI":"10.3389\/fmicb.2019.00827","article-title":"Application of Machine Learning in Microbiology","volume":"10","author":"Qu","year":"2019","journal-title":"Front. Microbiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.neucom.2013.02.054","article-title":"Comparing the learning effectiveness of BP, ELM, I-ELM, and SVM for corporate credit ratings","volume":"128","author":"Zhong","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.aca.2009.01.009","article-title":"Artificial neural networks in foodstuff analyses: Trends and perspectives A review","volume":"635","author":"Marini","year":"2009","journal-title":"Anal. Chim. Acta"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.artmed.2018.10.001","article-title":"Towards automatic encoding of medical procedures using convolutional neural networks and autoencoders","volume":"93","author":"Deng","year":"2018","journal-title":"Artif. Intell. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2472","DOI":"10.1016\/j.snb.2017.09.040","article-title":"A review of algorithms for SAW sensors e-nose based volatile compound identification","volume":"255","author":"Hotel","year":"2018","journal-title":"Sens. Actuators B Chem."},{"key":"ref_26","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_27","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_28","unstructured":"(2020, June 28). Portable Electronic Nose. Available online: https:\/\/airsense.com\/en\/products\/portable-electronic-nose."},{"key":"ref_29","first-page":"2121","article-title":"Adaptive Subgradient Methods for Online Learning and Stochastic Optimization","volume":"12","author":"Duchi","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_30","unstructured":"Tieleman, T., and Hinton, G. (2020, June 30). Lecture 6.5-RMSProp, COURSERA: Neural Networks for Machine Learning 2012. Available online: https:\/\/www.cs.toronto.edu\/~tijmen\/csc321\/slides\/lecture_slides_lec6.pdf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"72403","DOI":"10.1109\/ACCESS.2019.2919987","article-title":"Effective Neural Network Training with a New Weighting Mechanism-Based Optimization Algorithm","volume":"7","author":"Yu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_32","first-page":"555","article-title":"An Image Classification Method Based on Deep Neural Network with Energy Model","volume":"117","author":"Yang","year":"2018","journal-title":"CMES-Comput. Model. Eng. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"An, Y., Wang, X., Chu, R., Yue, B., Wu, L., Cui, J., and Qu, Z. (2019). Event classification for natural gas pipeline safety monitoring based on long short-term memory network and Adam algorithm. Struct. Health Monit.","DOI":"10.1177\/1475921719879071"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"104814","DOI":"10.1016\/j.knosys.2019.06.022","article-title":"SMOTE based class-specific extreme learning machine for imbalanced learning","volume":"187","author":"Raghuwanshi","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"123151","DOI":"10.1109\/ACCESS.2019.2937599","article-title":"A Multitask Learning Framework for Multi-Property Detection of Wine","volume":"7","author":"Yu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sun, X., Wan, Y., and Guo, R. (2009, January 14\u201316). Chinese wine classification using BPNN through combination of micrographs\u2019 shape and structure features. Proceedings of the 2009 Fifth International Conference on Natural Computation, Tianjin, China.","DOI":"10.1109\/ICNC.2009.409"},{"key":"ref_37","unstructured":"Cheng, S., and Zhou, X. (2013, January 20\u201322). Network traffic prediction based on BPNN optimized by self-adaptive immune genetic algorithm. Proceedings of the 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer, Shengyang, China."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"35898","DOI":"10.1109\/ACCESS.2018.2890553","article-title":"A BP Neural Network Recommendation Algorithm Based on Cloud Model","volume":"7","author":"Tang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_39","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":"2011","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.asoc.2016.02.039","article-title":"Multi layer ELM-RBF for multi-label learning","volume":"43","author":"Zhang","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_41","first-page":"3311","article-title":"Identification of Wine According to Grape Variety Using Near-Infrared Spectroscopy Based on Radial Basis Function Neural Networks and Least-Squares Support Vector Machines","volume":"75","author":"Yu","year":"2017","journal-title":"Food Anal. Methods"},{"key":"ref_42","first-page":"3097","article-title":"Fast and Robust RBF Neural Network Based on Global K-Means Clustering with Adaptive Selection Radius for Sound Source Angle Estimation","volume":"66","author":"Yang","year":"2018","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.neucom.2016.07.046","article-title":"An improved maximum spread algorithm with application to complex-valued RBF neural networks","volume":"216","author":"Liu","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3421","DOI":"10.1016\/j.neucom.2007.12.003","article-title":"Fully complex-valued radial basis function networks: Orthogonal least squares regression and classification","volume":"71","author":"Chen","year":"2008","journal-title":"Neurocomputing"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, H., Li, Q., Yan, B., Zhang, L., and Gu, Y. (2018). Bionic Electronic Nose Based on MOS Sensors Array and Machine Learning Algorithms Used for Wine Properties Detection. Sensors, 19.","DOI":"10.3390\/s19010045"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.asoc.2014.09.022","article-title":"New Neural Network-based Approaches for GPS GDOP Classification based on Neuro-Fuzzy Inference System, Radial Basis Function, and Improved Bee Algorithm","volume":"25","author":"Azarbad","year":"2014","journal-title":"Appl. Soft Comput."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.neunet.2015.12.011","article-title":"Two fast and accurate heuristic RBF learning rules for data classification","volume":"75","author":"Rouhani","year":"2016","journal-title":"Neural Netw."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2995","DOI":"10.1109\/TIT.2019.2893916","article-title":"Data-Dependent Generalization Bounds for Multi-Class Classification","volume":"65","author":"Lei","year":"2019","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_49","first-page":"256","article-title":"Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification","volume":"6","author":"Patil","year":"2013","journal-title":"Int. J. Comput. Sci. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/16\/4499\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:59:25Z","timestamp":1760176765000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/16\/4499"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,12]]},"references-count":49,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["s20164499"],"URL":"https:\/\/doi.org\/10.3390\/s20164499","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,12]]}}}