{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T22:45:54Z","timestamp":1768689954460,"version":"3.49.0"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2016,2,27]],"date-time":"2016-02-27T00:00:00Z","timestamp":1456531200000},"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>Electronic nose technology is used in many areas, and frequently in the beverage industry for classification and quality-control purposes. In this study, four different aroma data (strawberry, lemon, cherry, and melon) were obtained using a MOSES II electronic nose for the purpose of fruit classification. To improve the performance of the classification, the training phase of the neural network with two hidden layers was optimized using artificial bee colony algorithm (ABC), which is known to be successful in exploration. Test data were given to two different neural networks, each of which were trained separately with backpropagation (BP) and ABC, and average test performances were measured as 60% for the artificial neural network trained with BP and 76.39% for the artificial neural network trained with ABC. Training and test phases were repeated 30 times to obtain these average performance measurements. This level of performance shows that the artificial neural network trained with ABC is successful in classifying aroma data.<\/jats:p>","DOI":"10.3390\/s16030304","type":"journal-article","created":{"date-parts":[[2016,2,29]],"date-time":"2016-02-29T10:55:59Z","timestamp":1456743359000},"page":"304","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Classification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4279-0648","authenticated-orcid":false,"given":"M.","family":"Adak","sequence":"first","affiliation":[{"name":"Computer Engineering Department, Computer and Information Sciences Faculty, Sakarya University, 2nd Ring Street, Esentepe Campus, Serdivan, Sakarya 54187, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5005-8604","authenticated-orcid":false,"given":"Nejat","family":"Yumusak","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Computer and Information Sciences Faculty, Sakarya University, 2nd Ring Street, Esentepe Campus, Serdivan, Sakarya 54187, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,2,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5099","DOI":"10.3390\/s90705099","article-title":"Applications and Advances in Electronic-Nose Technologies","volume":"9","author":"Wilson","year":"2009","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2295","DOI":"10.3390\/s130202295","article-title":"Diverse Applications of Electronic-Nose Technologies in Agriculture and Forestry","volume":"13","author":"Wilson","year":"2013","journal-title":"Sensors"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"899","DOI":"10.3390\/s150100899","article-title":"Electronic-Nose Applications for Fruit Identification, Ripeness and Quality Grading","volume":"15","author":"Baietto","year":"2015","journal-title":"Sensors"},{"key":"ref_4","unstructured":"Pearce, T.C., Schiffman, S.S., Nagle, H.T., and Gardner, J.W. (2006). Handbook of Machine Olfaction: Electronic Nose Technology, John Wiley & Sons, Inc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.snb.2006.02.048","article-title":"A feature extraction algorithm for multi-peak signals in electronic noses","volume":"120","author":"Haddad","year":"2007","journal-title":"Sens. Actuators B Chem."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.snb.2004.05.044","article-title":"On predicting responses to mixtures in quartz microbalance sensors","volume":"106","author":"Carmel","year":"2005","journal-title":"Sens. Actuators B Chem."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/S0925-4005(03)00246-6","article-title":"An eNose algorithm for identifying chemicals and determining their concentration","volume":"93","author":"Carmel","year":"2003","journal-title":"Sens. Actuators B Chem."},{"key":"ref_8","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer-Verlag New York. [1st ed.]."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s10462-012-9328-0","article-title":"A comprehensive survey: Artificial bee colony (ABC) algorithm and applications","volume":"42","author":"Karaboga","year":"2012","journal-title":"Artif. Intell. Rev."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.jfoodeng.2015.03.003","article-title":"Evaluation of aroma release of gummy candies added with strawberry flavours by gas-chromatography\/mass-spectrometry and gas sensors arrays","volume":"167","author":"Pizzoni","year":"2015","journal-title":"J. Food Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.jchromb.2013.11.032","article-title":"Characterization of aroma compounds of Chinese famous liquors by gas chromatography-mass spectrometry and flash GC electronic-nose","volume":"945\u2013946","author":"Xiao","year":"2014","journal-title":"J. Chromatogr. B Anal. Technol. Biomed. Life Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1016\/j.foodcont.2012.02.024","article-title":"Application of electronic nose in Chinese spirits quality control and flavour assessment","volume":"26","author":"Liu","year":"2012","journal-title":"Food Control"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.jfoodeng.2014.06.004","article-title":"Artificial flavor perception of black tea using fusion of electronic nose and tongue response: A Bayesian statistical approach","volume":"142","author":"Roy","year":"2014","journal-title":"J. Food Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1016\/j.foodres.2013.02.005","article-title":"Evaluation of Chinese tea by the electronic nose and gas chromatography-mass spectrometry: Correlation with sensory properties and classification according to grade level","volume":"53","author":"Qin","year":"2013","journal-title":"Food Res. Int."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.jfoodeng.2011.12.037","article-title":"Instrumental testing of tea by combining the responses of electronic nose and tongue","volume":"110","author":"Roy","year":"2012","journal-title":"J. Food Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.snb.2009.02.025","article-title":"Electronic nose for black tea quality evaluation by an incremental RBF network","volume":"138","author":"Tudu","year":"2009","journal-title":"Sens. Actuators B Chem."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.snb.2007.12.032","article-title":"Preemptive identification of optimum fermentation time for black tea using electronic nose","volume":"131","author":"Bhattacharya","year":"2008","journal-title":"Sens. Actuators B Chem."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Dutta, A., Tudu, B., Bandyopadhyay, R., and Bhattacharyya, N. (2011, January 22\u201324). Black tea quality evaluation using electronic nose: An Artificial Bee Colony approach. Proceedings of the 2011 IEEE Recent Advances in Intelligent Computational Systems, Trivandrum, India.","DOI":"10.1109\/RAICS.2011.6069290"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.radphyschem.2014.09.002","article-title":"Application of mass spectrometry based electronic nose and chemometrics for fingerprinting radiation treatment","volume":"106","author":"Gupta","year":"2015","journal-title":"Radiat. Phys. Chem."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"10709","DOI":"10.3390\/s140610709","article-title":"Application of an Electronic Nose Instrument to Fast Classification of Polish Honey Types","volume":"14","author":"Dymerski","year":"2014","journal-title":"Sensors"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.foodres.2014.02.007","article-title":"Progress in authentication, typification and traceability of grapes and wines by chemometric approaches","volume":"60","author":"Versari","year":"2014","journal-title":"Food Res. Int."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8782","DOI":"10.3390\/s101008782","article-title":"Improved classification of Orthosiphon stamineus by data fusion of electronic nose and tongue sensors","volume":"10","author":"Zakaria","year":"2010","journal-title":"Sensors"},{"key":"ref_24","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":"Lebrun","year":"2008","journal-title":"Postharvest Biol. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.snb.2007.02.027","article-title":"Neural network and Bayesian network fusion models to fuse electronic nose and surface acoustic wave sensor data for apple defect detection","volume":"125","author":"Li","year":"2007","journal-title":"Sens. Actuators B Chem."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1016\/j.foodchem.2011.05.126","article-title":"Classification of Pecorino cheeses using electronic nose combined with artificial neural network and comparison with GC\u2013MS analysis of volatile compounds","volume":"129","author":"Cevoli","year":"2011","journal-title":"Food Chem."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"8879","DOI":"10.1016\/j.eswa.2010.06.008","article-title":"An expert system for perfume selection using artificial neural network","volume":"37","author":"Hanafizadeh","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"878","DOI":"10.3844\/jcssp.2009.878.882","article-title":"An Artificial Neural Networks-Based on-Line Monitoring Odor Sensing System","volume":"5","year":"2009","journal-title":"J. Comput. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1007\/s10916-007-9087-7","article-title":"E-Nose System for Anesthetic Dose Level Detection using Artificial Neural Network","volume":"31","author":"Saraoglu","year":"2007","journal-title":"J. Med. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1016\/j.bios.2004.03.002","article-title":"Detection of Mycobacterium tuberculosis (TB) in vitro and in situ using an electronic nose in combination with a neural network system","volume":"20","author":"Pavlou","year":"2004","journal-title":"Biosens. Bioelectron."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1016\/j.energy.2014.03.059","article-title":"Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey","volume":"69","author":"Uzlu","year":"2014","journal-title":"Energy"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"473","DOI":"10.14311\/NNW.2011.21.028","article-title":"The artificial bee colony algorithm in training artificial neural network for oil spill detection","volume":"21","author":"Ozkan","year":"2011","journal-title":"Neural Netw. World"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1080\/10916466.2010.497790","article-title":"An Improved Ant Colony Algorithm-Based ANN for Bottom Hole Pressure Prediction in Underbalanced Drilling","volume":"30","author":"Nasimi","year":"2012","journal-title":"Pet. Sci. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.saa.2015.01.086","article-title":"Isotherm and kinetics study of malachite green adsorption onto copper nanowires loaded on activated carbon: Artificial neural network modeling and genetic algorithm optimization","volume":"142","author":"Ghaedi","year":"2015","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1016\/j.saa.2014.08.011","article-title":"A hybrid artificial neural network and particle swarm optimization for prediction of removal of hazardous dye brilliant green from aqueous solution using zinc sulfide nanoparticle loaded on activated carbon","volume":"137","author":"Ghaedi","year":"2015","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1016\/j.saa.2014.04.175","article-title":"Artificial neural network and particle swarm optimization for removal of methyl orange by gold nanoparticles loaded on activated carbon and Tamarisk","volume":"132","author":"Ghaedi","year":"2014","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.bej.2014.01.002","article-title":"A combined artificial neural network modeling-particle swarm optimization strategy for improved production of marine bacterial lipopeptide from food waste","volume":"84","author":"Dhanarajan","year":"2014","journal-title":"Biochem. Eng. J."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.eswa.2010.06.070","article-title":"Robust identification of nonlinear complex systems using low complexity ANN and particle swarm optimization technique","volume":"38","author":"Majhi","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4229","DOI":"10.1109\/JSEN.2013.2265233","article-title":"Electronic Nose System Based on Quartz Crystal Microbalance Sensor for Blood Glucose and HbA1c Levels From Exhaled Breath Odor","volume":"13","author":"Saraoglu","year":"2013","journal-title":"IEEE Sens. J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.snb.2007.11.008","article-title":"Quantitative discrimination of the binary gas mixtures using a combinational structure of the probabilistic and multilayer neural networks","volume":"131","author":"Gulbag","year":"2008","journal-title":"Sens. Actuators B Chem."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1142\/S0218001411008932","article-title":"Gas Mixture Quantification Based on Hilbert-Huang Transform and Neural Network by a Single Sensor","volume":"25","author":"Wei","year":"2011","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_42","unstructured":"Karaboga, D. (2005). An Idea Based on Honey Bee Swarm for Numerical Optimization, Erciyes University. Technical Report TR06."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/3\/304\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:19:50Z","timestamp":1760210390000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/3\/304"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,2,27]]},"references-count":42,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2016,3]]}},"alternative-id":["s16030304"],"URL":"https:\/\/doi.org\/10.3390\/s16030304","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,2,27]]}}}