{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T19:24:11Z","timestamp":1777490651284,"version":"3.51.4"},"reference-count":63,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,12]],"date-time":"2021-03-12T00:00:00Z","timestamp":1615507200000},"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>Aroma is one of the main attributes that consumers consider when appreciating and selecting a coffee; hence it is considered an important quality trait. However, the most common methods to assess aroma are based on expensive equipment or human senses through sensory evaluation, which is time-consuming and requires highly trained assessors to avoid subjectivity. Therefore, this study aimed to estimate the coffee intensity and aromas using a low-cost and portable electronic nose (e-nose) and machine learning modeling. For this purpose, triplicates of nine commercial coffee samples with different intensity levels were used for this study. Two machine learning models were developed based on artificial neural networks using the data from the e-nose as inputs to (i) classify the samples into low, medium, and high-intensity (Model 1) and (ii) to predict the relative abundance of 45 different aromas (Model 2). Results showed that it is possible to estimate the intensity of coffees with high accuracy (98%; Model 1), as well as to predict the specific aromas obtaining a high correlation coefficient (R = 0.99), and no under- or over-fitting of the models were detected. The proposed contactless, nondestructive, rapid, reliable, and low-cost method showed to be effective in evaluating volatile compounds in coffee, which is a potential technique to be applied within all stages of the production process to detect any undesirable characteristics on\u2013time and ensure high-quality products.<\/jats:p>","DOI":"10.3390\/s21062016","type":"journal-article","created":{"date-parts":[[2021,3,14]],"date-time":"2021-03-14T23:52:06Z","timestamp":1615765926000},"page":"2016","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":94,"title":["Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9207-9307","authenticated-orcid":false,"given":"Claudia","family":"Gonzalez Viejo","sequence":"first","affiliation":[{"name":"Digital Agriculture Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia"}]},{"given":"Eden","family":"Tongson","sequence":"additional","affiliation":[{"name":"Digital Agriculture Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0377-5085","authenticated-orcid":false,"given":"Sigfredo","family":"Fuentes","sequence":"additional","affiliation":[{"name":"Digital Agriculture Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,12]]},"reference":[{"key":"ref_1","unstructured":"Euromonitor-International (2020). Hot Drinks: Euromonitor from Trade Sources\/National Statistics, Euromonitor International."},{"key":"ref_2","unstructured":"Horsler, J. (2021, January 27). From Piccolos to Percolators: How Australians\u2019 Coffee Consumption Habits have Changed During COVID-19. Available online: https:\/\/www.nielsen.com\/au\/en\/insights\/article\/2020\/from-piccolos-to-percolators-how-australians-coffee-consumption-habits-have-changed-during-covid-19\/?utm_source=sfmc&utm_medium=email&utm_campaign=newswire&utm_content=5-20-2020."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7","DOI":"10.2202\/1556-3758.2002","article-title":"Optimized Neural Network for Instant Coffee Classification through an Electronic Nose","volume":"7","author":"Bona","year":"2011","journal-title":"Int. J. Food Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1007\/s10068-017-0168-1","article-title":"Discrimination and geographical origin prediction of washed specialty Bourbon coffee from different coffee growing areas in Rwanda by using electronic nose and electronic tongue","volume":"26","author":"Flambeau","year":"2017","journal-title":"Food Sci. Biotechnol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1016\/j.proeng.2012.09.310","article-title":"Chemometric discrimination of Philippine civet coffee using electronic nose and gas chromatography mass spectrometry","volume":"47","author":"Ongo","year":"2012","journal-title":"Procedia Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2321","DOI":"10.1021\/jf505691u","article-title":"Changes in the Aromatic Profile of Espresso Coffee as a Function of the Grinding Grade and Extraction Time: A Study by the Electronic Nose System","volume":"63","author":"Severini","year":"2015","journal-title":"J. Agric. Food Chem."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1007\/s002170050487","article-title":"Influence of provenance and roast degree on the composition of potent odorants in Arabica coffees","volume":"209","author":"Mayer","year":"1999","journal-title":"Eur. Food Res. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3099","DOI":"10.1111\/1750-3841.14815","article-title":"Chemometric authen-tication of Brazilian coffees based on chemical profiling","volume":"84","author":"Monteiro","year":"2019","journal-title":"J. Food Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1002\/jsfa.1304","article-title":"Influence of extraction temperature on the final quality of espresso coffee","volume":"83","author":"Andueza","year":"2003","journal-title":"J. Sci. Food Agric."},{"key":"ref_10","first-page":"312","article-title":"The Effect of Water Hardness on Volatile Compounds and Flavour of Filter Coffee","volume":"9","year":"2021","journal-title":"Turk. J. Agric. Food Sci. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"505","DOI":"10.3109\/09637486.2015.1064871","article-title":"The influence of different types of preparation (espresso and brew) on coffee aroma and main bioactive constituents","volume":"66","author":"Caprioli","year":"2015","journal-title":"Int. J. Food Sci. Nutr."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lolli, V., Acharjee, A., Angelino, D., Tassotti, M., Del Rio, D., Mena, P., and Caligiani, A. (2020). Chemical Characterization of Cap-sule-Brewed Espresso Coffee Aroma from the Most Widespread Italian Brands by HS-SPME\/GC-MS. Molecules, 25.","DOI":"10.3390\/molecules25051166"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.foodchem.2009.02.072","article-title":"Influence of storage conditions on aroma compounds in coffee pads using static headspace GC\u2013MS","volume":"116","author":"Huybrighs","year":"2009","journal-title":"Food Chem."},{"key":"ref_14","unstructured":"Dzung, N.H., Dzuan, L., and Tu, H.D. (2003, January 8\u201311). The role of sensory evaluation in food quality control, food research and development: A case of coffee study. Proceedings of the 8th Asean food Conference, Hanoi, Vietnam."},{"key":"ref_15","unstructured":"Kemp, S., Hollowood, T., and Hort, J. (2011). Sensory Evaluation: A Practical Handbook, Wiley."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Fuentes, S., Summerson, V., Gonzalez Viejo, C., Tongson, E., Lipovetzky, N., Wilkinson, K.L., Szeto, C., and Unnithan, R.R. (2020). As-sessment of Smoke Contamination in Grapevine Berries and Taint in Wines Due to Bushfires Using a Low-Cost E-Nose and an Artificial Intelligence Approach. Sensors, 20.","DOI":"10.3390\/s20185108"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"127688","DOI":"10.1016\/j.snb.2020.127688","article-title":"Development of a low-cost e-nose to assess aroma profiles: An artificial intelligence application to assess beer quality","volume":"308","author":"Viejo","year":"2020","journal-title":"Sens. Actuators B Chem."},{"key":"ref_18","unstructured":"Gonzalez Viejo Duran, C. (2020). The Effect of Bubble Formation Within Carbonated Drinks on the Brewage Foamability, Bubble Dynamics and Sensory Perception by Consumers. The University of Melbourne."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s00217-006-0308-y","article-title":"Evaluation of sensory attributes of coffee brews from robusta coffee roasted under different conditions","volume":"224","author":"Nebesny","year":"2006","journal-title":"Eur. Food Res. Technol."},{"key":"ref_20","unstructured":"Pardo, M., Faglia, G., Sberveglieri, G., and Quercia, L. (2001, January 21\u201323). Electronic nose for coffee quality control. IMTC 2001. Rediscovering Measurement in the Age of Informatics (Cat. No.01CH 37188). Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference, Budapest, Hungary."},{"key":"ref_21","first-page":"352","article-title":"Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose","volume":"299","author":"Persaud","year":"1982","journal-title":"Nat. Cell Biol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Viejo, C.G., and Fuentes, S. (2020). Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning. Fermentation, 6.","DOI":"10.3390\/fermentation6040104"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Santos, J.P., Lozano, J., and Aleixandre, M. (2017). Electronic Noses Applications in Beer Technology. Brew. Technol., 177.","DOI":"10.5772\/intechopen.68822"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Santos, J.P., and Lozano, J. (2015, January 11\u201313). Real time detection of beer defects with a hand held electronic nose. Proceedings of the 2015 10th Spanish Conference on Electron Devices (CDE), Madrid, Spain.","DOI":"10.1109\/CDE.2015.7087492"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, H., Li, Q., Yan, B., Zhang, L., and Gu, Y. (2019). Bionic Electronic Nose Based on MOS Sensors Array and Machine Learning Algo-rithms Used for Wine Properties Detection. Sensors, 19.","DOI":"10.3390\/s19010045"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.meatsci.2017.04.240","article-title":"Electronic noses: Powerful tools in meat quality as-sessment","volume":"131","author":"Wojnowski","year":"2017","journal-title":"Meat Sci."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1039\/C6AY02610A","article-title":"Sensor array optimization and discrimination of apple juices according to variety by an electronic nose","volume":"9","author":"Wu","year":"2017","journal-title":"Anal. Methods"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.chemolab.2016.05.013","article-title":"A portable electronic nose as an expert system for aroma-based classification of saffron","volume":"156","author":"Kiani","year":"2016","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.snb.2011.07.009","article-title":"Discrimination of green tea quality using the electronic nose technique and the human panel test, comparison of linear and nonlinear classification tools","volume":"159","author":"Chen","year":"2011","journal-title":"Sens. Actuators B Chem."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/S0925-4005(03)00367-8","article-title":"Tea quality prediction using a tin oxide-based electronic nose: An artificial intelligence approach","volume":"94","author":"Dutta","year":"2003","journal-title":"Sens. Actuators B Chem."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1268","DOI":"10.1016\/j.lwt.2007.08.018","article-title":"Quality grade identification of green tea using E-nose by CA and ANN","volume":"41","author":"Yu","year":"2008","journal-title":"LWT"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"S477","DOI":"10.1111\/j.1750-3841.2010.01828.x","article-title":"Gas Chromatography\/Olfactometry and Electronic Nose Analyses of Retronasal Aroma of Espresso and Correlation with Sensory Evaluation by an Artificial Neural Network","volume":"75","author":"Michishita","year":"2010","journal-title":"J. Food Sci."},{"key":"ref_34","first-page":"56","article-title":"Detection and Classification of Indonesian Civet and Non-Civet Coffee Based on Statistical Analysis Comparison Using E-Nose","volume":"13","author":"Wakhid","year":"2020","journal-title":"Int. J. Intell. Eng. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"C960","DOI":"10.1111\/j.1750-3841.2012.02851.x","article-title":"Evaluation of Coffee Roasting Degree by Using Electronic Nose and Artificial Neural Network for Off-line Quality Control","volume":"77","author":"Romani","year":"2012","journal-title":"J. Food Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7521","DOI":"10.1039\/D0GC02956D","article-title":"Artificial intelligence: The silver bullet for sustainable materials development","volume":"22","author":"HarDIan","year":"2020","journal-title":"Green Chem."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Shi, Z., Yuan, X., Yan, Y., Tang, Y., Li, J., Liang, H., Tong, L., and Qiao, Z. Techno-Economic Analysis of Metal\u2013Organic Frameworks for Adsorption Heat Pumps\/Chillers: From Directional Computational Screening, Machine Learning to Experiment. J. Mater. Chem. A, 2021.","DOI":"10.1039\/D0TA11747A"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1039\/C7SC04665K","article-title":"Machine learning for the structure\u2013energy\u2013property land-scapes of molecular crystals","volume":"9","author":"Musil","year":"2018","journal-title":"Chem. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"121082","DOI":"10.1016\/j.jclepro.2020.121082","article-title":"Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability","volume":"260","author":"Pham","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1038\/89044","article-title":"Classifi-cation and diagnostic prediction of cancers using gene expression profiling and artificial neural networks","volume":"7","author":"Khan","year":"2001","journal-title":"Nat. Med."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Tonda, A., Boukhelifa, N., Chabin, T., Barnab\u00e9, M., G\u00e9not, B., Lutton, E., and Perrot, N. (2018). Interactive Machine Learning for Appli-cations in Food Science. Human and Machine Learning, Springer.","DOI":"10.1007\/978-3-319-90403-0_22"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.compag.2018.06.035","article-title":"Automated grapevine cultivar classification based on machine learning using leaf morpho-colorimetry, fractal dimension and near-infrared spectroscopy parameters","volume":"151","author":"Fuentes","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Fuentes, S., Chacon, G., Torrico, D.D., Zarate, A., and Viejo, C.G. (2019). Spatial Variability of Aroma Profiles of Cocoa Trees Obtained through Computer Vision and Machine Learning Modelling: A Cover Photography and High Spatial Remote Sensing Application. Sensors, 19.","DOI":"10.20944\/preprints201904.0316.v1"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Fuentes, S., Viejo, C.G., Chauhan, S.S., Joy, A., Tongson, E., and Dunshea, F.R. (2020). Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modeling Using Integrated Visible\/Infrared Thermal Cameras. Sensors, 20.","DOI":"10.3390\/s20216334"},{"key":"ref_45","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Machine Learn. Res."},{"key":"ref_46","unstructured":"Martino, J.C.R. (2019). Hands-On Machine Learning with Microsoft Excel 2019: Build Complete Data Analysis Flows, from Data Collection to Visualization, Packt Publishing."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Viejo, C.G., Torrico, D.D., Dunshea, F.R., and Fuentes, S. (2019). Development of Artificial Neural Network Models to Assess Beer Acceptability Based on Sensory Properties Using a Robotic Pourer: A Comparative Model Approach to Achieve an Artificial Intelligence System. Beverages, 5.","DOI":"10.3390\/beverages5020033"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1002\/jsfa.8506","article-title":"Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and machine learning algorithms","volume":"98","author":"Viejo","year":"2018","journal-title":"J. Sci. Food Agric."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Viejo, C.G., Caboche, C.H., Kerr, E.D., Pegg, C.L., Schulz, B.L., Howell, K., and Fuentes, S. (2020). Development of a Rapid Method to Assess Beer Foamability Based on Relative Protein Content Using RoboBEER and Machine Learning Modeling. Beverages, 6.","DOI":"10.3390\/beverages6020028"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Viejo, C.G., and Fuentes, S. (2020). Beer Aroma and Quality Traits Assessment Using Artificial Intelligence. Fermentation, 6.","DOI":"10.3390\/fermentation6020056"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Fuentes, S., Torrico, D.D., Tongson, E., and Viejo, C.G. (2020). Machine Learning Modeling of Wine Sensory Profiles and Color of Vertical Vintages of Pinot Noir Based on Chemical Fingerprinting, Weather and Management Data. Sensors, 20.","DOI":"10.3390\/s20133618"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Deep, K., Jain, M., and Salhi, S. (2019). Logistics, Supply Chain and Financial Predictive Analytics: Theory and Practices, Springer.","DOI":"10.1007\/978-981-13-0872-7"},{"key":"ref_53","unstructured":"Beale, M.H., Hagan, M.T., and Demuth, H.B. (2018). Deep Learning Toolbox User\u2019s Guide, MathWorks Inc."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1111\/1750-3841.14114","article-title":"Assessment of Beer Quality Based on a Robotic Pourer, Computer Vision, and Machine Learning Algorithms Using Commercial Beers","volume":"83","author":"Viejo","year":"2018","journal-title":"J. Food Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1080\/10473220290095899","article-title":"Carbon Monoxide Exposure from Coffee Roasting","volume":"17","author":"Newton","year":"2002","journal-title":"Appl. Occup. Environ. Hyg."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1016\/j.foodchem.2018.08.068","article-title":"Comparative evaluation of the volatile profiles and taste properties of roasted coffee beans as affected by drying method and detected by electronic nose, electronic tongue, and HS-SPME-GC-MS","volume":"272","author":"Dong","year":"2019","journal-title":"Food Chem."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.ifset.2017.07.009","article-title":"Effect of reversed coffee grinding and roasting process on physicochemical properties including volatile compound profiles","volume":"44","author":"Lee","year":"2017","journal-title":"Innov. Food Sci. Emerg. Technol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1016\/j.foodres.2018.03.077","article-title":"Variability of single bean coffee volatile compounds of Arabica and robusta roasted coffees analysed by SPME-GC-MS","volume":"108","author":"Caporaso","year":"2018","journal-title":"Food Res. Int."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"5768","DOI":"10.1021\/jf0341767","article-title":"Real-Time Monitoring of 4-Vinylguaiacol, Guaiacol, and Phenol during Coffee Roasting by Resonant Laser Ionization Time-of-Flight Mass Spectrometry","volume":"51","author":"Dorfner","year":"2003","journal-title":"J. Agric. Food Chem."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.jfca.2016.01.006","article-title":"Furan, 2-methylfuran and 3-methylfuran in coffee on the Canadian market","volume":"47","author":"Becalski","year":"2016","journal-title":"J. Food Compos. Anal."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2575","DOI":"10.1016\/j.foodchem.2012.06.060","article-title":"Climatic factors directly impact the volatile organic compound fingerprint in green Arabica coffee bean as well as coffee beverage quality","volume":"135","author":"Bertrand","year":"2012","journal-title":"Food Chem."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.foodchem.2018.08.078","article-title":"Impact of consumer behavior on furan and furan-derivative exposure during coffee consumption. A comparison between brewing methods and drinking preferences","volume":"272","author":"Rahn","year":"2019","journal-title":"Food Chem."},{"key":"ref_63","unstructured":"(2020, December 15). The Good Scents Company. The Good Scents Company Information System. Available online: http:\/\/www.thegoodscentscompany.com\/data\/rw1038291.html."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/6\/2016\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:34:54Z","timestamp":1760160894000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/6\/2016"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,12]]},"references-count":63,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["s21062016"],"URL":"https:\/\/doi.org\/10.3390\/s21062016","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,12]]}}}