{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T12:42:09Z","timestamp":1771332129443,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T00:00:00Z","timestamp":1660608000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T00:00:00Z","timestamp":1660608000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Comput"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s11047-022-09890-6","type":"journal-article","created":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T07:02:51Z","timestamp":1660633371000},"page":"195-223","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Deep learning networks with rough-refinement optimization for food quality assessment"],"prefix":"10.1007","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0987-3943","authenticated-orcid":false,"given":"Jin","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Kang","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Gexiang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Qiyu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Wangyang","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Weiping","family":"Jin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,16]]},"reference":[{"issue":"3","key":"9890_CR1","doi-asserted-by":"publisher","first-page":"511","DOI":"10.35833\/MPCE.2019.000255","volume":"8","author":"S Admasie","year":"2020","unstructured":"Admasie S, Bukhari SBA, Gush T, Haider R, Kim CH (2020) Intelligent islanding detection of multi-distributed generation using artificial neural network based on intrinsic mode function feature. J Mod Power Syst Clean Energy 8(3):511\u2013520","journal-title":"J Mod Power Syst Clean Energy"},{"issue":"2","key":"9890_CR2","first-page":"25","volume":"10","author":"G Agrawal","year":"2018","unstructured":"Agrawal G, Kang DK (2018) Wine quality classification with multilayer perceptron. Int J Internet Broadcast Commun 10(2):25\u201330","journal-title":"Int J Internet Broadcast Commun"},{"key":"9890_CR3","first-page":"10","volume":"1","author":"S Al-Dalalia","year":"2018","unstructured":"Al-Dalalia S, Zhenga F, Aleidc S, Abu-Ghoushd M, Samhourie M, Ammar AF (2018) Effect of dietary fibers from mango peels and date seeds on physicochemical properties and bread quality of Arabic bread. Int J Mod Res Eng Manage 1:10\u201324","journal-title":"Int J Mod Res Eng Manage"},{"key":"9890_CR4","doi-asserted-by":"crossref","unstructured":"Albawi S, Mohammed TA, Al-Zawi S (2017) Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET), pp.1\u20136. IEEE","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"issue":"1","key":"9890_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00500-016-2442-1","volume":"22","author":"I Aljarah","year":"2018","unstructured":"Aljarah I, Faris H, Mirjalili S (2018) Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Comput 22(1):1\u201315","journal-title":"Soft Comput"},{"key":"9890_CR6","doi-asserted-by":"crossref","unstructured":"Andrew AM (1993) Systems: an introductory analysis with applications to biology, control, and artificial intelligence, by John H. Holland MIT Press (Bradford Books), Cambridge, MA, 1992, xiv+ 211 pp.(Paperback\u00a3 13.50, cloth\u00a3 26.95). Robotica 11(5):489\u2013489","DOI":"10.1017\/S0263574700017136"},{"key":"9890_CR7","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","volume":"114","author":"M Belgiu","year":"2016","unstructured":"Belgiu M, Dr\u0103gu\u0163 L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24\u201331","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"9890_CR8","first-page":"1","volume-title":"Noise reduction in speech processing","author":"J Benesty","year":"2009","unstructured":"Benesty J, Chen J, Huang Y, Cohen I (2009) Pearson correlation coefficient. Noise reduction in speech processing. Springer, Berlin, pp 1\u20134"},{"key":"9890_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.aca.2015.04.042","volume":"891","author":"E Borr\u00e0s","year":"2015","unstructured":"Borr\u00e0s E, Ferr\u00e9 J, Boqu\u00e9 R, Mestres M, Ace\u00f1a L, Busto O (2015) Data fusion methodologies for food and beverage authentication and quality assessment: a review. Anal Chim Acta 891:1\u201314","journal-title":"Anal Chim Acta"},{"issue":"8","key":"9890_CR10","doi-asserted-by":"publisher","first-page":"1803","DOI":"10.3390\/foods10081803","volume":"10","author":"LC Class","year":"2021","unstructured":"Class LC, Kuhnen G, Rohn S, Kuballa J (2021) Diving deep into the data: a review of deep learning approaches and potential applications in foodomics. Foods 10(8):1803","journal-title":"Foods"},{"issue":"4","key":"9890_CR11","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1016\/j.dss.2009.05.016","volume":"47","author":"P Cortez","year":"2009","unstructured":"Cortez P, Cerdeira A, Almeida F, Matos T, Reis J (2009) Modeling wine preferences by data mining from physicochemical properties. Decis Support Syst 47(4):547\u2013553","journal-title":"Decis Support Syst"},{"issue":"8","key":"9890_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-016-0478-y","volume":"60","author":"R Cristin","year":"2017","unstructured":"Cristin R, Raj VC (2017) Consistency features and fuzzy-based segmentation for shadow and reflection detection in digital image forgery. SCIENCE CHINA Inf Sci 60(8):082101","journal-title":"SCIENCE CHINA Inf Sci"},{"key":"9890_CR13","unstructured":"Glutinous rice cake Webset. http:\/\/47.104.4.5:8080\/rqcdp\/a\/login;JSESSIONID=51159149d35343b7bb53839bc9880a68"},{"key":"9890_CR14","first-page":"1","volume":"1","author":"S Hosseinpour","year":"2021","unstructured":"Hosseinpour S, Martynenko A (2021) Food quality evaluation in drying: structuring of measurable food attributes into multi-dimensional fuzzy sets. Drying Technol 1:1\u201315","journal-title":"Drying Technol"},{"issue":"32","key":"9890_CR15","doi-asserted-by":"publisher","first-page":"12168","DOI":"10.1021\/acssuschemeng.0c03660","volume":"8","author":"P Hou","year":"2020","unstructured":"Hou P, Zhao B, Jolliet O, Zhu J, Wang P, Xu M (2020) Rapid prediction of chemical ecotoxicity through genetic algorithm optimized neural network models. ACS Sustain Chem Eng 8(32):12168\u201312176","journal-title":"ACS Sustain Chem Eng"},{"key":"9890_CR16","first-page":"318","volume-title":"International conference on modeling decisions for artificial intelligence","author":"D Karaboga","year":"2007","unstructured":"Karaboga D, Akay B, Ozturk C (2007) Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks. International conference on modeling decisions for artificial intelligence. Springer, Berlin, pp 318\u2013329"},{"issue":"11","key":"9890_CR17","doi-asserted-by":"publisher","first-page":"3173","DOI":"10.3390\/s20113173","volume":"20","author":"A Kaya","year":"2020","unstructured":"Kaya A, Ke\u00e7eli AS, Catal C, Tekinerdogan B (2020) Sensor failure tolerable machine learning-based food quality prediction model. Sensors 20(11):3173","journal-title":"Sensors"},{"key":"9890_CR18","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.jfoodeng.2015.10.007","volume":"171","author":"S Kiani","year":"2016","unstructured":"Kiani S, Minaei S, Ghasemi-Varnamkhasti M (2016) Fusion of artificial senses as a robust approach to food quality assessment. J Food Eng 171:230\u2013239","journal-title":"J Food Eng"},{"issue":"3","key":"9890_CR19","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.cosrev.2009.03.005","volume":"3","author":"M Luko\u0161evi\u010dius","year":"2009","unstructured":"Luko\u0161evi\u010dius M, Jaeger H (2009) Reservoir computing approaches to recurrent neural network training. Comput Sci Rev 3(3):127\u2013149","journal-title":"Comput Sci Rev"},{"key":"9890_CR20","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.foodchem.2018.03.008","volume":"257","author":"G Marrubini","year":"2018","unstructured":"Marrubini G, Appelblad P, Maietta M, Papetti A (2018) Hydrophilic interaction chromatography in food matrices analysis: an updated review. Food Chem 257:53\u201366","journal-title":"Food Chem"},{"key":"9890_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.swevo.2013.11.003","volume":"16","author":"SJ Nanda","year":"2014","unstructured":"Nanda SJ, Panda G (2014) A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol Comput 16:1\u201318","journal-title":"Swarm Evol Comput"},{"key":"9890_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2020.100297","volume":"38","author":"J Nayak","year":"2020","unstructured":"Nayak J, Vakula K, Dinesh P, Naik B, Pelusi D (2020) Intelligent food processing: journey from artificial neural network to deep learning. Comput Sci Rev 38:100297","journal-title":"Comput Sci Rev"},{"issue":"13","key":"9890_CR23","doi-asserted-by":"publisher","first-page":"1939","DOI":"10.1080\/10408398.2013.871693","volume":"55","author":"JH Qu","year":"2015","unstructured":"Qu JH, Liu D, Cheng JH, Sun DW, Ma J, Pu H, Zeng XA (2015) Applications of near-infrared spectroscopy in food safety evaluation and control: a review of recent research advances. Crit Rev Food Sci Nutr 55(13):1939\u20131954","journal-title":"Crit Rev Food Sci Nutr"},{"issue":"5","key":"9890_CR24","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1007\/s40333-020-0097-3","volume":"12","author":"MG Rostam","year":"2020","unstructured":"Rostam MG, Sadatinejad SJ, Malekian A (2020) Precipitation forecasting by large-scale climate indices and machine learning techniques. J Arid Land 12(5):854\u2013864","journal-title":"J Arid Land"},{"issue":"2","key":"9890_CR25","doi-asserted-by":"publisher","first-page":"429","DOI":"10.3390\/foods10020429","volume":"10","author":"C Ruiz-Capillas","year":"2021","unstructured":"Ruiz-Capillas C, Herrero AM, Pintado T, Delgado-Pando G (2021) Sensory analysis and consumer research in new meat products development. Foods 10(2):429","journal-title":"Foods"},{"key":"9890_CR26","doi-asserted-by":"publisher","first-page":"52","DOI":"10.35808\/ersj\/1407","volume":"22","author":"T Sadilek","year":"2019","unstructured":"Sadilek T, Journal ES (2019) Perception of food quality by consumers: literature review. Eur Res Stud J 22:52\u201362","journal-title":"Eur Res Stud J"},{"key":"9890_CR27","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.fct.2018.04.036","volume":"118","author":"M Salari","year":"2018","unstructured":"Salari M, Shahid ES, Afzali SH, Ehteshami M, Conti GO, Derakhshan Z, Sheibani SN (2018) Quality assessment and artificial neural networks modeling for characterization of chemical and physical parameters of potable water. Food Chem Toxicol 118:212\u2013219","journal-title":"Food Chem Toxicol"},{"key":"9890_CR28","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85\u2013117","journal-title":"Neural Netw"},{"key":"9890_CR29","unstructured":"Seiffert U (2001) Multiple layer perceptron training using genetic algorithms. In: ESANN, pp 159\u2013164"},{"issue":"11","key":"9890_CR30","doi-asserted-by":"publisher","first-page":"2539","DOI":"10.1007\/s00217-019-03369-y","volume":"245","author":"J Stangierski","year":"2019","unstructured":"Stangierski J, Weiss D, Kaczmarek A (2019) Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese. Eur Food Res Technol 245(11):2539\u20132547","journal-title":"Eur Food Res Technol"},{"key":"9890_CR31","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.neunet.2011.10.003","volume":"26","author":"JL Subirats","year":"2012","unstructured":"Subirats JL, Franco L, Jerez JM (2012) C-Mantec: a novel constructive neural network algorithm incorporating competition between neurons. Neural Netw 26:130\u2013140","journal-title":"Neural Netw"},{"key":"9890_CR32","first-page":"101","volume":"65","author":"Y Tao","year":"2018","unstructured":"Tao Y, Cloutie RS (2018) Voxelwise detection of cerebral microbleed in CADASIL patients by genetic algorithm and back propagation neural network. Adv Comput Sci Res 65:101\u2013105","journal-title":"Adv Comput Sci Res"},{"key":"9890_CR33","doi-asserted-by":"publisher","first-page":"929","DOI":"10.3389\/fchem.2019.00929","volume":"7","author":"B Vega-M\u00e1rquez","year":"2020","unstructured":"Vega-M\u00e1rquez B, Nepomuceno-Chamorro I, Jurado-Campos N, Rubio-Escudero C (2020) Deep learning techniques to improve the performance of olive oil classification. Front Chem 7:929","journal-title":"Front Chem"},{"issue":"3","key":"9890_CR34","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1039\/C5AY02724A","volume":"8","author":"A Wadehra","year":"2016","unstructured":"Wadehra A, Patil PS (2016) Application of electronic tongues in food processing. Anal Methods 8(3):474\u2013480","journal-title":"Anal Methods"},{"key":"9890_CR35","first-page":"83","volume":"7","author":"Y Wang","year":"2015","unstructured":"Wang Y, Yang B, Luo Y, He J, Tan H (2015) The application of big data mining in risk warning for food safety. Asian Agric Res 7:83\u201386","journal-title":"Asian Agric Res"},{"key":"9890_CR36","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1016\/j.foodcont.2017.04.013","volume":"79","author":"J Wang","year":"2017","unstructured":"Wang J, Yue H, Zhou Z (2017) An improved traceability system for food quality assurance and evaluation based on fuzzy classification and neural network. Food Control 79:363\u2013370","journal-title":"Food Control"},{"issue":"3","key":"9890_CR37","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.tics.2018.12.005","volume":"23","author":"JC Whittington","year":"2019","unstructured":"Whittington JC, Bogacz R (2019) Theories of error back-propagation in the brain. Trends Cogn Sci 23(3):235\u2013250","journal-title":"Trends Cogn Sci"},{"issue":"12","key":"9890_CR38","doi-asserted-by":"publisher","first-page":"2715","DOI":"10.3390\/s17122715","volume":"17","author":"W Wojnowski","year":"2017","unstructured":"Wojnowski W, Majchrzak T, Dymerski T, G\u0119bicki J, Namie\u015bnik J (2017) Portable electronic nose based on electrochemical sensors for food quality assessment. Sensors 17(12):2715","journal-title":"Sensors"},{"key":"9890_CR39","doi-asserted-by":"crossref","unstructured":"Wu M, Lin J, Shi S, Ren L, Wang Z (2020) Hybrid optimization-based GRU neural network for software reliability prediction. In: International conference of pioneering computer scientists, engineers and educators, pp 369\u2013383. Springer, Singapore.","DOI":"10.1007\/978-981-15-7984-4_27"},{"key":"9890_CR40","volume-title":"Nature-inspired metaheuristic algorithms","author":"XS Yang","year":"2010","unstructured":"Yang XS (2010) Nature-inspired metaheuristic algorithms. Luniver Press, Beckington"},{"issue":"2","key":"9890_CR41","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s00366-012-0254-1","volume":"29","author":"XS Yang","year":"2013","unstructured":"Yang XS (2013) Multiobjective firefly algorithm for continuous optimization. Eng Comput 29(2):175\u2013184","journal-title":"Eng Comput"},{"key":"9890_CR42","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.apenergy.2014.07.104","volume":"134","author":"F Yu","year":"2014","unstructured":"Yu F, Xu X (2014) A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network. Appl Energy 134:102\u2013113","journal-title":"Appl Energy"}],"container-title":["Natural Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11047-022-09890-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11047-022-09890-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11047-022-09890-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,26]],"date-time":"2023-03-26T21:24:08Z","timestamp":1679865848000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11047-022-09890-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,16]]},"references-count":42,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["9890"],"URL":"https:\/\/doi.org\/10.1007\/s11047-022-09890-6","relation":{},"ISSN":["1567-7818","1572-9796"],"issn-type":[{"value":"1567-7818","type":"print"},{"value":"1572-9796","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,16]]},"assertion":[{"value":"7 May 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}