[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119204-en":3,"doc-seo-119204-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119204,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Detecting Honey Adulteration - Advanced Approach Using UF-GC Coupled with Machine Learning","A novel approach to detecting honey adulteration combines ultrafast gas chromatography (UF-GC) with advanced machine learning. Support vector regression (SVR) and LASSO feature selection were used to predict adulteration in orange blossom and sunflower honeys. SVR achieved R2 values above 0.90 for combined honey types, while modeling the two types separately raised performance to beyond 0.99. LASSO showed particular strength when honey types were handled individually. The UF-GC and machine learning integration enables reliable detection and supports future extensions to other foods to strengthen food authenticity.","Article  \nDetecting Honey Adulteration: Advanced Approach Using UF-GC Coupled with Machine Learning  \nIrene Punta-Sánchez 1, Tomasz Dymerski 2, José Luis P. Calle 1, Ana Ruiz-Rodríguez 1, Marta Ferreiro-González 1, * and Miguel Palma 1  \n1 Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain; [irene.punta@uca.es](irene.punta@uca.es) (I.P.-S.); [joseluis.perezcalle@uca.es](joseluis.perezcalle@uca.es) (J.L.P.C.); [ana.ruiz@uca.es](ana.ruiz@uca.es) (A.R.-R.); [miguel.palma@uca.es](miguel.palma@uca.es) (M.P.)  \n2 Department of Analytical Chemistry, Faculty of Chemistry, Gda ´nsk University of Technology, 11/12 G, Narutowicza Str., 80-233 Gdansk, Poland; [tomasz.dymerski@pg.edu.pl](tomasz.dymerski@pg.edu.pl)  \n* Correspondence: marta.ferreiro@uca.es; Tel.: +34-956-01-6363  \nCitation: Punta-Sánchez, I.; Dymerski, T.; Calle, J.L.P.; Ruiz-Rodríguez, A.; Ferreiro-González, M.; Palma, M.  \nDetecting Honey Adulteration: Advanced Approach Using UF-GC Coupled with Machine Learning. Sensors 2024, 24, 7481. [https://](https://)[ ](https://)[doi.org/10.3390/s24237481](doi.org/10.3390/s24237481)  \nAcademic Editor: James Covington  \nReceived: 9 September 2024  \nRevised: 31 October 2024  \nAccepted: 18 November 2024  \nPublished: 23 November 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nAbstract: This article introduces a novel approach to detecting honey adulteration by combining ultrafast gas chromatography (UF-GC) with advanced machine learning techniques. Machine learning models, particularly support vector regression (SVR) and least absolute shrinkage and selection operator (LASSO), were applied to predict adulteration in orange blossom (OB) and sunflower (SF) honeys. The SVR model achieved R2 values above 0 .90 for combined honey types. Treating OB and SF honeys separately resulted in a significant accuracy improvement, with R2 values exceeding 0 .99. LASSO proved especially effective when honey types were treated individually. The integration of UF-GC with machine learning not only provides a reliable method for detecting honey adulteration, but also sets a precedent for future research in the application of this technique to other food products, potentially enhancing food authenticity across the industry.  \nKeywords: honey; adulteration; ultra-fast gas chromatography; machine learning; regression; classification; food control; volatile compounds  \n1. Introduction  \nHoney is a natural sweetener produced by Apis mellifera L. bees from the nectar of plants or from secretions of living parts of plants or excretions of plant-sucking insects on the living parts of plants [1] . The composition and properties of honey depend on the botanical origin of the source of nectars or secretions, climatic conditions, environmental factors, and bee farming practices. Honey can be classified into two categories depending on the secretions of plants used for their synthesis: blossom honey made from the nectar of flowers, and honeydew honey made from secretions of all living parts of plants other than flowers or excretions of insects [2,3] .  \nMonofloral honey is a type of honey produced from a single botanical source holding distinctive organoleptic properties. [4] . Monofloral honey is generally considered to be more valuable than multifloral honey because it is more difficult to produce, and it has a unique flavor profile that is specific to the flower or plant from which it was derived. In the case of orange blossom (OB) and sunflower (SF) honeys, their distinct flavors and aromas make them highly desirable. OB honey has a delicate ","cbCaiiEzHABQuaRP","https://ap.wps.com/l/cbCaiiEzHABQuaRP","pdf",3004501,1,14,"English","en",105,"# Introduction\n## Honey types and adulteration risks\n## Detection techniques for adulterants","[{\"question\":\"What method combination is proposed to detect honey adulteration?\",\"answer\":\"The approach integrates ultrafast gas chromatography (UF-GC) with machine learning models for prediction.\"},{\"question\":\"Which machine learning techniques are used in the study?\",\"answer\":\"Support vector regression (SVR) and LASSO are used to model and select relevant predictive variables.\"},{\"question\":\"How does treating orange blossom and sunflower honeys separately affect accuracy?\",\"answer\":\"Separately modeling the two honey types significantly improves performance, with R2 values exceeding 0.99.\"}]","Detecting Honey Adulteration - Advanced Approach Using UF-GC Coupled with Machine Learning | PDF",1785723085,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"detecting-honey-adulteration-advanced-approach-using-uf-gc-coupled-with-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/detecting-honey-adulteration-advanced-approach-using-uf-gc-coupled-with-machine-learning/119204/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What method combination is proposed to detect honey adulteration?","Question",{"text":75,"@type":76},"The approach integrates ultrafast gas chromatography (UF-GC) with machine learning models for prediction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning techniques are used in the study?",{"text":80,"@type":76},"Support vector regression (SVR) and LASSO are used to model and select relevant predictive variables.",{"name":82,"@type":73,"acceptedAnswer":83},"How does treating orange blossom and sunflower honeys separately affect accuracy?",{"text":84,"@type":76},"Separately modeling the two honey types significantly improves performance, with R2 values exceeding 0.99.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]