[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126921-en":3,"doc-seo-126921-105":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},126921,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices","Fruit juices are among the most widely consumed beverages worldwide and are produced under strict regulatory requirements. This study develops a headspace–gas chromatography–ion mobility spectrometry (HS-GC-IMS) workflow combined with machine-learning algorithms to characterize juices from orange, pineapple, and apple-and-grape raw materials. Ion mobility sum spectra (IMSS) are generated after optimizing HS conditions via Box–Behnken design and response surface methodology, then classified using supervised models.","foods   \nArticle  \nNovel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices  \nJos² Luis P. Calle, Mercedes V¡zquez-Espinosa , Marta Barea-Sepólveda , Ana Ruiz-Rodr½guez , Marta Ferreiro-Gonz¡lez * and Miguel Palma   \nCitation: Calle, J.L.P.; VázquezEspinosa, M.; Barea-Sepúlveda, M.; Ruiz-Rodríguez, A.; Ferreiro-González, M.; Palma, M. Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices. Foods 2023, 12, 2536. [https://](https://)[ ](https://)[doi.org/10.3390/foods12132536](doi.org/10.3390/foods12132536)  \nAcademic Editors: Marco Arlorio and Matteo Bordiga  \nReceived: 7 June 2023  \nRevised: 26 June 2023  \nAccepted: 28 June 2023  \nPublished: 29 June 2023  \nCopyright: © 2023 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/)) .  \nDepartment of Analytical Chemistry, Faculty of Sciences, University of Cadiz, IVAGRO, ceiA3, Puerto Real,  \n11510 Cadiz, Spain; [joseluis.perezcalle@uca.es](joseluis.perezcalle@uca.es) (J.L.P.C.); mercedes.vazquez@uca.es (M.V.-E.); [marta.barea@gm.uca.es](marta.barea@gm.uca.es) (M.B.-S.); [ana.ruiz@uca.es](ana.ruiz@uca.es) (A.R.-R.); [miguel.palma@uca.es](miguel.palma@uca.es) (M.P.)  \n* Correspondence: marta.ferreiro@uca.es; Tel.: +34-956-016-359  \nAbstract: Fruit juices are one of the most widely consumed beverages worldwide, and their production is subject to strict regulations. Therefore, this study presents a methodology based on the use of headspace–gas chromatography–ion mobility spectrometry (HS-GC-IMS) in combination with machine-learning algorithms for the characterization juices of different raw material (orange, pineapple, or apple and grape) . For this purpose, the ion mobility sum spectrum (IMSS) was used. First, an optimization of the most important conditions in generating the HS was carried out using a Box–Behnken design coupled with a response surface methodology. The following factors were studied: temperature, time, and sample volume. The optimum values were 46.3 􀀎 C, 5 min, and 750 􀀖L, respectively. Once the conditions were optimized, 76 samples of the different types of juices were analyzed and the IMSS was combined with different machine-learning algorithms for its characterization. The exploratory analysis by hierarchical cluster analysis (HCA) and principal component analysis (PCA) revealed a clear tendency to group the samples according to the type of fruit juice and, to a lesser extent, the commercial brand. The combination of IMSS with supervised classification techniques reported an excellent result with 100% accuracy on the test set for support vector machines (SVM) and random forest (RF) models regarding the specific fruit used. Nevertheless, all the models have proven to be an effective alternative for characterizing and classifying the different types of juices.  \nKeywords: fruit juices; ion mobility spectrometry; optimization; Box–Behnken; support vector machine; random forest; machine learning  \n1. Introduction  \nThe production of fruit juices is regulated by the European directive 2012/12 EU, which defines fruit juice as the product derived 100% from the squeezing of healthy and ripe fruit [1] . These beverages are consumed all over the world, as, in addition to their flavor, they are beneficial for health. They are an excellent source of nutrients, especially phenolic compounds and carotenoids. In fact, numerous studies have demonstrated the importance of fruit juice consumption for different functions such as cardiovascular functions [2], cognitive health [3], lipid metabolism [4], etc. In addition to the beneficial properties of juices, they can be consumed throughou","cbCaiskP4NsR1f2h","https://ap.wps.com/l/cbCaiskP4NsR1f2h","pdf",2073641,1,13,"English","en",105,"# Abstract\n# 1. Introduction","[{\"question\":\"What analytical approach does the study propose for fruit juice discrimination?\",\"answer\":\"It uses headspace–gas chromatography–ion mobility spectrometry (HS-GC-IMS) with ion mobility sum spectra (IMSS) combined with machine-learning algorithms.\"},{\"question\":\"How were the headspace generation conditions optimized?\",\"answer\":\"The conditions were optimized using a Box–Behnken design coupled with response surface methodology, varying temperature, time, and sample volume.\"},{\"question\":\"Which machine-learning models performed best on the test set?\",\"answer\":\"Support vector machines (SVM) and random forest (RF) achieved 100% accuracy on the test set for discriminating the specific fruit used.\"}]","Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices | PDF",1785935668,33,{"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},"novel-method-based-on-ion-mobility-spectrometry-combined-with-machine-learning-for-the-discrimination-of-fruit-juices","",{"@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/novel-method-based-on-ion-mobility-spectrometry-combined-with-machine-learning-for-the-discrimination-of-fruit-juices/126921/",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","2026-08-05",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 analytical approach does the study propose for fruit juice discrimination?","Question",{"text":75,"@type":76},"It uses headspace–gas chromatography–ion mobility spectrometry (HS-GC-IMS) with ion mobility sum spectra (IMSS) combined with machine-learning algorithms.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the headspace generation conditions optimized?",{"text":80,"@type":76},"The conditions were optimized using a Box–Behnken design coupled with response surface methodology, varying temperature, time, and sample volume.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine-learning models performed best on the test set?",{"text":84,"@type":76},"Support vector machines (SVM) and random forest (RF) achieved 100% accuracy on the test set for discriminating the specific fruit used.","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"]