[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128554-en":3,"doc-seo-128554-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128554,549768064622,"Anda","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","A Machine Learning Approach Investigating Consumers' Familiarity with and Involvement in the Just Noticeable Color Difference and Cured Color Characterization Scale - read online free","Study investigates how visual color perception relates to instrumental color in dry-cured ham, emphasizing the Just Noticeable Color Difference (JNCD). It also examines how consumer involvement and familiarity shape color-related associations and JNCD judgments. Ham slices are measured instrumentally and photographed, while consumers score and match picture colors and report involvement, familiarity, and demographics. Consumers are clustered by involvement, JNCD is computed per cluster, and an interpretable machine-learning model links visual appraisal to instrumental color.","foods   \nArticle  \nA Machine Learning Approach Investigating Consumers'Familiarity with and Involvement in the Just Noticeable Color Difference and Cured Color Characterization Scale  \nGuillermo Ripoll 1,2, Begoña Panea 1,2, * and Mar½a 􀂁ngeles Latorre 2,3  \nCitation: Ripoll, G.; Panea, B.; Latorre, M.Á. A Machine Learning Approach Investigating Consumers'Familiarity with and Involvement in the Just Noticeable Color Difference and Cured Color Characterization Scale. Foods 2023, 12, 4426 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)foods12244426  \nAcademic Editor: Youngseung Lee  \nReceived: 30 October 2023  \nRevised: 7 December 2023  \nAccepted: 8 December 2023  \nPublished: 10 December 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/)) .  \n1 Animal Science Department, Centro de Investigaciân y Tecnolog½a Agroalimentaria de Aragân (CITA), Avda. Montañana 930, 50059 Zaragoza, Spain; [gripoll@aragon.es](gripoll@aragon.es)  \n2 Agrifood Institute of Aragon-IA2, CITA-University of Zaragoza, Avda. Miguel Servet, 177,  \n50013 Zaragoza, Spain; [malatorr@unizar.es](malatorr@unizar.es)  \n3 Facultad de Veterinaria, Universidad de Zaragoza, Avda. Miguel Servet, 177, 50013 Zaragoza, Spain  \n* Correspondence: bpanea@cita-aragon.es  \nAbstract: The aim of this study was to elucidate the relations between the visual color perception and the instrumental color of dry-cured ham, with a specific focus on determining the Just Noticeable Color Difference (JNCD) . Additionally, we studied the influence of consumer involvement and familiarity on color-related associations and JNCD. Slices of ham were examined to determine their instrumental color and photos were taken. Consumers were surveyed about color scoring and matching of the pictures; they were also asked about their involvement in food, familiarity with cured ham, and sociodemographic characteristics. Consumers were clustered according to their level of involvement and the JNCD was calculated for the clusters. An interpretable machine learning algorithm was used to relate the visual appraisal to the instrumental color. A JNCD of DE*ab = 6.2 was established, although it was lower for younger people. DE*ab was also influenced by the involvement of consumers. The machine-learning algorithm results were better than those obtained via multiple linear regressions when consumers' psychographic characteristics were included. The most important color variables of the algorithm were L* and hab. The findings of this research underscore the impact of consumers' involvement and familiarity with dry-cured ham on their perception of color.  \nKeywords: just-noticeable; difference; JND; JNCD; delta E; consumer; machine learning  \n1. Introduction  \nFor decades, dry-cured hams have been sold as the whole hind pig leg, but thereis an increasing trend of consuming packets of sliced ham. According to data published by the Spanish Government [1], sales dry-cured ham commercialized as packed sliced ham increased from 41.9% in 2008 to 52.5% in 2013 and to 61.2% in 2020 . In this purchase scenario, consumers have the chance to consider several product aspects, and color plays a major role in consumer decisions [2,3] . Many studies on meat and meat products have related the tristimulus coordinates of the CIELab color space to hue angle and chroma, orto other indicators like b*/a* or the 630 nm and 580 nm reflectance ratios to consumers'color perception, or even their purchase intention [4–7] . However, the aim of relating color perception to instrumental color is not trivial. First, the relation of trichromatic coordinates to color visual appraisal is not linear [4,5] . In addition, ","cbCaio24Hs9OeSxI","https://ap.wps.com/l/cbCaio24Hs9OeSxI","pdf",2315925,2,1,16,"English","en",105,"# Introduction\n## Color perception and instrumental color relationships\n## Just Noticeable Color Difference (JNCD)\n## Consumer factors affecting perception\n# Methods\n## Sample preparation and instrumental color measurement\n## Consumer surveys and clustering\n## Interpretable machine-learning approach\n# Results and discussion\n## JNCD estimation and effects of age and involvement\n## Key model variables for color characterization\n# Conclusions","[{\"question\":\"What does the study examine regarding color differences in dry-cured ham?\",\"answer\":\"It examines the relationship between visual color perception and instrumental color, focusing on the Just Noticeable Color Difference (JNCD) for ham samples.\"},{\"question\":\"How are consumer involvement and familiarity used in the analysis?\",\"answer\":\"Consumers report their involvement in food and familiarity with cured ham; consumers are then clustered by involvement level and JNCD is calculated across clusters.\"},{\"question\":\"Why is machine learning compared with multiple linear regression?\",\"answer\":\"The study uses an interpretable machine-learning algorithm to relate visual appraisal to instrumental color and compares its performance against multiple linear regressions that include consumer psychographic characteristics.\"}]","A Machine Learning Approach Investigating Consumers' Familiarity with and Involvement in the Just Noticeable Color Difference and Cured Color Characterization Scale - read online free | PDF",1786001706,40,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"a-machine-learning-approach-investigating-consumers-familiarity-with-and-involvement-in-the-just-noticeable-color-difference-and-cured-color-characterization-scale-read-online-free","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/a-machine-learning-approach-investigating-consumers-familiarity-with-and-involvement-in-the-just-noticeable-color-difference-and-cured-color-characterization-scale-read-online-free/128554/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What does the study examine regarding color differences in dry-cured ham?","Question",{"text":76,"@type":77},"It examines the relationship between visual color perception and instrumental color, focusing on the Just Noticeable Color Difference (JNCD) for ham samples.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are consumer involvement and familiarity used in the analysis?",{"text":81,"@type":77},"Consumers report their involvement in food and familiarity with cured ham; consumers are then clustered by involvement level and JNCD is calculated across clusters.",{"name":83,"@type":74,"acceptedAnswer":84},"Why is machine learning compared with multiple linear regression?",{"text":85,"@type":77},"The study uses an interpretable machine-learning algorithm to relate visual appraisal to instrumental color and compares its performance against multiple linear regressions that include consumer psychographic characteristics.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":30,"slug":119},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]