[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-128838-105":59,"doc-detail-128838-en":117},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":110,"head_meta":112,"extra_data":114,"updated_unix":116},105,"en","smartphone-based-digital-image-analysis-for-qualitative-classification-of-food-dyes-using-machine-learning-effects-of-color-space-and-lighting-conditions","Smartphone-Based Digital Image Analysis for Qualitative Classification of Food Dyes Using Machine Learning - Effects of Color Space and Lighting Conditions","","Smartphone-based digital image analysis (DIA) provides an affordable route for chemical colorimetry, yet prior work has largely emphasized quantitative measurements. This study develops a machine learning–assisted approach for qualitative identification of nine synthetic food dyes. Smartphone images were acquired under closed and open lighting, converted into RGB, normalized RGB (rgb), HSL, and CIELAB features, and classified using a KNN model. The method achieved at least 86% accuracy across color spaces and lighting conditions, and highest commercial product performance occurred with HSL, benefiting from open illumination, supporting low-cost, portable qualitative colorimetric analysis.",{"@graph":69,"@context":109},[70,84,100],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/smartphone-based-digital-image-analysis-for-qualitative-classification-of-food-dyes-using-machine-learning-effects-of-color-space-and-lighting-conditions/128838/",{"url":83,"name":65,"@type":85,"author":86,"headline":65,"publisher":89,"fileFormat":92,"inLanguage":63,"description":67,"dateModified":93,"datePublished":94,"encodingFormat":92,"isAccessibleForFree":95,"interactionStatistic":96},"DigitalDocument",{"name":87,"@type":88},"Aria","Person",{"url":74,"name":90,"@type":91},"DocShare","Organization","application/pdf","2026-09-01","2026-08-06",true,{"@type":97,"interactionType":98,"userInteractionCount":81},"InteractionCounter",{"@type":99},"ViewAction",{"@type":101,"mainEntity":102},"FAQPage",[103],{"name":104,"@type":105,"acceptedAnswer":106},"Which color space and lighting condition performed best for commercial samples?","Question",{"text":107,"@type":108},"HSL produced the highest classification accuracy on commercial products, and the open lighting setup consistently yielded better performance than closed lighting.","Answer","https://schema.org",{"og:url":83,"og:type":111,"og:title":65,"og:site_name":90,"og:description":67},"article",{"robots":113,"canonical":83},"index,follow",{"doc_id":115,"site_id":62},128838,1786003808,{"code":4,"msg":5,"data":118},{"doc_id":115,"user_id":119,"nickname":87,"user_avatar":120,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":121,"file_id":122,"file_url":123,"file_type":124,"file_size":125,"view_count":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":126,"language":127,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":128,"faqs":129,"seo_title":130,"seo_description":67,"update_tm":116,"read_time":131},2336474459895,"https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916","SMARTPHONE-BASED DIGITAL IMAGE ANALYSIS FOR QUALITATIVE CLASSIFICATION OF FOOD DYES USING MACHINE LEARNING: EFFECTS OF COLOR SPACE AND  \nLIGHTING CONDITIONS  \n\n| Yozef Tjandra1, Giovania Evangeline Halim2, Jansen Briano2, and Martin\u003Cbr>Tjahjono2*\u003Cbr>1 Calvin Institute of Technology, Department of IT and Big Data Analytics, Jakarta, Indonesia\u003Cbr>2 Calvin Institute of Technology, Department of Chemical and Food Processing, Jakarta, Indonesia |  |\n| --- | --- |\n| ARTICLE INFO\u003Cbr>\u003Cbr>ABSTRACT |  |\n| Keywords:\u003Cbr>Colorimetry;\u003Cbr>Color Space;\u003Cbr>Machine Learning; Qualitative Analysis Food dyes;\u003Cbr>Smartphone.\u003Cbr>Article History:\u003Cbr>Received: 2025-08-02\u003Cbr>Accepted: 2025-08-23\u003Cbr>Published: 2025-08-31\u003Cbr>doi:10.20961/jkpk.v10i2.107520\u003Cbr>\u003Cbr>©2025 The Authors. This openaccess article is distributed under a (CC-BY-SA License) | Smartphone-based digital image analysis (DIA) has emerged as an affordable and accessible method for chemical analysis, particularly in colorimetry. While most existing studies have focused on quantitative applications, this study explores a machine learning–assisted DIA approach for the qualitative classification of synthetic food dyes. Digital images of nine food dyes solutions (Carmoisine, Sunset Yellow, Allura Red, Ponceau 4R, Tartrazine, Fast Green FCF, Brilliant Blue FCF, Quinoline Yellow WS, and Indigo Carmine), were captured under both controlled (closed) and open lighting conditions using a smartphone camera. The images were subsequently processed to extract color values in different color spaces, namely RGB, normalized RGB (rgb), HSL, and CIELAB. These values served as input features for a k-nearest neighbors (KNN) classifier trained to identify the dye present in each solution. The KNN model performed well on model solutions, with at least 86% accuracy across all color spaces and lighting conditions. To assess practical applicability, the classifier was also tested on seven commercial food and health products. The results show that HSL color space yielded the highest classification accuracy in the commercial sample testing, across both lighting setups, with the open condition consistently producing better performance. These findings demonstrate the potential use of smartphone-based DIA combined with machine learning for low-cost, portable, and reliable solutions for qualitative colorimetric analysis. |\n| *Corresponding Author: [martin.tjahjono@calvin.ac.id](martin.tjahjono@calvin.ac.id)\u003Cbr>How to cite: Y. Tjandra, G. E. Halim, J. Briano, and M. Tjahjono , \"Smartphone-Based Digital Image Analysis for Qualitative Classification of Food Dyes Using Machine Learning: Effects of Color Space and Lighting Conditions ,” Jurnal Kimia dan Pendidikan Kimia (JKPK) , vol. 10 , no. 2 , pp. 309–322 , 2025.[Online] . Available: [https://doi.org/ 10.20961/jkpk.v10i2.107520](https://doi.org/ 10.20961/jkpk.v10i2.107520) |  |\n\nINTRODUCTION  \nColorimetry is an analytical technique commonly used for the qualitative and quantitative determination of substances based on color information [1] . Standard colorimetric practices rely on specialized instruments such as spectrophotometers or colorimeters for analysis [2] . However, these  \nspecialized instruments are often costly, nonportable, and often require specially trained personnel to operate. As a result, recent trends have shifted in favor of more affordable and accessible analytical methods, with smartphone-based digital image analysis (DIA) gaining significant traction [3-6] .  \nSmartphone-based DIA typically involves capturing images of a colored substance using a smartphone camera, extracting the relevant color values from the images, then analyzing them to determine the substrate concentrations [6] . Modern smartphones are equipped with high resolution cameras and advanced computational abilities, which allows their use for field or onsite chemical analysis [7-9] . Applications of smartphone-based DIA in colorimetry include food spoilage monitoring [8 , 9], heavy met","cbCainfmgW4XfEV7","https://ap.wps.com/l/cbCainfmgW4XfEV7","pdf",622030,14,"English","# Abstract\n# Introduction\n## Background: colorimetry and the limitations of conventional instruments\n## Shift toward smartphone-based digital image analysis\n## Gap: qualitative testing needs\n## Challenges in traditional colorimetric classification\n## Role of machine learning for robust dye identification","[{\"question\":\"Which color space and lighting condition performed best for commercial samples?\",\"answer\":\"HSL produced the highest classification accuracy on commercial products, and the open lighting setup consistently yielded better performance than closed lighting.\"}]","Smartphone-Based Digital Image Analysis for Qualitative Classification of Food Dyes Using Machine Learning - Effects of Color Space and Lighting Conditions | PDF",35]