[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122469-en":3,"doc-seo-122469-105":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},122469,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Application of machine learning and deep learning to detect adulteration in food flour based on spectroscopy data - a systematic review","Food quality and safety are critical in the food industry, yet adulteration and counterfeiting in powdered products like flour persist, creating health and economic risks. This systematic literature review analyzes research trends combining spectroscopy with machine learning and deep learning to detect flour adulterants quickly and accurately. From 105 screened records across Scopus, Web of Science, and PubMed, 32 articles were selected. Findings highlight a rising publication trend since 2019, with dominant Asian contributions, and report high performance using NIR/Vis-NIR/Raman spectroscopy alongside PLSR, CNN, and SVM.","Application of machine learning and deep learning to detect adulteration in food flour based on spectroscopy data: a systematic review  \nNadya Hafidzatun Nisa1, Rudiati Evi Masithoh1*, Muhammad Fahri Reza Pahlawan2, and Andra Tersiana Wati1,3 1Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia  \n2Department of Smart Agriculture Systems, College of Agricultural and Life Science, Chungnam National University, Daejeon, 34134, Republic of Korea  \n3Department of Agricultural Product Technology, Faculty of Halal Industry, Universitas Nahdlatul Ulama Yogyakarta, Indonesia  \nAbstract. Food quality and safety are very important aspects in the food industry, but counterfeiting often occurs, especially in powdered products. The development of non-destructive technology based on spectroscopy, combined with machine learning and deep learning algorithms, is increasingly being applied to quickly and accurately detect adulterants. This study aims to identify, analyze, and review research trends related to the detection of adulterated powdered food products by combining spectroscopy technology and machine learning or deep learning methods through a systematic literature review (SLR) approach. The study identified 32 out of 105 articles selected from Scopus, Web of Science, and PubMed. The research trend shows a significant increase since 2019, dominated by the Asian region.  \nCommonly used spectroscopy technologies include NIR, Vis-NIR, and Raman, combined with algorithms such as PLSR, CNN, and SVM to improve detection accuracy beyond 90% and achieve high R-squared (R²) values. Data pre-processing techniques, such as filtering, have also proven effective in improving analysis results. The development of intelligent detection systems using machine learning models, data augmentation techniques, and transfer learning, along with multidisciplinary collaboration between food science, computer science, and instrumentation fields, will strengthen future research.  \nKeywords: machine learning, spectroscopy, non-destructive, adulteration detection, powdered food  \n1 Introduction  \nFood quality and safety are two important aspects in the food industry, as both are directly related to consumer health. The addition of unauthorized or hazardous substances to food products, especially powdered products such as flour, is a common practice, often with the aim of obtaining economic benefits. One common form of ingredient addition is the use of synthetic dyes, which are cheap and readily available but can pose significant health risks [1-2] . In addition to synthetic dyes, natural ingredients such as starch, which are more economical, are often used to increase volume and reduce raw material costs [3-4] . This can be detrimental to consumers both economically and health-wise. Furthermore, authenticating powdered food products against counterfeiting remains a challenge because it still relies on destructive methods that require high investment costs, sample preparation, and the use of several chemicals.  \nNon-destructive methods are one of the main solutions for detecting counterfeit agricultural and food products. In addition to being fast, non-destructive, and producing minimal waste, this method is also highly accurate. The use of spectroscopy tools on food ingredients, especially powdered products, has been widely adopted, such as Visible-Near Infrared (VisNIR) Spectroscopy in detecting adulteration of cocoa powder [5], Near-Infrared (NIR) and Mid-Infrared (MIR) spectroscopy in detecting adulteration of powdered milk [6], and FT-IR spectroscopy in detecting adulteration of coconut sugar [7] . These techniques offera fast, reliable, and non-destructive approach to detecting counterfeit products in agricultural and food products.  \nRecently, spectroscopy has been developed by integrating machine learning (ML) and deep learning (DL) algorithms to detect food counterfeiting. M","cbCaiome9uqJL8iL","https://ap.wps.com/l/cbCaiome9uqJL8iL","pdf",1968542,1,12,"English","en",105,"# Introduction\n## Non-destructive spectroscopy for counterfeit detection\n## Integrating ML and DL with spectroscopy\n# Systematic review focus\n## Selected studies and trends\n## Spectroscopy technologies and algorithms","[{\"question\":\"What is the document’s main goal?\",\"answer\":\"To identify, analyze, and review research trends on detecting adulterated powdered food products by combining spectroscopy with machine learning or deep learning using a systematic literature review approach.\"},{\"question\":\"Which spectroscopy technologies and models are commonly used?\",\"answer\":\"NIR, Vis-NIR, and Raman are commonly paired with algorithms such as PLSR, CNN, and SVM to improve detection accuracy and provide strong statistical performance.\"},{\"question\":\"How many studies were selected and what trend does the review report?\",\"answer\":\"32 out of 105 articles were selected from Scopus, Web of Science, and PubMed. Publications show a significant increase since 2019, dominated by research from Asia.\"}]","Application of machine learning and deep learning to detect adulteration in food flour based on spectroscopy data - a systematic review | PDF",1785810816,30,{"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},"application-of-machine-learning-and-deep-learning-to-detect-adulteration-in-food-flour-based-on-spectroscopy-data-a-systematic-review","",{"@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/application-of-machine-learning-and-deep-learning-to-detect-adulteration-in-food-flour-based-on-spectroscopy-data-a-systematic-review/122469/",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","2026-08-04",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 is the document’s main goal?","Question",{"text":75,"@type":76},"To identify, analyze, and review research trends on detecting adulterated powdered food products by combining spectroscopy with machine learning or deep learning using a systematic literature review approach.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which spectroscopy technologies and models are commonly used?",{"text":80,"@type":76},"NIR, Vis-NIR, and Raman are commonly paired with algorithms such as PLSR, CNN, and SVM to improve detection accuracy and provide strong statistical performance.",{"name":82,"@type":73,"acceptedAnswer":83},"How many studies were selected and what trend does the review report?",{"text":84,"@type":76},"32 out of 105 articles were selected from Scopus, Web of Science, and PubMed. 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