[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118912-en":3,"doc-seo-118912-105":30,"detail-sidebar-cat-0-en-105":90},{"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},118912,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Spectroscopy and Machine Learning in Food Processing Survey","Food safety and quality control require simple, non-destructive sensing from production through tasting, while remaining suitable for continuous industrial monitoring. Optical and spectroscopic measurements have become central approaches for non-destructive analysis, yet complex spectra often demand chemometrics and validated prediction or classification models. This survey compiles and evaluates recent bibliographic studies, focusing on food processing problems, spectroscopy types, machine learning models, and keyword trends to outline the research perspective.","Spectroscopy and machine learning in food processing survey  \nMahtem Mengstu 1,2 *, Alper Taner1, and Hüseyin Duran 1  \n1Ondokuz Mayis University, Faculty of Agriculture, Department of Agricultural Machinery and Technologies Engineering, Samsun, Turkiye  \n2Hamlemlamo Agricultural College, Department of Agricultural Engineering, Keren, Eritrea  \nAbstract. For food safety, quality control from the foodstuff production to the tasting of foods is needed and should be simple and non-destructive. Recent and notable non-destructive measurements of food and agricultural products are based on optical and spectroscopic techniques. Spectroscopy, meets the requirements of industrial applications for continuous quality control and process monitoring. Hence, this article covers a survey of recent research works, highlighting the application of spectroscopy and machine learning in food processing from bibliographic database. The survey was based on relevant articles, obtained from scientific database and evaluated selected research works based on survey inquires, the assessment included food processing problem addressed (varieties classification, origin identification, adulteration and quality control), types of spectroscopy used, machine learning models applied to solve the particular problem  \nand keyword analysis to show the perspective of the research.  \n1 Introduction  \nFrom health and safety perspective, consumer’s demand of quality and safe food is ever increasing. At the present time, maintaining food quality and safety assurance are principal subjects of food and beverage industries globally. Specialists, producers and researchers in this area have great responsibility that the food they process is safe and will not be a potential danger to the health of consumers [1] . For food safety, quality sensing from the foodstuff production to the tasting of foods is needed and should be simple, non-destructive, simultaneous, rapid, qualitative, and quantitative. In addition, the usefulness of the measured data for software evaluations is also required [2] .  \nSpectroscopy, meets the requirements of industrial applications for continuous quality control and process monitoring. Furthermore, most of the recent remarkable nondestructive measurements of food and agricultural products are based on optical and spectroscopic techniques However, as being based on indirect measurements, yielding highly complex and broad spectra, almost impossible to interpret with the unaided eye, NIR spectroscopy requires calibration with mathematical and statistical tools (chemometrics) to extract analytical information from the corresponding spectra [3] . Spectroscopy is the study of how light interacts with physical bodies (Fig. 1) . In spectroscopy applications, light is irradiated into a sample using different mechanisms [4] . When the outcome of the interaction of the light and the sample (such as fruits and grains) is recorded, the chemical information of the particular sample is therefore collected.  \n* [Corresponding author: ](Corresponding author: mahtem23@gmail.com)[mahtem23@gmail.com](Corresponding author: mahtem23@gmail.com)  \nFig. 1. Basic principle of spectroscopy.  \nSpectroscopy standardizations correlate spectral data with chemical or physical information of the sample under investigation [5]. The information obtained can ultimately be processed and by using validated prediction or classification models, tasks such as content quantification, identification of certain foreign materials which is commonly known as adulteration, origin identification, variety classification and other problems can be solves effectively. Thus, the aim of this work is it to make a survey on the application of spectroscopy and machine learning in solving food processing problems. The survey is based on relevant articles, obtained from scientific database and to evaluate selected research works based on survey inquires to extract the expected information that  \n© The Authors","cbCaiazkhVKLdGCn","https://ap.wps.com/l/cbCaiazkhVKLdGCn","pdf",2244477,1,4,"English","en",105,"# Introduction\n# Material and method\n## Search strategy and survey inquiries\n# Results and discussion\n## Area of study","[{\"question\":\"Why are non-destructive sensing methods important in food processing?\",\"answer\":\"Food safety and quality assurance require measurements that are simple, non-destructive, rapid, and suitable for both qualitative and quantitative evaluation from production to tasting.\"},{\"question\":\"How does spectroscopy support industrial quality control?\",\"answer\":\"Spectroscopy enables continuous quality control and process monitoring, but because it relies on indirect measurements, extracting analytical information typically requires calibration using mathematical and statistical tools (chemometrics).\"},{\"question\":\"What kinds of problems does the survey focus on for spectroscopy and machine learning?\",\"answer\":\"The survey covers food processing and quality evaluation tasks such as variety classification, origin identification, adulteration detection, and quality control, linking them with spectroscopy types and machine learning models.\"}]","Spectroscopy and Machine Learning in Food Processing Survey | 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are non-destructive sensing methods important in food processing?","Question",{"text":74,"@type":75},"Food safety and quality assurance require measurements that are simple, non-destructive, rapid, and suitable for both qualitative and quantitative evaluation from production to tasting.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does spectroscopy support industrial quality control?",{"text":79,"@type":75},"Spectroscopy enables continuous quality control and process monitoring, but because it relies on indirect measurements, extracting analytical information typically requires calibration using mathematical and statistical tools (chemometrics).",{"name":81,"@type":72,"acceptedAnswer":82},"What kinds of problems does the survey focus on for spectroscopy and machine learning?",{"text":83,"@type":75},"The survey covers food processing and quality evaluation tasks such as variety classification, origin identification, adulteration detection, and quality control, linking them 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