[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124924-en":3,"doc-seo-124924-105":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},124924,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","A Supervised Machine Learning Classification Framework for Beverage Quality Prediction","Energy-intensive food and beverage production makes product quality critical to consumers and industry stakeholders. This thesis addresses assessment challenges faced by food and beverage enterprises by proposing a supervised machine learning approach that leverages multiple beverage features to predict beverage quality. The review compares key techniques and discusses technology integration, including deep learning involvement. Results show accurate quality prediction based on chemical composition and enable identification of critical chemical parameters that guide targeted formulation and production adjustments.","A Supervised Machine Learning Classification Framework for  \nBeverage Quality Prediction  \n1,Jules MUHAYIMANA,2Dr Leopord Hakizimana  \n1Candidate in MIT, Graduate school, University of Kigali  \n2Lecturer, Graduate School, University of Kigali  \n* E-mail of the [corresponding author: muhayejules@gmail.com](corresponding author: muhayejules@gmail.com)  \nAbstract – Since the production of food and beverages is energy-intensive, the quality of food and beverage is important for the consumers as well as the food and beverage industry, the economic, political and social condition are posing challenge to Food and beverage small and medium and large industries assessment is an evaluation method used to measure the strengths and weaknesses of a food and beverage system to make improvements. With the start-up business success help of a machine learning model and several features of beverages, this thesis would focus on important features that affect the quality of beverage production and have a model to predict a beverage quality. This review would also compare and discuss each technique and provide suggestions based on the current technology. This review would deliberate technology integration and the involvement of deep learning to enable several types of current technologies and the results demonstrate the model's ability to accurately predict beverage quality based on chemical composition. Furthermore, the developed model allows for the identification of critical chemical parameters influencing beverage quality. Manufacturers can use this information to make targeted adjustments in the formulation and production process, leading to enhanced product quality and consistency.  \nKeywords – Machine Learning, Classification, Beverage and Prediction.  \nI. INTRODUCTION  \nIn this section introduction of the study, the background of the study, the problem statement, the objectives ofthe study, the scope and limitation of the research, and the significance of the study are all outlined.  \n1.1. Introduction  \nOver the four decades we’ve been in business, we’ve seen beverage change and evolve with consumer demands and beverage trends. The beverage industries include all industries involved in processing raw beverage materials, similarly to those who package and distribute them. This includes fresh, prepared beverages as well as packaged beverages (Coles, 2011) .  \nMachine learning (ML) refers to an application of AI that provides the system which is able to automatically learn and enhance through practical knowledge instead of relying on direct programming. This is too simple because a large amount of data today is available which makes it easier to machines to be trained rather than programmed. It is deemed a significant technological breakthrough capable of scrutinizing vast volumes of data. ML is rapidly transforming the world by changing all segments including healthcare services, transport, food, education, and different assembly line and many more (Singh, 2020) . Machine learning is a swiftly expanding domain with limitless potential applications. Over the upcoming years, we anticipate witnessing machine learning revolutionize numerous sectors, such as manufacturing, retail, and healthcare. In manufacturing, machine learning can be used for quality control, automation and customization (Ambadipudi, 2023)  \nThe food and beverages manufacturing and processing create significant hazards related to the risk of fire and exposure to toxic gases. Therefore, the development of food and beverage assessments is essential and should be a priority to ensure food and beverage safety and public health. Numerous methodological processes and technologies have been created for the evaluation of food and beverages, encompassing imaging, odor, taste, electromagnetic sensing, and various other approaches. (Alabi, 2020) .  \nAccording to Georgia Pratt, the beverage industry is sub-divided into two segments, those are the production and the distribution of the f","cbCaicyPIkiTMNRO","https://ap.wps.com/l/cbCaicyPIkiTMNRO","pdf",2480920,1,32,"English","en",105,"# Introduction\n## Background\n## Problem Statement\n# Machine Learning and Beverage Quality Context\n# Standards and Regulatory Requirements","[{\"question\":\"What problem does the supervised machine learning framework aim to solve in beverage quality assessment?\",\"answer\":\"It targets the difficulty beverage companies face in ensuring consistent product quality and safety by using beverage features to predict quality reliably.\"},{\"question\":\"How does the proposed approach predict beverage quality?\",\"answer\":\"It uses supervised machine learning classification models trained on multiple beverage features, with reported performance based on chemical composition.\"},{\"question\":\"What practical outcome does the model provide for manufacturers?\",\"answer\":\"It helps identify critical chemical parameters, enabling targeted adjustments in formulation and the production process to improve quality and consistency.\"}]","A Supervised Machine Learning Classification Framework for Beverage Quality Prediction | 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