[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122956-en":3,"doc-seo-122956-105":29,"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},122956,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Performance evaluation of machine learning algorithms for meat freshness assessment - compare AI methods - KNN SVM Naïve Bayes","The study addresses the need for non-destructive, reliable prediction of beef meat freshness in the meat industry. Using a dataset of beef meat sample images, the work extracts color and texture features and applies three artificial-intelligence classifiers: support vector machines, k-nearest neighbor, and naïve Bayes. Classification accuracy is reported for each algorithm, yielding approximately 92.59% for KNN, 90.12% for SVM, and 87.65% for Naïve Bayes. Results indicate that KNN achieves the highest freshness classification performance against the other models.","Performance evaluation of machine learning algorithms for  \nmeat freshness assessment  \nAssia Arsalane1, Abdessamad Klilou2, Noureddine El Barbri3  \n1Mechatronics Department, Laboratory of Engineering and Applied Technologies, Higher School of Technology, Sultan Moulay  \nSlimane University, Beni Mellal, Morocco  \n2Microelectronic, Embedded Systems and Telecommunications (MiSET) Team, Faculty of Sciences and Technologies, Sultan Moulay  \nSlimane University, Beni Mellal, Morocco  \n3Laboratory of Science and Technology for the Engineer, LaSTI-ENSA, Sultan Moulay Slimane University, Khouribga, Morocco  \nArticle history:  \nReceived May 21, 2024 Revised Jun 16, 2024 Accepted Jul 2, 2024  \nKeywords:  \nBeef meat  \nMachine learning  \nK-nearest neighbor Support vector machines Naïve bayes  \nCorresponding Author:  \nIn meat industry, a non-destructive evaluation and prediction of meat quality attributes is highly required. Artificial vision technology is a powerful and widely used tool for meat quality evaluation because of reliability, reproducibility, non-invasiveness, and non-destructiveness. Machine learning methods are a fundamental and crucial part of artificial vision technology. Their choice is critical in determining successfully the quality of meat. The goal of this paper was to compare the performance of three artificial intelligence-based methods to evaluate the beef meat freshness. In this research, a dataset of beef meat samples images was used to extract the color and texture features. Different methods including the support vector machines (SVM), k-nearest neighbor (KNN), and naïve Bayes (NB) algorithms were applied to determine the freshness of samples. The accuracy rates of KNN, SVM and NB algorithms were obtained about 92.59%, 90.12% and 87.65%, respectively. The results show that the KNN provides the highest classification rates against SVM and NB algorithms.  \nThis is an open access article under the CC BY-SA license.  \nAssia Arsalane  \nMechatronics Department, Laboratory of Engineering and Applied Technologies, Higher School of Technology, Sultan Moulay Slimane University  \nBéni Mellal, Morocco  \nEmail: [arsalan.assia@gmail.com](arsalan.assia@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nMore than ever before, the assessment of the quality of meat has become a matter of great concern for both researchers and consumers. Traditional microbiological techniques are efficient but laborious, time consuming, demand qualified human, destructive and applicable only for off-line control [1] . On the contrary, non-destructive detection techniques such as e-eye, e-nose and e-tongue are a growing field based on physics, electronics, computer science and machine learning algorithms [2] . These techniques are rapid, repeatable, considered environmentally friendly because they eliminate the need for chemical reagents [3], cost-efficient and suitable for online assessment [4], [5] . They have been widely used to control food quality such as meat [6], fruits [7], oil [8], fish [9] vegetable [10], and milk products [11] .  \nArtificial vision technology has been widely applied to detect the quality of meat due to its low cost and high efficiency [2] . In brief, artificial vision technology is a structure that is able to offer a precise physical explanation of an object over image analysis [12] . The image captured by the physical sensor is processed and then classified using machine learning methods. Therefore, machine learning methods are fundamental and crucial [13] . Machine learning is a type of artificial intelligence that enables systems to  \nlearn from their experiences and progress without the need for explicit programming. It seeks to create algorithms that have access to data and can utilize it to educate themselves [10] . Machine learning includes data pre-processing, feature engineering, model selection, assessment, optimization methods, unsupervised, and supervised algorithms [13] . These algorithms are used for da","cbCaivUHFdbMuT4q","https://ap.wps.com/l/cbCaivUHFdbMuT4q","pdf",349852,1,"English","en",105,"# INTRODUCTION\n## Non-destructive meat quality assessment\n## Role of artificial vision and machine learning\n# RELATED WORK\n## Machine vision + classifiers for meat classification\n## Olfactory visualization and chemometrics\n## Virtual expert and CNN/SVM approach","[{\"question\":\"Why is non-destructive meat freshness assessment important?\",\"answer\":\"It supports reliable, reproducible evaluation without invasive or destructive procedures, enabling faster and online-friendly quality control.\"},{\"question\":\"Which features and inputs are used to evaluate beef freshness?\",\"answer\":\"Beef meat sample images are used to extract color and texture features, which are then fed to machine learning classifiers.\"},{\"question\":\"How do KNN, SVM, and Naïve Bayes perform for freshness classification?\",\"answer\":\"Reported accuracy is about 92.59% for KNN, 90.12% for SVM, and 87.65% for Naïve Bayes, with KNN providing the highest classification rates.\"}]","Performance evaluation of machine learning algorithms for meat freshness assessment - compare AI methods - KNN SVM Naïve Bayes | PDF",1785813884,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"performance-evaluation-of-machine-learning-algorithms-for-meat-freshness-assessment-compare-ai-methods-knn-svm-naive-bayes","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/performance-evaluation-of-machine-learning-algorithms-for-meat-freshness-assessment-compare-ai-methods-knn-svm-naive-bayes/122956/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is non-destructive meat freshness assessment important?","Question",{"text":74,"@type":75},"It supports reliable, reproducible evaluation without invasive or destructive procedures, enabling faster and online-friendly quality control.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which features and inputs are used to evaluate beef freshness?",{"text":79,"@type":75},"Beef meat sample images are used to extract color and texture features, which are then fed to machine learning classifiers.",{"name":81,"@type":72,"acceptedAnswer":82},"How do KNN, SVM, and Naïve Bayes perform for freshness classification?",{"text":83,"@type":75},"Reported accuracy is about 92.59% for KNN, 90.12% for SVM, and 87.65% for Naïve Bayes, with KNN providing the highest classification rates.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]