[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119349-en":3,"doc-seo-119349-105":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},119349,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Non-destructive analysis of Ganoderma lucidum composition using hyperspectral imaging and machine learning","Ganoderma lucidum quality is difficult to assess with conventional chemical tests because polysaccharide and ergosterol levels are influenced by cultivation and harvest conditions, while traditional methods can be destructive, complex, and costly. A fast, noninvasive workflow combines hyperspectral imaging with machine learning to predict polysaccharides and ergosterol in cap and powder. Visible-near infrared and short-wave infrared spectra are modeled using neural, extreme learning machine, and decision tree approaches, with extreme learning plus genetic optimization and voting achieving the strongest accuracy.","TYPE Original Research PUBLISHED 26 February 2025  \nDOI 10.3389/fchem.2025.1534216  \nOPEN ACCESS  \nEDITED BY  \nFederica Aureli,  \nNational Institute of Health (ISS), Italy  \nREVIEWED BY  \nSulaymon Eshkabilov,  \nNorth Dakota State University, United States Estefania Alfaro Mejia,  \nUniversity of Puerto Rico at Mayagüez, Puerto Rico  \nJinhuan Xu,  \nQilu University of Technology, China  \n*CORRESPONDENCE  \nWei Zhang,  \n [weizcaas@126.com](weizcaas@126.com)[ ](weizcaas@126.com)Xueyuan Bai,  \n [baixy1212@163.com](baixy1212@163.com)  \nRECEIVED 25 November 2024  \nACCEPTED 30 January 2025  \nPUBLISHED 26 February 2025  \nCITATION  \nRan J, Xu H, Wang Z, Zhang W and Bai X (2025) Non-destructive analysis of Ganoderma lucidum composition using hyperspectral imaging and machine learning.  \nFront. Chem. 13:1534216 .  \ndoi: 10.3389/fchem.2025.1534216  \nCOPYRIGHT  \n© 2025 Ran, Xu, Wang, Zhang and Bai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nNon-destructive analysis of Ganoderma lucidum composition using hyperspectral imaging and machine learning  \nJing Ran, Hui Xu, Zhilong Wang, Wei Zhang* and Xueyuan Bai*  \nNortheast Asia Institute of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, Jilin, China  \nBackground: Ganoderma lucidum is a widely used medicinal fungus whose quality is influenced by various factors, making traditional chemical detection methods complex and economically challenging. This study addresses the need for fast, noninvasive testing methods by combining hyperspectral imaging with machine learning to predict polysaccharide and ergosterol levels in Ganoderma lucidum cap and powder.  \nMethods: Hyperspectral images in the visible near-infrared (385–1009 nm) and short-wave infrared (899–1695 nm) ranges were collected, with ergosterol measured by high-performance liquid chromatography and polysaccharides assessed via the phenol-sulfuric acid method. Three machine learning models—a feedforward neural network, an extreme learning machine, and a decision tree—were tested.  \nResults: Notably, the extreme learning machine model, optimized by a genetic algorithm with voting, provided superior predictions, achieving R2 values of 0. 96 and 0 . 97 for polysaccharides and ergosterol, respectively.  \nConclusion: This integration of hyperspectral imaging and machine learning offers a novel, nondestructive approach to assessing Ganoderma lucidum quality.  \nKEYWORDS  \npolysaccharide, ergosterol, hyperspectral imaging, machine learning model, medicinal fungus  \n1 Introduction  \nGanoderma lucidum, a member of the Ganodermataceae family, is classified as a whiterot fungi (Wang T. T. et al., 2024) . This species primarily grows in tropical, subtropical, and temperate climates. As one of the leading producers, China has developed a large-scale Ganoderma lucidum planting industry. This fungus is rich in specific bioactive components, including polysaccharides, triterpenes, proteins, and sterols (Cör et al., 2022) . Ganoderma lucidum, known for its remarkable antioxidant, antibacterial, tumor-inhibiting, and antiinflammatory effects, holds a significant position in the healthcare and nutrition market. This is attributed to its three primary functions: nourishment, treatment, and tonification. As awareness of the quality of Ganoderma lucidum increases, it becomes evident that its quality is significantly affected by many factors, including producing area, cultivation environment, harvest conditions, and so on. Researchers have developed various quality control techniques for Ganoderma lucidum, including UV-Vis spectrophotomet","cbCaihNxyZtWBfFI","https://ap.wps.com/l/cbCaihNxyZtWBfFI","pdf",2733498,1,13,"English","en",105,"# Introduction\n## Background and quality-control challenges\n## Spectral technologies and hyperspectral imaging\n## Machine learning for non-destructive prediction\n# Methods\n## Hyperspectral data acquisition\n## Reference assays (HPLC and phenol-sulfuric acid)\n## Machine learning models\n# Results\n## Model performance and optimization\n# Conclusion\n## Non-destructive quality assessment framework","[{\"question\":\"Why are traditional chemical detection methods challenging for Ganoderma lucidum quality control?\",\"answer\":\"Quality is affected by many production factors, and traditional tests are often complex, time-consuming, destructive, and sensitive to experimental procedures.\"},{\"question\":\"Which hyperspectral wavelength ranges were used for prediction?\",\"answer\":\"Hyperspectral images were collected in the visible near-infrared range (385–1009 nm) and the short-wave infrared range (899–1695 nm).\"},{\"question\":\"How did the best-performing machine learning model improve prediction accuracy?\",\"answer\":\"The extreme learning machine model, optimized using a genetic algorithm with voting, delivered superior predictions with high R² values for both polysaccharides and ergosterol.\"}]","Non-destructive analysis of Ganoderma lucidum composition using hyperspectral imaging and machine learning | PDF",1785723846,33,{"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},"non-destructive-analysis-of-ganoderma-lucidum-composition-using-hyperspectral-imaging-and-machine-learning","",{"@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/non-destructive-analysis-of-ganoderma-lucidum-composition-using-hyperspectral-imaging-and-machine-learning/119349/",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","2026-08-03",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},"Why are traditional chemical detection methods challenging for Ganoderma lucidum quality control?","Question",{"text":75,"@type":76},"Quality is affected by many production factors, and traditional tests are often complex, time-consuming, destructive, and sensitive to experimental procedures.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which hyperspectral wavelength ranges were used for prediction?",{"text":80,"@type":76},"Hyperspectral images were collected in the visible near-infrared range (385–1009 nm) and the short-wave infrared range (899–1695 nm).",{"name":82,"@type":73,"acceptedAnswer":83},"How did the best-performing machine learning model improve prediction accuracy?",{"text":84,"@type":76},"The extreme learning machine model, optimized using a genetic algorithm with voting, delivered superior predictions with high R² values for both polysaccharides and ergosterol.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]