[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127908-en":3,"doc-seo-127908-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127908,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Prediction of the impact of tobacco waste hydrothermal products on compost microbial growth using hyperspectral imaging combined with machine learning","The study addresses limited understanding of how hydrothermal products affect compost microorganism growth, which hampers wider adoption of hydrothermal-coupled composting for tobacco waste. Tobacco straw samples underwent hydrothermal treatment and were analyzed using hyperspectral imaging with machine learning. Regions of interest were used to extract spectra, and multivariate analyses assessed changes in key variables tied to microbial growth. The results classify impacts as promoting, inhibiting, or neutral, identify influential wavelengths (900–1700 nm), and show Random Forest achieves the best predictive accuracy (Rc≈0.957, RMSE≈3.584), enabling faster, more reliable compositional and bioactivity prediction.","TYPE Original Research PUBLISHED 05 November 2024 DOI 10.3389/fmicb.2024.1476803  \nOPEN ACCESS  \nEDITED BY  \nWenjie Ren,  \nChinese Academy of Sciences, China  \nREVIEWED BY  \nWen-Ming Xie,  \nNanjing Normal University, China Agustami Sitorus,  \nNational Research and Innovation Agency (BRIN), Indonesia  \n*CORRESPONDENCE  \nGuotao Yang  \n [lemonmassa2@163.com](lemonmassa2@163.com)  \nRECEIVED 06 August 2024  \nACCEPTED 21 October 2024  \nPUBLISHED 05 November 2024  \nCITATION  \nLiu D, Ma X, Ye C, Jin Y, Huang K, Niu C, Zhang G, Li D, Ma L, Li S and Yang G (2024) Prediction of the impact of tobacco waste hydrothermal products on compost microbial growth using hyperspectral imaging combined with machine learning.  \nFront. Microbiol. 15:1476803 .  \ndoi: 10.3389/fmicb.2024.1476803  \nCOPYRIGHT  \n© 2024 Liu, Ma, Ye, Jin, Huang, Niu, Zhang, Li, Ma, Li and Yang. This is an open-access article distributed under the terms of the  \nCreative 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.  \nPrediction of the impact of tobacco waste hydrothermal products on compost microbial growth using hyperspectral imaging combined with machine learning  \nDandan Liu 1, Xinxin Ma 2, Changwen Ye 1, Yiying Jin 2, Kuo Huang 1, Chenqi Niu 1, Ge Zhang 1, Dong Li 1, Linzhi Ma3, Suxiao Li3 and Guotao Yang 1*  \n1China Tobacco Standardization Research Center, Zhengzhou Tobacco Research Institute, Zhengzhou, China, 2School of Environment, Tsinghua University, Beijing, China, 3College of Physical Engineering, Zhengzhou University, Zhengzhou, China  \nThe insufficient understanding of the impact of hydrothermal products on the growth characteristics of compost microorganisms presents a significant challenge to the broader implementation of hydrothermal coupled composting for tobacco waste. Traditional biochemical detection methods are labor-intensive and timeconsuming, highlighting the need for faster and more accurate alternatives. This study investigated the effects of hydrothermal treatment on tobacco straw products and their influence on compost microorganism growth, using hyperspectral imaging (HSI) technology and machine learning algorithms. Sixtyone tobacco straw samples were analyzed with a hyperspectral camera, and image processing was used to extract average spectra from regions of interest (ROI) . Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were applied to assess four key variables: nicotine content, total humic acid content, Penicillium chrysogenum H/C ratio, and Bacillus subtilis OD600 ratio. The effects of hydrothermal treatment on compost were classified as promoting, inhibiting, or neutral regarding microbial growth. The Competitive Adaptive Reweighted Sampling (CARS) method identified the most influential wavelengths in the 900-1700 nm spectral range. The Random Forest (RF) model outperformed SVM, KNN, and XGBoost models in predicting microbial growth responses, achieving Rc =0 . 957, RMSE=3.584. Key wavelengths were identified at 1096 nm, 1101 nm, 1163 nm, 1335 nm, and 1421 nm. The results indicate that hyperspectral imaging combined with machine learning can accurately predict changes in the chemical composition of tobacco straws and their effects on microbial activity. This method provides an innovative and effective means of improving the resource usage of tobacco straws in composting, enhancing sustainable waste management procedures.  \nKEYWORDS  \ncomposting, hydrothermal, hyperspectral imaging, machine learning, tobacco waste  \nFrontiers in Microbiology 01 [frontiersin.org](frontiersin.org)  \nHighlights  \n• HCA and PCA sorted hydrothermal treatment impact on composting into three types.  \n• Four machin","cbCaioXe03uKGHSh","https://ap.wps.com/l/cbCaioXe03uKGHSh","pdf",1599513,2,1,13,"English","en",105,"# Highlights\n## Main findings\n# Introduction\n## Tobacco waste context and need\n## Hydrothermal coupling composting and challenges\n## Traditional assessment methods","[{\"question\":\"What problem does the study target regarding hydrothermal-coupled composting for tobacco waste?\",\"answer\":\"It targets the insufficient understanding of how hydrothermal products influence compost microorganism growth, which makes implementation difficult and highlights the need for faster, more accurate assessment methods.\"},{\"question\":\"How were hyperspectral imaging and machine learning used in the study?\",\"answer\":\"Hyperspectral imaging captured spectra from tobacco straw samples, regions of interest were processed to extract average spectra, and machine learning models used these spectral features to predict compost microbial growth responses.\"},{\"question\":\"Which model performed best, and what wavelengths were identified as influential?\",\"answer\":\"The Random Forest model outperformed SVM, KNN, and XGBoost, with Rc≈0.957 and RMSE≈3.584. Influential wavelengths were identified in the 900–1700 nm range, including 1096, 1101, 1163, 1335, and 1421 nm.\"}]","Prediction of the impact of tobacco waste hydrothermal products on compost microbial growth using hyperspectral imaging combined with machine learning | PDF",1785942857,33,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"prediction-of-the-impact-of-tobacco-waste-hydrothermal-products-on-compost-microbial-growth-using-hyperspectral-imaging-combined-with-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/prediction-of-the-impact-of-tobacco-waste-hydrothermal-products-on-compost-microbial-growth-using-hyperspectral-imaging-combined-with-machine-learning/127908/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-28","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the study target regarding hydrothermal-coupled composting for tobacco waste?","Question",{"text":76,"@type":77},"It targets the insufficient understanding of how hydrothermal products influence compost microorganism growth, which makes implementation difficult and highlights the need for faster, more accurate assessment methods.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were hyperspectral imaging and machine learning used in the study?",{"text":81,"@type":77},"Hyperspectral imaging captured spectra from tobacco straw samples, regions of interest were processed to extract average spectra, and machine learning models used these spectral features to predict compost microbial growth responses.",{"name":83,"@type":74,"acceptedAnswer":84},"Which model performed best, and what wavelengths were identified as influential?",{"text":85,"@type":77},"The Random Forest model outperformed SVM, KNN, and XGBoost, with Rc≈0.957 and RMSE≈3.584. 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