[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127701-en":3,"doc-seo-127701-105":30,"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":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},127701,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Rapid assessment of the blood-feeding histories of wild-caught malaria mosquitoes using mid-infrared spectroscopy and machine learning","The study addresses the human blood index (HBI) as a key parameter for evaluating malaria transmission risk and notes that current mosquito-blood-meal identification methods are labor-intensive, costly, and error-prone. It presents the first field application of mid-infrared spectroscopy combined with machine learning (MIRS-ML), directly benchmarked against PCR. ATR-FTIR scanned Anopheles funestus blood meals confirmed by PCR enable logistic regression and multi-layer perceptron classification with 88%–90% accuracy and closely aligned HBI estimates.","Mwanga etal. Malaria Journal (2024) 23:86 [https://doi.org/10.1186/s12936-024-04915-0](https://doi.org/10.1186/s12936-024-04915-0)  \nMalaria Journal  \n RESEARCH Open Access  \nRapid assessment of the blood-feeding   histories of wild-caught malaria mosquitoes using mid-infrared spectroscopy and machine learning  \nEmmanuel P. Mwanga 1,2*, Idrisa S. Mchola 1, Faraja E. Makala 1, Issa H. Mshani 1,2, Doreen J. Siria 1,2,  \nSophia H. Mwinyi 1,2, Said Abbasi1, Godian Seleman 1, Jacqueline N. Mgaya 1, Mario González Jiménez3, Klaas Wynne3, Maggy T. Sikulu‑Lord4, Prashanth Selvaraj5, Fredros O. Okumu 1,2,6,7†, Francesco Baldini2† and Simon A. Babayan2†  \nAbstract  \nBackground The degree to which Anopheles mosquitoes prefer biting humans over other vertebrate hosts, i. e. the human blood index (HBI), is a crucial parameter for assessing malaria transmission risk. However, existing techniques for identifying mosquito blood meals are demanding in terms of time and effort, involve costly reagents, and are prone to inaccuracies due to factors such as cross‑reactivity with other antigens or partially digested  \nblood meals in the mosquito gut. This study demonstrates the first field application of mid‑infrared spectroscopy and machine learning (MIRS‑ML), to rapidly assess the blood‑feeding histories of malaria vectors, with direct comparison to PCR assays.  \nMethods and results Female Anopheles funestus mosquitoes (N = 1854) were collected from rural Tanzania and desiccated then scanned with an attenuated total reflectance Fourier‑transform Infrared (ATR‑FTIR) spectrometer. Blood meals were confirmed by PCR, establishing the ‘ground truth’ for machine learning algorithms. Logistic regression and multi‑layer perceptron classifiers were employed to identify blood meal sources, achieving accuracies of 88%–90%, respectively, as well as HBI estimates aligning well with the PCR‑based standard HBI.  \nConclusions This research provides evidence of MIRS‑ML effectiveness in classifying blood meals in wild Anopheles funestus, as a potential complementary surveillance tool in settings where conventional molecular techniques are impractical. The cost‑effectiveness, simplicity, and scalability of MIRS‑ML, along with its generalizability, outweigh minor gaps in HBI estimation. Since this approach has already been demonstrated for measuring other entomological and parasitological indicators of malaria, the validation in this study broadens its range of use cases, positioning itas an integrated system for estimating pathogen transmission risk and evaluating the impact of interventions. Keywords Anopheles, Human blood index machine learning, Transfer learning, VectorSphere  \n†Fredros O. Okumu, Francesco Baldini, and Simon A. Babayan co‑supervised this work equally.  \n*Correspondence: Emmanuel P. Mwanga[emwanga@ihi.or.tz](emwanga@ihi.or.tz)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ()[. The Creative Commons Public Domain Dedication waiver (](http://creativecommons.org/licens","cbCairBZA7G2ETzm","https://ap.wps.com/l/cbCairBZA7G2ETzm","pdf",1593917,1,11,"English","en",105,"# Background\n## Need for rapid entomological surveillance\n## Role of human blood index (HBI) in transmission risk\n# Methods and results\n## Field collection and ATR-FTIR scanning\n## PCR confirmation and ground truth\n## Classifiers and performance metrics\n# Conclusions\n## MIRS-ML as a complementary surveillance tool","[{\"question\":\"Why is the human blood index (HBI) important in malaria surveillance?\",\"answer\":\"HBI reflects how strongly Anopheles mosquitoes prefer biting humans versus other vertebrate hosts, directly supporting assessment of malaria transmission risk and guiding vector control planning.\"},{\"question\":\"How does the study rapidly identify blood meals in wild-caught mosquitoes?\",\"answer\":\"Female Anopheles funestus mosquitoes are scanned using ATR-FTIR mid-infrared spectroscopy, and machine learning classifiers predict blood-meal sources. PCR confirmation provides ground truth for training and evaluation.\"},{\"question\":\"What performance did the machine learning models achieve and how did HBI estimates compare to PCR?\",\"answer\":\"Logistic regression and multi-layer perceptron models achieved about 88%–90% accuracy. Estimated HBI values aligned well with the PCR-based standard HBI.\"}]","Rapid assessment of the blood-feeding histories of wild-caught malaria mosquitoes using mid-infrared spectroscopy and machine learning | PDF",1785940997,28,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"rapid-assessment-of-the-blood-feeding-histories-of-wild-caught-malaria-mosquitoes-using-mid-infrared-spectroscopy-and-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@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/rapid-assessment-of-the-blood-feeding-histories-of-wild-caught-malaria-mosquitoes-using-mid-infrared-spectroscopy-and-machine-learning/127701/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","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},"Why is the human blood index (HBI) important in malaria surveillance?","Question",{"text":76,"@type":77},"HBI reflects how strongly Anopheles mosquitoes prefer biting humans versus other vertebrate hosts, directly supporting assessment of malaria transmission risk and guiding vector control planning.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the study rapidly identify blood meals in wild-caught mosquitoes?",{"text":81,"@type":77},"Female Anopheles funestus mosquitoes are scanned using ATR-FTIR mid-infrared spectroscopy, and machine learning classifiers predict blood-meal sources. PCR confirmation provides ground truth for training and evaluation.",{"name":83,"@type":74,"acceptedAnswer":84},"What performance did the machine learning models achieve and how did HBI estimates compare to PCR?",{"text":85,"@type":77},"Logistic regression and multi-layer perceptron models achieved about 88%–90% accuracy. Estimated HBI values aligned well with the PCR-based standard HBI.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]