[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127797-en":3,"doc-seo-127797-105":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},127797,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Advancing age grading techniques for Glossina morsitans morsitans - through mid-infrared spectroscopy and machine learning","Tsetse flies transmit African trypanosomes that cause sleeping sickness in humans and animal trypanosomiasis in wildlife and livestock. Determining fly age supports vector-control effectiveness assessment and disease-risk modelling, yet conventional age-grading methods are labour-intensive, slow, and often unreliable due to limited skilled personnel. This study evaluates mid-infrared spectroscopy (MIRS) combined with machine learning to estimate tsetse sex and age from cuticle spectra. Using insectary-reared Glossina m. morsitans, ML achieved 96% sex classification accuracy and predicted age groups with 94% and 87% accuracy for males and females. Key discriminative regions reflect lipid and protein content.","Biology Methods and Protocols, 2024, bpae058  \n[https://doi.org/10.1093/biomethods/bpae058](https://doi.org/10.1093/biomethods/bpae058)  \n[Advance Access Publication Date: 17 August 2024](Advance Access Publication Date: 17 August 2024)  \nMethods Article  \nAdvancing age grading techniques for Glossina morsitans morsitans, vectors of African trypanosomiasis, through mid-infrared spectroscopy and machine learning  \nMauro Pazmi~no-Betancourth 1, Ivan Casas Gmez-Uribarri  1, Karina Mondragon-Shem  2, Simon A. Babayan  1, Francesco Baldini  1,3,†,􀀃 , Lee Rafuse Haines 2,4,†,􀀃  \n1School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, G12 8QQ, Glasgow, United Kingdom 2Department of Vector Biology, Liverpool School of Tropical Medicine, L3 5QA, Liverpool, United Kingdom  \n3Environmental Health, and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Ifakara, P. O. Box 53, United Republic of Tanzania 4Department of Biological Sciences, University of Notre Dame, 46556, Notre Dame, United States  \n􀀃 Correspondence address. School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, United Kingdom.  \nE-mail: [Francesco.Baldini@glasgow.ac.uk](Francesco.Baldini@glasgow.ac.uk) (F.B.); Department of Biological Sciences, University of Notre Dame, 46556, Notre Dame, United States.  \nE-mail: [lhaines@nd.edu](lhaines@nd.edu) (L.R.H.)  \n†These authors equally supervised the work.  \nAbstract  \nTsetse are the insects responsible for transmitting African trypanosomes, which cause sleeping sickness in humans and animal trypanosomiasis in wildlife and livestock. Knowing the age of these flies is important when assessing the effectiveness of vector control programs and modelling disease risk. Current methods to assess fly age are, however, labour-intensive, slow, and often inaccurate as skilled personnel are in short supply. Mid-infrared spectroscopy (MIRS), a fast and cost-effective tool to accurately estimate several biological traits of insects, offers a promising alternative. This is achieved by characterising the biochemical composition of the insect cuticle using infrared light coupled with machine–learning (ML) algorithms to estimate the traits of interest. We tested the performance of MIRS in estimating tsetse sex and age for the first-time using spectra obtained from their cuticle. We used 541 insectaryreared Glossina m. morsitans of two different age groups for males (5 and 7 weeks) and three age groups for females (3 days, 5 weeks, and 7 weeks) . Spectra were collected from the head, thorax, and abdomen of each sample. ML models differentiated between male and female flies with a 96% accuracy and predicted the age group with 94% and 87% accuracy for males and females, respectively. The key infrared regions important for discriminating sex and age classification were characteristic of lipid and protein content. Our results support the use of MIRS as a rapid and accurate way to identify tsetse sex and age with minimal pre-processing. Further validation using wild-caught tsetse could pave the way for this technique to be implemented as a routine surveillance tool in vector control programmes.  \nLay Summary  \nMale and female tsetse transmit the parasites that cause sleeping sickness in humans and nagana in livestock. To control these diseases, knowing the age of these flies is important, as it helps evaluate the efficacy of control measures and assess disease risk. However, current age-grading methods are laborious, often unreliable, and in the case of male tsetse, highly inaccurate. This study explores a novel approach that uses mid-infrared spectroscopy (MIRS) to estimate the age of individual tsetse. Machine learning (ML) can detect signatures in MIRS for components of a fly's cuticle which differ between sexes and change as they age. We trained ML models that distinguished male from female flies with 96% accuracy and predicted the correct age group with 94% a","cbCaieTl7HbUpFan","https://ap.wps.com/l/cbCaieTl7HbUpFan","pdf",1943779,3,1,10,"English","en",105,"# Abstract\n## Lay Summary\n# Introduction","[{\"question\":\"Why is determining tsetse fly age important for disease control?\",\"answer\":\"Age information helps evaluate the effectiveness of vector control programmes and supports modelling of disease risk. It also improves monitoring of disease-carrying insects.\"},{\"question\":\"What method does the study propose for age grading tsetse flies?\",\"answer\":\"The study uses mid-infrared spectroscopy (MIRS) of the fly cuticle combined with machine-learning (ML) algorithms to estimate sex and age.\"},{\"question\":\"How accurate were the machine-learning models for sex and age classification?\",\"answer\":\"The models differentiated male and female tsetse with 96% accuracy. Age-group prediction reached 94% accuracy for males and 87% for females.\"}]","Advancing age grading techniques for Glossina morsitans morsitans - through mid-infrared spectroscopy and machine learning | PDF",1785941800,25,{"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},"advancing-age-grading-techniques-for-glossina-morsitans-morsitans-through-mid-infrared-spectroscopy-and-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/advancing-age-grading-techniques-for-glossina-morsitans-morsitans-through-mid-infrared-spectroscopy-and-machine-learning/127797/",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","2026-08-23","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 determining tsetse fly age important for disease control?","Question",{"text":76,"@type":77},"Age information helps evaluate the effectiveness of vector control programmes and supports modelling of disease risk. It also improves monitoring of disease-carrying insects.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What method does the study propose for age grading tsetse flies?",{"text":81,"@type":77},"The study uses mid-infrared spectroscopy (MIRS) of the fly cuticle combined with machine-learning (ML) algorithms to estimate sex and age.",{"name":83,"@type":74,"acceptedAnswer":84},"How accurate were the machine-learning models for sex and age classification?",{"text":85,"@type":77},"The models differentiated male and female tsetse with 96% accuracy. 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