ArticleMalaria journal2019
Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis.
Article in Malaria journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The application of spectroscopy techniques for diagnosis of malaria parasites and arboviruses and surveillance of mosquito vectors: A systematic review and critical appraisal of evidence.PLoS neglected tropical diseases · 2021Pooled it
- Age-grading and species identification of male mosquito Anopheles gambiae s.l. using mid-infrared spectroscopy and machine learning.Scientific reports · 2026Article
- Towards scalable age-grading of Aedes albopictus mosquito using mid-infrared spectroscopy and machine learning.Scientific reports · 2025Article
- Reagent-free detection of Plasmodium falciparum malaria infections in field-collected mosquitoes using mid-infrared spectroscopy and machine learning.Scientific reports · 2024Article
- Rapid assessment of the blood-feeding histories of wild-caught malaria mosquitoes using mid-infrared spectroscopy and machine learning.Malaria journal · 2024Article
- Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus.Parasites & vectors · 2024Article
- Advancing age grading techniques forBiology methods & protocols · 2024Article
- Key considerations, target product profiles, and research gaps in the application of infrared spectroscopy and artificial intelligence for malaria surveillance and diagnosis.Malaria journal · 2023Review
- Evaluation of diffuse reflectance spectroscopy for predicting age, species, and cuticular resistance of Anopheles gambiae s.l under laboratory conditions.Scientific reports · 2023Article
- Using transfer learning and dimensionality reduction techniques to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra.BMC bioinformatics · 2023Article
- Article
- Effects of sample preservation methods and duration of storage on the performance of mid-infrared spectroscopy for predicting the age of malaria vectors.Parasites & vectors · 2022Article
- Autofluorescent Biomolecules in Diptera: From Structure to Metabolism and Behavior.Molecules (Basel, Switzerland) · 2022Review
- Detection of Plasmodium falciparum in laboratory-reared and naturally infected wild mosquitoes using near-infrared spectroscopy.Scientific reports · 2021Article
- Podoconiosis: key priorities for research and implementation.Transactions of the Royal Society of Tropical Medicine and Hygiene · 2020Article
- A Literature Review of Host Feeding Patterns of InvasiveInsects · 2020Review
- Assessment of vector-host-pathogen relationships using data mining and machine learning.Computational and structural biotechnology journal · 2020Review
- Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis.Malaria journal · 2019Article
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13 authors.
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Abstract
backgroundThe propensity of different Anopheles mosquitoes to bite humans instead of other vertebrates influences their capacity to transmit pathogens to humans. Unfortunately, determining proportions of mosquitoes that have fed on humans, i.e. Human Blood Index (HBI), currently requires expensive and time-consuming laboratory procedures involving enzyme-linked immunosorbent assays (ELISA) or polymerase chain reactions (PCR). Here, mid-infrared (MIR) spectroscopy and supervised machine learning are used to accurately distinguish between vertebrate blood meals in guts of malaria mosquitoes, without any molecular techniques.
methodsLaboratory-reared Anopheles arabiensis females were fed on humans, chickens, goats or bovines, then held for 6 to 8 h, after which they were killed and preserved in silica. The sample size was 2000 mosquitoes (500 per host species). Five individuals of each host species were enrolled to ensure genotype variability, and 100 mosquitoes fed on each. Dried mosquito abdomens were individually scanned using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra (4000 cm
resultsThe logistic regression (LR) model achieved the highest accuracy, correctly predicting true vertebrate blood meal sources with overall accuracy of 98.4%. The model correctly identified 96% goat blood meals, 97% of bovine blood meals, 100% of chicken blood meals and 100% of human blood meals. Three percent of bovine blood meals were misclassified as goat, and 2% of goat blood meals misclassified as human.
conclusionMid-infrared spectroscopy coupled with supervised machine learning can accurately identify multiple vertebrate blood meals in malaria vectors, thus potentially enabling rapid assessment of mosquito blood-feeding histories and vectorial capacities. The technique is cost-effective, fast, simple, and requires no reagents other than desiccants. However, scaling it up will require field validation of the findings and boosting relevant technical capacity in affected countries.
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