Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning

Abstract

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. must survive for at least 10 days to possibly transmit malaria. Anopheles Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles and . mid-infrared spectroscopy-based prediction of gambiae An. arabiensis mosquito age structures was statistically indistinguishable from true modelled distributions. The accuracy of classifying mosquitoes by species was 82.6%. The method has a negligible cost per mosquito, does not require highly trained personnel, is rapid, and so can be easily applied in both laboratory and field settings. Our results indicate this method is a promising alternative to current mosquito species and age-grading approaches, with further improvements to accuracy and expansion for use Any reports and responses or comments on the article can be found at the end of the article. approaches, with further improvements to accuracy and expansion for use with other mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.

Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis

“…After blood-feeding, mosquitoes were held for 6 h for digestion to begin and to minimize potential differences associated with extent of blood digestion in the gut [39]. After the holding period, the mosquitoes were killed using chloroform [33] and preserved in micro-centrifuge tubes with silica gel to keep them dry before scanning. Each sample was labelled by date, vertebrate host type, mosquito species, sample ID and age.…”

“…MIR spectra were captured (spectral range 4000–400 cm −1 ), with spectral resolution set at 5 cm −1 . Each individual specimen was scanned 32 times in 30 s, and resulting spectra averaged to obtain a single representative spectrum as previously described [33]. The spectra were recorded in absorbance units and stored using Bruker OPUS software [36].…”

“…The OPUS software [36] was used to clean and compensate spectra with water vapor absorption bands (intense bands centered around 2340 cm −1 , 3600 and 550 cm −1 wavenumbers) and carbon dioxide (CO 2 ) interference bands (4000–3400 cm −1 and 2200–1300 cm −1 ) as previously described [33]. The cleaned spectral data were converted from the Bruker OPUS format to text files in Python™.…”

“…The cleaned spectral data were converted from the Bruker OPUS format to text files in Python™. Further spectral cleaning was done by discarding spectra with low intensities (below 0.11 absorbance units; mostly between 400 and 500 cm −1 ) and spectra with no features (flat spectra) [33]. The final cleaned spectra matrix was saved in comma separated values (CSV) format for further analysis.…”

“…Further improvements could potentially be achieved by relying on mid-infrared (MIR) wavelengths (4000 cm −1 to 400 cm −1 ), which compared to those of NIR (12,500 cm −1 to 400 cm −1 ), Fig. 1 also allow detection of changes in chemical composition of samples, and can clearly show contributions of different chemical bonds of product constituents in separate peaks [33].

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