Advancing age grading techniques for Glossina morsitans morsitans, vectors of African trypanosomiasis, through mid-infrared spectroscopy and machine learning

Abstract

Tsetse 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. However, current methods to assess fly age are 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 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 insectary-reared 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. Machine learning 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 fast and accurate way to identify tsetse sex and age with minimal pre-processing. Further validation using wild-caught tsetse can pave the way for this technique to be implemented as a routine surveillance tool in vector control programmes. Author summaryMale 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 can detect signatures in MIRS that help identify the composition of a flys cuticle, which differs between sexes and changes as they age. We trained machine learning models that distinguished male from female flies with 96% accuracy and predicted the correct age group with 94% accuracy for males and 87% accuracy for females. MIRS offers a fast and reliable way to identify tsetse sex and age with minimal preparation. If this method is successfully validated with wild flies, it holds the potential to vastly increase the accuracy of the way we monitor and combat these disease-carrying insects, thus offering significant advantages in our efforts to control them.