Fig. 2: Variation in MIRS, machine-learning model architecture, the sensitivity of the trained model.

We collected the MIRS of 41,151 female mosquitoes belonging to three species from diverse laboratories, genetic backgrounds, and environments and three age classes spanning 1-17 days post pupal emergence. a, b Unsupervised clustering of MIRS measurements using Uniform Manifold Approximation and Projection of MIRS in two dimensional space (plot axes) from An. arabiensis, An. coluzzii and An. gambiae coloured according to site of origin (a) and source of variation (b). c Representative variation of mid-infrared absorption spectra of An. arabiensis, An. coluzzii and An. gambiae and of three age classes. d Schematic representation of the deep convolutional neural network that takes MIRS inputs and outputs mosquito age and species. The input layer (wavenumber values) is fed through five 1-dimensional convolutional layers, comprising of 16 filters each (convolutional layers region), followed by a dense layer of 500 features and age and species output layers (dense layers) that were used to make predictions.