A non-contact methodology based on imaging analysis, chemometrics, and machine learning to predict the lethality of stressors on C. elegans populations in liquid culture

This work was centred on developing an objective, reproducible and non-destructive methodology to predict the lethality of C. elegans populations contained in liquid culture mediums, addressing the handicaps presented for imaging analysis in those media types from a numerical point of view, applying chemometric and machine learning procedures on imaging data obtained with a basic image device and processing. The experiment was carried out by taking videos from nematode populations exposed to different conditions of three stressors (hydrogen peroxide, heat and UV radiation). The processed video datasets were used as predictors for different configurations in regression methods. The dimensionality reduction approach improved the prediction capacity of the imaging information compared to the raw dataset. Moreover, the best result was achieved with a super learner model, demonstrating the synergistic effect of combining results from models with lower prediction capacity to develop a meta-model with high prediction capabilities.