Saccharide concentration prediction from proxy sea surface microlayer samples analyzed via infrared spectroscopy and quantitative machine learning

The physical and chemical properties of the sea surface microlayer (SSML) are dynamic and complex. With an enrichment of organics from dissolved organic carbon (DOC) and many mechanisms for their release into the atmosphere, high-throughput analysis of SSML samples is necessary. Collection of more detailed information about the SSML would enable greater understanding of the release of ice nucleating and cloud condensation particles and provide critical feedback for climate models. The work presented herein details an investigation to develop machine learning (ML) methodology utilizing infrared spectroscopy data to accurately estimate saccharide concentrations in complex solutions. We evaluated several machine learning approaches toward this goal. Support Vector Regression (SVR) models are shown to predict the accurate generalized saccharide concentrations best. Our work presents an application combining fast spectroscopic techniques with ML to analyze SSML chemistry more efficiently, without sacrificing accuracy or precision.