Comparison of dimensionality reduction techniques for cross-source transfer of fluorescence contaminant detection models

Fluorescence spectroscopy shows promise as a tool for monitoring water quality due to its real-time capabilities and sensitive detection of several compounds of interest. Previous work has shown the possible use of fluorescence to detect and quantify low levels of polycyclic aromatic hydrocarbons and fluorescing pesticides. However, the fluorescence-based contaminant detection models are highly source-specific and require significant effort and resources to build and calibrate them for each source water of interest. In this study, the novel application of data processing techniques was investigated to enable the transfer of fluorescence detection models from one water source to another. A contaminant detection model from a relatively consistent and low organic background source (Lake Ontario, TOC: 2.07–2.26 mg L−1) was transferred to the Otonabee River, which has higher organic concentrations and distinct characteristics (TOC: 5.20–5.66 mg L−1). Only a few additional fluorescence spectra of the background water quality and contaminants of interest were required to successfully transfer the model, without the need for labelled samples in the new source. Notable differences in peak location and spectral shape of identical compounds were found in source-specific models between the two water sources, implying variability in fluorescence signals resulting from environmental conditions. Despite the impact of environmental conditions, features identified by principal component analysis (PCA) and an autoencoder produced sensitive transferred models capable of addressing the spatial and temporal source diversity with mean absolute error (MAE)