Detection of Polysaccharide Markers of Fungal Infections by Surface-Enhanced Raman Scattering and Machine Learning Methods.

In this study, we used the SERS method for the first time to measure the spectra of four polysaccharide markers of fungal infections: linear β-(1→3)- and β-(1→6)-linked D-glucans, branched mannan of Candida albicans and galactomannan of Aspergillus fumigatus. Aqueous solutions of the polysaccharides were studied in concentrations from 10 pg/mL to 100 μg/mL. The spectra were analyzed using machine learning methods: principal component analysis for data visualization and partial least squares with a ridge regularizer, which were used to construct metrics reflecting the accuracy of substance recognition relative to each other. The spectral changes with varying analyte concentration were observed and stable calibration has been achieved. Subsequent measurements of fungal polysaccharides in the presence of a physiological concentration of human serum albumin (45 mg/mL), used to model blood serum, enabled accurate analyte detection in a clinically relevant concentration range of 10 pg/mL to 100 ng/mL. In this case, the calibration dependence was calculated using the partial least squares method with the L1-regularizer. Blind testing was evaluated using a train-derived applicability-domain criterion based on the disagreement between the model prediction and an independent concentration estimate.