Precision classification and quantitative analysis of bacteria biomarkers via surface-enhanced Raman spectroscopy and machine learning

[Display omitted] •Integrating SERS with convolutional neural networks results in 99.99 % classification accuracy for six bacterial biomarkers.•High-accuracy concentration predictions for bacterial biomarkers are achieved through CNN regression.•This approach overcomes challenges posed by spectral variability across different concentration ranges.•The study highlights the transformative potential of advanced machine learning algorithms to enhance biomedical diagnostics. The SERS spectra of six bacterial biomarkers, 2,3-DHBA, 2,5-DHBA, Pyocyanin, lipoteichoic acid (LTA), Enterobactin, and β-carotene, of various concentrations, were obtained from silver nanorod array substrates, and the spectral peaks and the corresponding vibrational modes were identified to classify different spectra. The spectral variations in three different concentration regions due to various reasons have imposed a challenge to use classic calibration curve methods to quantify the concentration of biomarkers. Depending on baseline removal strategy, i.e., local or global baseline removal, the calibration curve differed significantly. With the aid of convolutional neural network (CNN), a two-step process was established to classify and quantify biomarker solutions based on SERS spectra: using a specific CNN model, a remarkable differentiation and classification accuracy of 99.99 % for all six biomarkers regardless of the concentration can be achieved. After classification, six regression CNN models were established to predict the concentration of biomarkers, with coefficient of determination R2 > 0.97 and mean absolute error (MAE)