Publications

Bacterial sensing involves complex and variable samples that require advanced handling and analytical methods. To address these challenges, machine learning—especially deep learning—and SERS-based microfluidics have shown great promise. While previous studies have majorly focused on 1D spectral classification, the use of 2D representations of SERS spectra has not yet been explored, particularly for on-chip bacterial identification. In this work, we introduce a novel framework that combines SERS-enabled microfluidics with optimized 2D convolutional neural networks (2D-CNNs) for bacterial classification. SERS integration inside microfluidic chips was achieved through direct laser writing, enabling custom active areas and efficient on-chip measurements. We systematically evaluated nine distinct 1D-to-2D spectral transformations, with spectrogram and continuous wavelet transform yielding test accuracies of 99% and 97%, respectively, on controlled datasets. Using transfer learning, we achieved 100% accuracy on the on-chip dataset, demonstrating the model’s adaptability to new data. In contrast, other transformations, like pairwise distance and autocorrelation, performed below 93%, indicating their limited ability to capture subtle spectral features. This framework offers high sample control, parallelization, and the potential for expanding the bacteria database, making it ideal for low-data-volume situations such as rare infections. Further development and testing across strains, environments, and practical challenges can further improve our approach’s reliability for real-world diagnostics.

Surface-enhanced Raman spectroscopy (SERS) is a powerful optical sensing technique used in various applications, including medicine, microbiology, and environmental analysis. Planar SERS substrates are of particular interest due to their ease of integration in lab-on-chips and better reproducibility compared to colloidal SERS. The performance of these SERS substrates is quantified using metrics such as enhancement factor, sensitivity, and reproducibility. However, there is yet to be a consensus on how to practically compare and interpret such metrics in publications and experiments. These performance metrics are strongly influenced by the nanostructures’ material, architecture, element sizes, as well as the circumstances surrounding the experiments. Understanding the effect of these characteristics on the SERS substrates’ performance could not only enable a better performance but also direct their development for different applications. Thus, we developed a planar SERS-substrate characterization protocol to explore the correlation between the nanostructures’ physical characteristics and the performance metrics through coordinate-transformed spectroscopic measurements over structure-characterized areas. Seven commercial SERS substrates, with various surface architectures fabricated using different fabrication technologies, were studied using this benchmarking protocol. The results demonstrated how this protocol can indicate a SERS substrate’s suitability for a specific application, thus, guiding the substrate’s further adaptations or development.