SERS-Based Biosensors Combined with Machine Learning for Medical Application

Surface-enhanced Raman spectroscopy (SERS) has shown strength in non-invasive, rapid, trace analysis and has been used in many fields in medicine. Machine learning (ML) is an algorithm that can imitate human learning styles and structure existing content with the knowledge to effectively improve learning efficiency. Integrating SERS and ML can have a promising future in the medical field. In this review, we summarize the applications of SERS combined with ML in recent years, such as the recognition of biological molecules, rapid diagnosis of diseases, developing of new immunoassay techniques, and enhancing SERS capabilities in semi-quantitative measurements. Ultimately, the possible opportunities and challenges of combining SERS with ML are addressed.

Surface-Enhanced Raman Scattering-Based Odor Compass: Locating Multiple Chemical Sources and Pathogens

Olfaction is important for identifying and avoiding toxic substances in living systems. Many efforts have been made to realize artificial olfaction systems that reflect the capacity of biological systems. A sophisticated example of an artificial olfaction device is the odor compass which uses chemical sensor data to identify odor source direction. Successful odor compass designs often rely on plume-based detection and mobile robots, where active, mechanical motion of the sensor platform is employed. Passive, diffusion-based odor compasses remain elusive as detection of low analyte concentrations and quantification of small concentration gradients from within the sensor platform are necessary. Further, simultaneously identifying multiple odor sources using an odor compass remains an ongoing challenge, especially for similar analytes. Here, we show that surface-enhanced Raman scattering (SERS) sensors overcome these challenges, and we present the first SERS odor compass. Using a grid array of SERS sensors, machine learning analysis enables reliable identification of multiple odor sources arising from diffusion of analytes from one or two localized sources. Specifically, convolutional neural network and support vector machine classifier models achieve over 90% accuracy for a multiple odor source problem. This system is then used to identify the location of an Escherichia coli biofilm via its complex signature of volatile organic compounds. Thus, the fabricated SERS chemical sensors have the needed limit of detection and quantification for diffusion-based odor compasses. Solving the multiple odor source problem with a passive platform opens a path toward an Internet of things approach to monitor toxic gases and indoor pathogens.

Longitudinal Monitoring of Biofilm Formation via Robust Surface-Enhanced Raman Scattering Quantification of Pseudomonas aeruginosa-Produced Metabolites

Detection of bacterial metabolites at low concentrations in fluids with complex background allows for applications ranging from detecting biomarkers of respiratory infections to identifying contaminated medical instruments. Surface-enhanced Raman scattering (SERS) spectroscopy, when utilizing plasmonic nanogaps, has the relatively unique capacity to reach trace molecular detection limits in a label-free format, yet large-area device fabrication incorporating nanogaps with this level of performance has proven difficult. Here, we demonstrate the advantages of using chemical assembly to fabricate SERS surfaces with controlled nanometer gap spacings between plasmonic nanospheres. Control of nanogap spacings via the length of the chemical crosslinker provides uniform SERS signals, exhibiting detection of pyocyanin, a secondary metabolite of Pseudomonas aeruginosa, in aqueous media at concentration of 100 pg·mL^-1. When using machine learning algorithms to analyze the SERS data of the conditioned medium from a bacterial culture, having a more complex background, we achieve 1 ng·mL^-1 limit of detection of pyocyanin and robust quantification of concentration spanning 5 orders of magnitude. Nanogaps are also incorporated in an in-line microfluidic device, enabling longitudinal monitoring of P. aeruginosa biofilm formation via rapid pyocyanin detection in a medium effluent as early as 3 h after inoculation and quantification in under 9 h. Surface-attached bacteria exposed to a bactericidal antibiotic were differentially less susceptible after 10 h of growth, indicating that these devices may be useful for early intervention of bacterial infections.