Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Abstract

Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS’s full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

Unveiling practical considerations for reliable and standardized SERS measurements: lessons from a comprehensive review of oblique angle deposition-fabricated silver nanorod array substrates

“…To achieve this objective, chemometric analysis and machine learning techniques emerge as potent tools, which can efficiently handle these spectra and uncover valuable insights for SERS detection or spectral analysis. [236][237][238][239][240] Chemometric analyses, such as principal component analysis (PCA), partial least squares (PLS), and cluster analysis, are widely used for SERS spectra due to itheir ability to reduce data dimensionality and uncover hidden patterns. These techniques are particularly useful when dealing with large datasets or when the relationship between the spectra and analyte properties is not well understood.…”

“…To achieve this objective, chemometric analysis and machine learning techniques emerge as potent tools, which can efficiently handle these spectra and uncover valuable insights for SERS detection or spectral analysis. 236–240…”

Novel Digital SERS-Microfluidic Chip for Rapid and Accurate Quantification of Microorganisms

“…Moreover, the integration of SERS with microfluidic devices has been explored. 21,22 The microfluidic platform enables precise control over sample processing and delivery, minimizing sample contamination and reducing the impact on SERS intensity, thereby achieving more precise and repeatable microbial quantitative detection. 26−28 However, these methods still primarily rely on the SERS intensity as the main determinant.…”

“…Furthermore, the progress in nanotechnology has facilitated the development of SERS active nanoprobes that can specifically bind to target microorganisms utilizing recognition elements like antibodies or aptamers. ,− These nanoprobes can generate SERS signals proportional to the concentration of microorganisms. Moreover, the integration of SERS with microfluidic devices has been explored. , The microfluidic platform enables precise control over sample processing and delivery, minimizing sample contamination and reducing the impact on SERS intensity, thereby achieving more precise and repeatable microbial quantitative detection. − However, these methods still primarily rely on the SERS intensity as the main determinant. Furthermore, one strategy involves dispersing microbial particles on the substrate and scanning the dispersed area of the sample point by point using SERS mapping technology to count the microorganisms based on characteristic signals. , However, the absence of discretization and physical isolation of microbial samples, along with the variations in size, clustering, random distribution, and other challenges associated with microorganisms, can result in missed or repeated scanning counts, ultimately leading to inaccuracies in the quantitation results.…”

Plasmonic Sensors: A New Frontier in Nanotechnology

“…SERS is a surface-sensitive technique that can detect analytes even at the level of single molecules [ 51 ]. In this Special Issue, Beeram et al provide an excellent overview of recent trends in SERS-based plasmonic sensors for disease diagnostics, biomolecule detection, and machine learning techniques [ 52 ]. The authors review the work of the past decade and use simplified language to address the needs of an interdisciplinary audience.…”