An Inside Look at the Implementation of Artificial Intelligence in Surface-Enhanced Raman Spectroscopy Applications

The integration of artificial intelligence (AI) with surface-enhanced Raman spectroscopy (SERS) is poised to advance various fields such as biomedicine, environmental protection, and food safety, according to a recent review article published in Small Methods (1).

SERS, a technique known for its ability to provide precise molecular fingerprints through Raman scattering, has long been celebrated for its high sensitivity and specificity (2). It plays a crucial role in detecting and identifying chemical and biological substances at very low concentrations (1,2). However, the complexity and vast data generated by SERS pose significant challenges, necessitating continuous improvements in the data interpretation technology (1).

AI with young man in the night | Image Credit: © Tierney - stock.adobe.com.

That is where AI can be useful, according to the researchers at Shanghai Jiao Tong University (1). The team, led by Zhou Chen and Jian Ye, highlight in their review how AI's ability to learn complex patterns and make sense of large data sets offers unprecedented opportunities for optimizing the SERS method (1). This includes the design of SERS substrates and reporter molecules, refining synthetic routes, improving instrumentation, and enhancing data preprocessing and analysis methods (1).

Read More: An Interview with AI About Its Potential Role in Vibrational and Atomic Spectroscopy

Throughout the review article, the authors make it clear that the application of AI in SERS applications improves upon traditional methods. Because these methods are often limited by human capacity and conventional computational approaches, traditional approaches are not optimized or able to handle the amount of data normally produced by SERS experiments (1). On the other hand, AI is equipped to handle these data sets because of its automated and pattern recognition capabilities. As a result, AI can significantly accelerate the optimization processes and yield deeper insights into the underlying physics and chemistry within the spectral data (1).

One of the key advancements the authors discussed is using AI in the design and synthesis of SERS substrates. These substrates, which amplify the Raman signal of the molecules being studied, are critical for the sensitivity of SERS (1). AI algorithms can analyze vast amounts of data to identify the most effective substrate materials and structures, leading to more efficient and effective SERS applications (1).

Furthermore, AI-driven approaches are changing the way data from SERS is processed and analyzed. Traditional data analysis methods can be time-consuming and prone to errors, especially when dealing with complex spectra (1). However, advancements in AI have permitted analysts to accurately interpret these spectra, identifying subtle differences and patterns that might be missed by human analysts (1). As a result of this technological advancement, the research process is accelerated while still delivering reliable results.

In addition to the above improvements, the integration of AI has helped modify SERS instruments in a positive way. AI can optimize the operational parameters of these instruments in real-time, ensuring that they are always performing at their best. This continuous optimization leads to more consistent and reliable measurements, further solidifying SERS as a powerful analytical tool (1).

Despite the significant progress made, AI in SERS applications still has challenges that need to be overcome. The authors highlight the need for large, high-quality data sets to train AI models, the integration of AI with existing laboratory workflows, and the development of user-friendly AI tools for researchers (1).

As AI continues to evolve, its application in SERS is expected to unlock new possibilities, driving advancements across various scientific and industrial domains. The findings published in Small Methods underscore the importance of continued investment in AI technologies to harness their full potential in enhancing SERS and other analytical techniques.

References

(1) Bi, X.; Lin, L.; Chen, Z.; Ye, J. Artificial Intelligence for Surface-Enhanced Raman Spectroscopy. Small Methods 2023, ASAP. DOI: 10.1002/smtd.202301243

(2) Wetzel, W. An Inside Look at the Latest in Surface-enhanced Raman Spectroscopy. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/an-inside-look-at-the-latest-in-surface-enhanced-raman-spectroscopy (accessed 2024-05-29).