Self-supervised learning for inter-laboratory variation minimization in surface-enhanced Raman scattering spectroscopy

Surface-enhanced Raman scattering (SERS) remains a significant area of research since it’s discovery 50 years ago. The surface-based technique has been used in a wide variety of fields, most prominently in chemical detection, cellular imaging and medical diagnostics, offering high sensitivity and specificity when probing and quantifying a chosen analyte or monitoring nanoparticle uptake and accumulation. However, despite its promise, SERS is mostly confined to academic laboratories and is not recognised as a gold standard analytical technique. This is due to the variations that are observed in SERS measurements, mainly caused by poorly characterised SERS substrates, lack of universal calibration methods and uncorrelated results. To convince the wider scientific community that SERS should be a routinely used analytical technique, the field is now focusing on methods that will increase the reproducibility of the SERS signals and how to validate the results with more well-established techniques. This review explores the difficulties experienced by SERS users, the methods adopted to reduce variation and suggestions of best practices and strategies that should be adopted if one is to achieve absolute quantification. Graphical Abstract

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Self-supervised learning for inter-laboratory variation minimization in surface-enhanced Raman scattering spectroscopy

Abstract: Surface-Enhanced Raman Scattering Spectroscopy (SERS) is still considered poorly reproducible despite its numerous advantages and is not a sufficiently robust analytical technique for routine implementation outside of academia. In this...

Cited by 3 publications

References 26 publications

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