Real-time validation of Surface-Enhanced Raman Scattering substrates via convolutional neural network algorithm

Abstract

Development of next-generation sensor technologies could be advanced by exploiting surface enhanced Raman spectroscopy (SERS) substrates capable of considerably enhancing signal detection of analytes of interest down to single molecule levels. The widespread implementation of SERS, however, requires overcoming many of the existing and emerging challenges related to the enhancement inconsistency from the different parts of substrates, resulting in inconsistent readings. Here, a custom-designed convolutional neural networks (CNN) algorithm is developed to specifically analyse and validate substrates in real-time, providing a rapid output and identification of SERS active structures on each substrate enabling a consistently high signal enhancement. A computational algorithm has been developed to identify regions of high and consistent SERS activity from acquired optical microscopy images of SERS structures in real-time. The CNN model is tested on the inputted optical images and outputs the predicted consistent SERS-active structures or regions overlaid on top of the input image. The optimised CNN model used is a pre-trained neural network (VGG16) with the last layers fine-tuned. This model achieves an accuracy of 90% in classifying our uniquely developed electrohydrodynamically (EHD) fabricated, highly enhancing, SERS-active structures from optical images, enabling a real-time predictive tool for SERS substrates. The CNN output generates a heatmap, which provides feedback on the regions of high SERS activity, overlaid on the optical image to rapidly facilitate the SERS spectral acquisition measurements. Beyond its application for EHD, the versatility of the developed method renders it easily extendable for implementation with further, current and future, fabrication techniques of SERS substrates. The full script run yields an accuracy of 87.5 ± 12.0% for in-situ measurement acquisition, successfully demonstrating a proof-of-concept of rapidly identifying consistent SERS structures via the real-time CNN processing of optical images.