Application of jitter augmentation in classification of Raman scattering from glucose liquid with several machine learning models

Abstract

The utilisation of machine learning technology for invasive diabetes diagnosis has emerged as a prominent research area in the medical field in recent years. The study employs Raman spectroscopy of a glucose fluid sample to ascertain glucose levels. We produce glucose-liquid samples with 14 varying ratios of glucose to pure water to replicate the 14 glucose concentrations found in human blood and acquire the corresponding Raman spectra. With the optimistic result of the previous study, we also design the jittering augmentation approach to enhance the dataset and the dataset size was increased by a factor of 100. We employ a 1-D Convolutional Neural Network to ascertain glucose levels in samples. The result proves the positive effect of jitter augmentation for improving the performance of the deep learning model as well as high accuracy of these models in classification of Raman scattering.