Quantitative analysis of Raman spectra for glucose concentration in human blood using Gramian angular field and convolutional neural network

In this study, convolutional neural network based on Gramian angular field (GAF-CNN) was firstly proposed. The 1-D Raman spectral data was converted into images and used for predicting the biochemical value of blood glucose. 106 sets of blood spectrums were acquired by Fourier transform (FT) Raman spectroscopy. Spectral data ranging from 800 cm(-1) to 1800 cm(-1) were selected for quantitative analysis of the blood glucose. Data augmentation was used to train neural networks and normalize the Raman spectra. And, we applied principal component analysis (PCA) for dimension reduction and information extraction. The root mean squared error of prediction (RMSEP) are 0.06570 (GADF) and 0.06774 (GASF), the determination coefficient of prediction (R-2) are 0.99929 (GADF) and 0.99925 (GASF), and the residual predictive deviation of prediction (RPD) are 37.56324 (GADF) and 36.43362 (GASF). GAF-CNN model performed better for predicting of glucose concentration. The GAF-CNN model can be used to establish a calibration model to predict blood glucose concentration. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

In this study, a convolutional neural network based on Gramian angular field (GAF-CNN) was proposed to predict the biochemical value of blood glucose using converted images of 1-D Raman spectral data. A total of 106 sets of blood spectrums were acquired by Fourier transform Raman spectroscopy, and spectral data ranging from 800 cm(-1) to 1800 cm(-1) were selected for quantitative analysis. Data augmentation, normalization, and principal component analysis were applied for training, dimension reduction, and information extraction. The GAF-CNN model outperformed in predicting glucose concentration and could be used to establish a calibration model for blood glucose prediction.