Detection of glucose concentration in a turbid medium using a stacked auto-encoder deep neural network

Abstract

In order to detect the components of a turbid medium, this paper proposes a glucose concentration reconstruction method based on a stacked auto-encoder (SAE) deep neural network. Analysis of different optical properties is performed using multiple diffused reflection spectroscopy and multiple source-detector separation (SDS). In this experiment, a 20% intralipid solution was used to prepare 4%, 5% and 10% intralipid solutions as the research object. At a certain glucose concentration, thirty source-detector separations of diffused reflection spectral signals within the 0.47–4.095 mm range (0.125 mm interval) from the incident position were detected, and a SAE deep neural network was used for modeling and predicting glucose concentration under multiple spectra. The root mean square error of prediction (RMSEP) of the SAE deep neural network using the 4% and 5% intralipid solutions decreased by approximately 26.42% compared with the results using only the 4% intralipid solutions by partial least squares regression (PLSR) method. Moreover, the RMSEP of the SAE deep neural network using the 4% and 5% intralipid solutions decreased by approximately 34.25% compared with the PLSR method using the 5% intralipid solution sample. These results show that the reconstruction accuracy of the SAE deep neural network is higher than the traditional PLSR method, which proves that the SAE neural network is highly suitable for prediction of turbid medium concentration.