Detection of glucose concentration in a turbid medium using a stacked auto-encoder deep neural network
Detection of glucose concentration in a turbid medium using a stacked auto-encoder deep neural network
复制标题
使用堆叠式自动编码器检测混浊介质中的葡萄糖浓度
DOI：
10.1016/j.infrared.2020.103198
复制
发表时间：
2020-03-01
影响因子：
3.3
通讯作者：
Wang, Yao
中科院分区：
文献类型：
--
作者：
Han, Guang;Liu, Fang;Wang, Yao
为了检测混浊介质的成分，本文提出了一种基于堆叠式自动编码器（SAE）深度神经网络的葡萄糖浓度重建方法。使用多重漫反射光谱和多源检测器分离（SDS）进行不同的光学性质的分析。本实验以20%的拟除虫菊酯溶液配制成4%、5%和10%的拟除虫菊酯溶液作为研究对象。在一定的葡萄糖浓度下，检测距离入射位置0.47-4.095 mm范围内（0.125 mm间隔）的漫反射光谱信号的三十个源-检测器间隔，并使用SAE深度神经网络对多个光谱下的葡萄糖浓度进行建模和预测。采用偏最小二乘回归（PLSR）方法，与仅使用4%内毒素溶液的结果相比，使用4%和5%内毒素溶液的SAE深度神经网络的预测均方根误差（RMSEP）降低了约26.42%。此外，使用4%和5%的内毒素溶液的SAE深度神经网络的RMSEP与使用5%的内毒素溶液样品的PLSR方法相比降低了约34.25%。这些结果表明，SAE深度神经网络的重构精度高于传统的PLSR方法，证明SAE神经网络高度适用于混浊介质浓度的预测。
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.