Noninvasive blood glucose sensing by near-infrared spectroscopy based on PLSR combines SAE deep neural network approach

Abstract

Abstract Near-infrared spectroscopy has been considered as one of the most effective methods for noninvasive blood glucose sensing. Due to the strong scattering of human tissues and the differences among individuals, the relationship between spectral data and blood glucose concentration is nonlinear. Therefore, the linear prediction model has limitations when modeling multiple human samples. The present paper proposes a hybrid model in order to improve the prediction accuracy and versatility of the method, which was based on integrated linear partial least square regression (PLSR) with the nonlinear stacked auto-encoder (SAE) deep neural network. In this work, the diffuse reflectance spectrum of the palm was measured at six different wavelengths in 19 healthy subjects. The prediction results of multiple samples demonstrated that the correlation coefficients of the PLSR-SAE model is improved from 0.3021 to 0.9216 on average, which significantly optimizes the prediction effect compared with the traditional PLSR model. In addition, the prediction accuracy of Support Vector Regression (SVR) model and PLSR-SAE model are 0.8243 and 0.9216 respectively. Furthermore, in Clarke error grid analysis, the PLSR-SAE model could achieve 97.96% of points in A region, which has demonstrated that the prediction accuracy of this noninvasive blood glucose detection method might meets the precision range of clinical laboratory standards. Furthermore, it shows the potential of combining linear and nonlinear regression models for noninvasive prediction of other blood components.