Research on continuum power regression in noninvasive measurement of human blood glucose

Accurate measurement of human blood glucose concentration is very significant for the treatment of diabetes. In the present paper, the method of continuum power regression can improve the predictive accuracy of noninvasive measurement of human blood glucose concentration with near infrared spectroscopy. This method is the expansion of the traditional method of partial least squares (PLS). It can achieve simpleness, and can significantly improve predictive accuracy when the power coefficient is fit. Using the method, quantitative analysis models of four ingredient experiment and noninvasive experiment of body were established, and these models can be used to predict the predictive samples. Experimental results show that compared with the PLS, the quantitative analysis models of this method not only can improve predictive accuracy, but also can set different power coefficient for different individuals to achieve the best results of models. According to different individuals, the power coefficient can be selected flexibly, which is of great value to the research on noninvasive measurement of human blood glucose concentration with near infrared spectroscopy.