[Development of human blood glucose noninvasive measurement system based on near infrared spectral technology].

A small non-invasive measurement system for human blood glucose has been developed, which can achieve fast, real-time and non invasive measurement of human blood glucose. The device is mainly composed of four parts, i. e. fixture, light system, data acquisition and processing systems, and spectrometer. A new scheme of light source driving was proposed, which can meet the requirements of light source under a variety of conditions of spectral acquisition. An integrated fixture design was proposed, which not only simplifies the optical structure of the system, but also improves the reproducibility of measurement conditions. The micro control system mainly achieves control function, dealing with data, data storage and so on. As the most important component, microprocessor DSP TMS320F2812 has many advantages, such as low power, high processing speed, high computing ability and so on. Wavelet denoising is used to pretreat the spectral data, which can decrease the loss of incident light and improve the signal-to-noise ratio. Kernel partial least squares method was adopted to build the mathematical model, which can improve the precision of the system. In the calibration experiment of the system, the standard values were measured by One-Touch. The correlation coefficient between standard blood glucose values and truth values is 0.95. The root mean square error of measurement is 0.6 mmol x L(-1). The system has good reproducibility.

[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.