利用中红外光谱分析技术进行人体血糖浓度检测时,由于高频噪声与低频基线难以避免的混入,以致很难从光谱数据中提取微弱的血糖信息,因此,提出了一种改进的小波分析光谱预处理方法以期去除其影响。该方法首先按照经验将光谱数据进行尺度J的小波分解,而后通过能量谱分析确定最终的分解尺度J。对尺度J下的细节部分进行极大极小阈值滤波,将其他各尺度下的细节部分直接置0,同时在低频部分以二次曲线拟合由人体组织散射引入的基线漂移并加以去除。将该预处理方法应用于人体血糖无创检测实验数据,以交互验证评价模型,预测值与参考值的相关系数为0.88,预测均方根误差为1.14 mmol/L,模型的预测精度得到较大幅度提高。

When measure blood glucose concentration using Mid-IR spectral analysis technique,it is inevitable that high-frequency noises and low-frequency baseline drifting will be added to the spectral data. Thus,it is difficult to extract the weak signal of blood glucose from the obtained spectra. An improved preprocessing method based on wavelet analysis is presented,which can eliminate the noise and correct the baseline drifting at the same time. Firstly,spectra were decomposed into detail and approximation at level J which was estimated according to experience. Then the decomposing level J was determined by further analysis of power density Spectra. The noise was eliminated by filtering high-frequency signal at level J with minimum/maxmum threshold and set high-frequency signal at the other levels to 0. And the baseline at level J was fitted with a quadratic polynomial whose coefficients were calculated by least square curve fitting method. Then remove the quadratic polynomial baseline from the spectral data. After applying this preprocessing method to a set of oral glucose tolerance test data,the cross validation result reveals that the correlation coefficient between prediction value and true value is 0.88,and the root mean square error of prediction is 1.14 mmol/L. The precision of calibration is greatly improved.