小波分析在人血清中葡萄糖短波近红外定量中的应用

Application of Wavelet Analysis in Measurement of Glucose Concentration in Human Serum by Short-wave Near Infrared Spectra

利用小波分析对13名志愿者18个血清样品的短波近红外光谱进行去噪预处理,以血糖仪测定的血糖为参考,采用间隔偏最小二乘法(iPLS)在700 nm～1060nm短波近红外波段建立血糖浓度预测模型。由相关系数(R)和预测标准差(RMSEP)对预测模型的精确度进行了评价。预测模型的相关系数为0.9654,均方根预测误差为0.2435,并和采用傅立叶变换去噪方法及iPLS建模的结果进行了比较。结果表明:小波分析预处理数据的方法能更有效地扣除噪声干扰,使模型具有更强的抗干扰能力和更高的预测精度。

Wavelet analysis method was used in data denoising processing of 13 volunteers＇ 18 serum samples＇ short-wave near-infrared absorption spectra from 700 nm to 1 060 nm. Using the blood glucose value measured by glucose meter as standard value, iPLS（ interval Partial Least Square） method was used to make the glucose prediction model. The accuracy of the predictive model was estimated by correlation coefficient （R） and root-mean-square prediction error （RMSEP）. The result is R = 0. 9654, RMSEP = 0.2435. By Comparing the result of data denoising by wavelet analysis with that by Fourier transformation method, the result shows that the wavelet analysis can denoising the signal more effectively and thus build a better predictive model.