West China School of Pharmacy, Sichuan University, Chengdu 610041, People's Republic of China.
Appl Spectrosc. 2014;68(4):428-33. doi: 10.1366/13-07250.
The goal of this research was to develop a method for noninvasive blood glucose assay. Near-infrared (NIR) spectroscopy and Raman spectroscopy, two more promising techniques compared to other methods, were investigated in two kinds of artificial plasma (AP). Calibration models were generated by performing partial least squares (PLS) regression and optimized individually by considering spectral range, spectral pretreatment methods, and number of model factors. The two spectroscopic models were validated for the determination of glucose, and the results show that the two spectroscopic models established are robust, accurate, and repeatable. Compared to Raman spectroscopy, the performance of NIR spectroscopy was much better, with lower root mean square errors of cross-validation (RMSECV) of 0.128 and 0.094 mg/ml, lower root mean square errors of validation (RMSEP) of 0.061 and 0.046 mg/ml, higher correlation coefficients (R) of 99.15% and 99.55%, and higher residual predictive deviations (RPD) of 10.8 and 15.0 for artificial plasma I and II, respectively.
本研究旨在开发一种非侵入性血糖检测方法。与其他方法相比,近红外(NIR)光谱和拉曼光谱这两种更有前途的技术在两种人工血浆(AP)中进行了研究。通过执行偏最小二乘(PLS)回归生成校准模型,并通过考虑光谱范围、光谱预处理方法和模型因子数量来单独优化。对两种光谱模型进行了验证,以确定葡萄糖的含量,结果表明,所建立的两种光谱模型具有稳健性、准确性和可重复性。与拉曼光谱相比,NIR 光谱的性能要好得多,其交叉验证均方根误差(RMSECV)分别为 0.128 和 0.094mg/ml,验证均方根误差(RMSEP)分别为 0.061 和 0.046mg/ml,相关系数(R)分别为 99.15%和 99.55%,人工血浆 I 和 II 的剩余预测偏差(RPD)分别为 10.8 和 15.0。