Chemical-Pharmaceutical Engineering Department, School of Applied Medical Sciences, German-Jordanian University, P.O. Box 35247 Amman 11180 Jordan; E-Mail:
Sensors (Basel). 2009;9(8):6254-60. doi: 10.3390/s90806254. Epub 2009 Aug 6.
Fourier transformation infrared (FT-IR) spectroscopy has been used to measure glucose concentrations in different matrices. The accuracy of the FT-IR technique does not meet the requirements of medical applications, so we have developed a new, efficient and precise method based on attenuated total reflectance coupled with wavelet transformation (ATR-WT-IR). One thousand interferograms, divided into training- and testing-sets, have been recorded from four glucose concentrations using an ATR-IR unit. Signals were subjected to (WT) and neural network (NN) study in order to design correlation algorithm. The Pearson's Correlation Coefficient (PCC) obtained by judging the predicted- against the real-concentrations was 0.9954, with a mean square error of 8.4e-005. The proposed ATR-WT-IR method shows efficiency in glucose prediction and could possibly to be integrated into a non-invasive monitoring technique.
傅里叶变换红外（FT-IR）光谱法已被用于测量不同基质中的葡萄糖浓度。FT-IR 技术的准确性不符合医学应用的要求，因此我们开发了一种新的、高效和精确的方法，基于衰减全反射结合小波变换（ATR-WT-IR）。使用 ATR-IR 单元从四个葡萄糖浓度记录了一千个干涉图，分为训练集和测试集。对信号进行（WT）和神经网络（NN）研究，以设计相关算法。通过判断预测浓度与真实浓度之间的 Pearson 相关系数（PCC）为 0.9954，均方误差为 8.4e-005。所提出的 ATR-WT-IR 方法在葡萄糖预测方面表现出高效性，并且可能集成到非侵入性监测技术中。
