Dual Wavelength based Approach with Partial Least Square Regression for the Prediction of Glucose Concentration

Deepshikha Yadava, Manjri Singha, Sahil Sharmaa, Surinder P Singha, P. Dubeya
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引用次数: 1

Abstract

Diabetes mellitus is a group of metabolic disorder characterized by high blood sugar levels. Monitoring of blood glucose levels at regular intervals plays a crucial role in the management of diabetes. The non-invasive real-time monitoring of glucose using near-infrared (NIR), Raman, acoustic and bio-impedance techniques have an edge over available invasive techniques but suffers from low Signal to Noise ratio (S/N) and other interferences. In the present work, we have attempted to improve S/N for the efficient detection of feeble signals corresponding to the physiological glucose concentrations. Investigations were carried out in the NIR region particularly from 800-1400 nm for the identification of the unique absorption features of glucose using UV-Vis NIR spectrophotometer with different ranges of glucose concentrations including 5 g/dl- 45 g/dl, 1400 mg/dl -2500 mg/dl, 35 mg/dl-650 mg/dl . Savitzky Golay (SG) pre-processing filter was applied on the raw data for enhancing the S/N for better prediction of glucose concentrations. The absorption spectra of glucose revealed the presence of a peak at 960 nm. Therefore, considering the absorbance at 960 nm, provided an enhancement in the S/N ratio from 17 dB to 27 dB. Further, partial least square regression (PLSR), has been applied on SG filtered data for a better prediction of glucose concentration with a correlation coefficient ( R 2 ) value of 0.99 and root mean square error of prediction (RMSE) of 2.29 mg/dl. Further, based on the NIR spectral data, we have developed a measurement technique using two LED sources of 950 nm and 860 nm, and a wide detector (700 - 1100 nm) which converts obtained optical signal into voltage. It has been observed that by considering dual wavelength detection points the prediction of glucose concentration is improved. Furthermore, with increase in the test glucose concentrations, the voltage signal decreased corresponding to the 950 nm LED. This is attributed to reduced signal intensities reaching the photodiode as a result of the increase in glucose absorption. Incorporating dual wavelengths for PLSR reduced the RMSE from 8.98 mg/dl to 6.49 mg/dl and also improved the R 2 value from 0.97 to 0.99.
基于双波长的偏最小二乘回归法预测葡萄糖浓度
糖尿病是一组以高血糖为特征的代谢紊乱。定期监测血糖水平在糖尿病的治疗中起着至关重要的作用。使用近红外(NIR)、拉曼、声学和生物阻抗技术的无创血糖实时监测比现有的有创技术有优势,但存在低信噪比(S/N)和其他干扰。在目前的工作中,我们试图提高S/N,以有效检测与生理葡萄糖浓度相对应的微弱信号。在葡萄糖浓度范围为5g /dl ~ 45g /dl、1400mg /dl ~ 2500mg /dl、35mg /dl ~ 650mg /dl的情况下,在800 ~ 1400nm的近红外光谱范围内对葡萄糖的独特吸收特性进行了研究。对原始数据进行Savitzky Golay (SG)预处理滤波,提高S/N,更好地预测葡萄糖浓度。葡萄糖的吸收光谱在960 nm处有一个峰。因此,考虑到960 nm处的吸光度,提供了从17 dB到27 dB的信噪比增强。此外,将偏最小二乘回归(PLSR)应用于SG过滤的数据,可以更好地预测葡萄糖浓度,相关系数(r2)为0.99,预测均方根误差(RMSE)为2.29 mg/dl。此外,基于近红外光谱数据,我们开发了一种测量技术,使用两个950 nm和860 nm的LED光源,以及一个宽检测器(700 - 1100 nm),将获得的光信号转换为电压。观察到,考虑双波长检测点可以改善葡萄糖浓度的预测。此外,随着测试葡萄糖浓度的增加,950 nm LED对应的电压信号减小。这是由于葡萄糖吸收增加导致到达光电二极管的信号强度降低。采用双波长的PLSR将RMSE从8.98 mg/dl降低到6.49 mg/dl,并将r2值从0.97提高到0.99。
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