A Quantitative Analysis of Glucose from Enhanced NIR Spectra through Linear Regression Model Coupled with Optimized Bandpass Filtering

Proceedings

Islam, Tanvir Tazul¹  Mishal, Mahbubur Rahman²  Antor, Shahadat Hossain³ 

[1] Author to whom correspondence should be addressed.;Department of Electrical and Computer Engineering, North South University, Dhaka 1229, Bangladesh;Presented at the Eurosensors 2018 Conference, Graz, Austria, 9–12 September 2018

关键词: noninvasive;    glucose;    NIR;    Chebyshev filter;    baseline correction;    PLS;    PCR;   

DOI  :  10.3390/proceedings2131010

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

This study proposes a new preprocessing technique that combines Chebyshev filtering with baseline correction technique Asymmetric Least Squares (ALS) and Savitzky-Golay transformation (SGT) to improve the prediction of Glucose from near Infrared (NIR) spectra through linear regression models Partial Least Squares (PLS) and Principal Component Regression (PCR). To investigate the performance of the proposed technique, a calibration model was first developed and then validated through prediction of Glucose from NIR spectra of a mixture of glucose, urea, and triacetin in a phosphate buffer solution where the component concentrations are within their physiological range in blood. Results indicate that the proposed technique improves the performance of both PLS and PCR and achieves standard error of prediction (SEP) as low as 12.76 mg/dL which is in the clinically acceptable level and comparable to the existing literature.

【 授权许可】

CC BY