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

Mahbubur Rahman Mishal,Tanzilur Rahman,Shahadat Hossain Antor,Tanvir Tazul Islam

doi:10.3390/proceedings2131010

Abstract

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.

Highlights

Diabetes mellitus is a chronic disease that is associated with the abnormal metabolism of glucose.Regular monitoring of blood glucose level is necessary for those suffering from Diabetes.Conventionally, this monitoring is done by drawing blood several times per day and administer insulin manually
Two different multivariate regression analysis, Partial Least Squares (PLS), and Principal Component Regression (PCR) were applied on the preprocessed spectra
For removing issues such baseline variation from the spectra, four different data correction methods were applied on spectra before feeding them to PLS and PCR

Summary

Introduction

Diabetes mellitus is a chronic disease that is associated with the abnormal metabolism of glucose. Regular monitoring of blood glucose level is necessary for those suffering from Diabetes. This monitoring is done by drawing blood several times per day and administer insulin manually. Existing methods involve collecting blood sample by breaking skin which is often painful and uncomfortable for patients. NIR spectroscopy has been identified as the most popular technique among many others for noninvasive glucose monitoring [1,2,3,4]. Spectra that is collected by the NIR instrument can contain unwanted frequency component or noise. Filtering method can remove those noises superimposed on high frequency signals [2,3,4,5].

Preparation of the Data

Preprocessing

Regression

Data Correction and Filtering

Regression on the preprocessed data