AASCIT - Journal - All Issues

ISSN Print: 2381-1072 ISSN Online: 2381-1080

Engineering and Technology

The Journal

Manuscript Information

Blood Glucose Detection from Infrared Spectral Data by Gaussian Single-Peak Fitting

Engineering and Technology
Vol.4 , No. 4, Publication Date: Aug. 25, 2017, Page: 43-47

396 Views Since August 25, 2017, 739 Downloads Since Aug. 25, 2017

Paper in PDF (342K)

Follow on

Authors

[1]	Li Gun, Department of Biomedical Engineering, School of Electronic Information Engineering, Xi’an Technological University, Xi’an, China.
[2]	Feng Qiaomei, Department of Biomedical Engineering, School of Electronic Information Engineering, Xi’an Technological University, Xi’an, China.
[3]	Guo Rui, Department of Biomedical Engineering, School of Electronic Information Engineering, Xi’an Technological University, Xi’an, China.
[4]	Xu Fei, Department of Biomedical Engineering, School of Electronic Information Engineering, Xi’an Technological University, Xi’an, China.
[5]	Zhou Kaikai, Department of Biomedical Engineering, School of Electronic Information Engineering, Xi’an Technological University, Xi’an, China.

Abstract

Infrared spectra contain abundant information on the structure of the material. It requires fewer samples for detecting the composition of matter. The characteristics of infrared spectroscopy technology enhance its wide use in biomedical, materials, food testing and criminal investigation science, etc. This paper presents a method for analyzing infrared spectra data based on Gaussian single-peak fitting. Firstly, recognition and peak shape extraction algorithm of Fourier transform infrared spectra for Gaussian fitting is proposed. Secondly, the method is applied to extract specific infrared spectra data from the control group and the experimental group in order to obtain the sample data with different content of blood glucose. Finally, the Gaussian single-peak fitting method was applied to analyze the infrared spectra data. By comparing its results with the second derivative spectra of infrared absorption spectra, the results show that the Gaussian single-peak fitting is not only effective for detecting the glucose in blood, but also very important for processing the Infrared spectroscopic data, which can provide further application of infrared spectroscopy detection technology.

Keywords

Fourier Transform Infrared Spectroscopy (FTIR), Data Fitting, Glucose Detection, Biochemical Detection

Reference

[01]	Feng J, Wu Q, Zhou Y, et al. Interaction between flavin mononucleotide-containing azoreductase and azo dyes. Spectroscopy Letters, 2016, 49 (10): 626-632.
[02]	Joanna D, Magdalena S, Gabriel N, et al. Phospholipid-protein balance in affective disorders: Analysis of human blood serum using Raman and FTIR spectroscopy. A pilot study. Journal of Pharmaceutical and Biomedical Analysis, 2016, 131: 287-296.
[03]	Hui L, Qinglong S, Daping S, et al. Comparison of red blood cells from gastric cancer patients and healthy persons using FTIR spectroscopy. Journal of Molecular Structure, 2017, 1130, 33-37.
[04]	Furong T, Joao C, Chenchen B, et al. Gold nanostars for efficient in vitro and in vivo real-time SERS detection and drug delivery via plasmonic-tunable Raman/FTIR imaging. Biomaterials, 2016, 106: 87.
[05]	Mohammdreza K, Keyvan G, Amir B G, et al. Diagnostic prediction of renal failure from blood serum analysis by FTIR spectrometry and chemometrics. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2015, 136: 1782-1875.
[06]	Shiddappa M S, Muniswamy D. Fourier transform infrared (FT-IR) study on cyanide induced biochemical and structural changes in rat sperm. Toxicology Reports, 2015, 2: 1347-1356.
[07]	Shiek S S J A, Winkins S, Suresh K, et al. Neural network algorithm for the early detection of Parkinson’s disease from blood plasma by FTIR micro-spectroscopy. Vibrational Spectroscopy, 2010, 53: 181-188.
[08]	Miguel P, Hermann L. T, Werner M. Infrared spectroscopic analysis of human interstitial fluid in vitro and in vivo using FT-IR spectroscopy and pulsed quantum cascade lasers (QCL): Establishing a new approach to non invasive glucose measurement. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2012, 85: 61-65.
[09]	Nicldessa M E, Holroydb E S, Hamiltona G. Analytical method development using FTIR-ATR and FT-Raman spectroscopy to assay fructose, sucrose, glucose and dihydroxyacetone, in Leptospermum scoparium nectar. Vibrational Spectroscopy, 2016, 84: 38-43.
[10]	Sherif S M. The Impact of Elevated Blood Glycemic Level of Patients with Type 2 Diabetes Mellitus on the Erythrocyte Membrane: FTIR Study. Cell Biochemistry and Biophysics, 2010, 58 (1): 45-51.
[11]	Shen Y C, Davies A G, Linfield E H. Determination of Glucose Concentration in Whole Blood using Fourier-Transform Infrared Spectroscopy. Journal of Biological Physics, 2003, 29 (2): 129-133.
[12]	Hermann L-T, Michael W, Arian X, et al. A novel approach to non-invasive glucose measurement by mid-infrared spectroscopy: The combination of quantum cascade lasers (QCL) and photoacoustic detection. Vibrational Spectroscopy, 2005, 38 (1-2): 209-215.