Noninvasive and fast measurement of blood glucose in vivo by near infrared (NIR) spectroscopy.

This research was to develop a method for noninvasive and fast blood glucose assay in vivo. Near-infrared (NIR) spectroscopy, a more promising technique compared to other methods, was investigated in rats with diabetes and normal rats. Calibration models are generated by two different multivariate strategies: partial least squares (PLS) as linear regression method and artificial neural networks (ANN) as non-linear regression method. The PLS model was optimized individually by considering spectral range, spectral pretreatment methods and number of model factors, while the ANN model was studied individually by selecting spectral pretreatment methods, parameters of network topology, number of hidden neurons, and times of epoch. The results of the validation showed the two models were robust, accurate and repeatable. Compared to the ANN model, the performance of the PLS model was much better, with lower root mean square error of validation (RMSEP) of 0.419 and higher correlation coefficients (R) of 96.22%.

[1]  Ercan Senyigit,et al.  Artificial neural network models for lot-sizing problem: a case study , 2012, Neural Computing and Applications.

[2]  F. Lu,et al.  Jiaotai pill (交泰丸) enhances insulin signaling through phosphatidylinositol 3-kinase pathway in skeletal muscle of diabetic rats , 2013, Chinese Journal of Integrative Medicine.

[3]  Xiaoting Li,et al.  A feasibility study on quantitative analysis of glucose and fructose in lotus root powder by FT-NIR spectroscopy and chemometrics. , 2012, Food chemistry.

[4]  R. Paramesran,et al.  A two-level partial least squares system for non-invasive blood glucose concentration prediction , 2010 .

[5]  Chengjun Song,et al.  Sericin protects against diabetes-induced injuries in sciatic nerve and related nerve cells , 2013, Neural regeneration research.

[6]  J. Dushoff,et al.  Modelling of OGTT curve identifies 1 h plasma glucose level as a strong predictor of incident type 2 diabetes: results from two prospective cohorts , 2014, Diabetologia.

[7]  R S Parker,et al.  The intravenous route to blood glucose control. , 2001, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[8]  Chunjie Wu,et al.  Dynamic prediction models for alkaloid content using NIR technology for the study and online analysis of parching in Areca Seed , 2011 .

[9]  C. Huck,et al.  Simultaneous determination of the micro-, meso-, and macropore size fractions of porous polymers by a combined use of Fourier transform near-infrared diffuse reflection spectroscopy and multivariate techniques. , 2008, Analytical chemistry.

[10]  Névine Rizkalla,et al.  Artificial Neural Networks: Comparison of Two Programs for Modeling a Process of Nanoparticle Preparation , 2005, Drug development and industrial pharmacy.

[11]  Jingwei Shao,et al.  In Vivo Blood Glucose Quantification Using Raman Spectroscopy , 2012, PloS one.

[12]  Asoke K. Nandi,et al.  Feature generation using genetic programming with comparative partner selection for diabetes classification , 2013, Expert Syst. Appl..

[13]  L. Ye,et al.  Noninvasive Measurement of Glucose in Artificial Plasma with Near-Infrared and Raman Spectroscopy , 2014, Applied spectroscopy.

[14]  M. Pacheco,et al.  Differential diagnosis between experimental endophthalmitis and uveitis in vitreous with Raman spectroscopy and principal components analysis. , 2012, Journal of photochemistry and photobiology. B, Biology.

[15]  Hoeil Chung,et al.  Wide area coverage Raman spectroscopy for reliable quantitative analysis and its applications. , 2013, The Analyst.

[16]  David I. Ellis,et al.  Illuminating disease and enlightening biomedicine: Raman spectroscopy as a diagnostic tool. , 2013, The Analyst.

[17]  A. Maran,et al.  Non-invasive glucose monitoring: assessment of technologies and devices according to quantitative criteria. , 2007, Diabetes research and clinical practice.

[18]  S. K. Vashist.  Non-invasive glucose monitoring technology in diabetes management: a review. , 2012, Analytica chimica acta.

[19]  J. Shaw,et al.  Global and societal implications of the diabetes epidemic , 2001, Nature.

[20]  Jiang He,et al.  Prevalence and control of diabetes in Chinese adults. , 2013, JAMA.

[21]  J. Ilonen,et al.  OGTT and random plasma glucose in the prediction of type 1 diabetes and time to diagnosis , 2015, Diabetologia.

[22]  Kevin Dhaliwal,et al.  Surface-enhanced Raman scattering in cancer detection and imaging. , 2013, Trends in biotechnology.

[23]  Ali Mirsepahi,et al.  A comparative approach of inverse modelling applied to an irradiative batch dryer employing several artificial neural networks , 2014 .