Quantitative evaluation of glucose spectra from NIR spectroscopy measurements using PLS regression analysis

S. Hepriyadi, I. Setiadi, A. Nasution

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The quantitative evaluations were carried out in NIR spectroscopy that was implemented for monitoring and predicting the concentration of glucose samples. The collected absorbance data was preprocessed in developing PLS model before calibration using Savitzky-Golay filter. The spectrum was corrected by subtracting the offset of the regression to the absorption value and dividing this difference by the slope using Leave One Out Cross Validation (LOOCV) of the training set to determine the optimum number of PLS components. The Samples of glucose solution consist of 21 different molarity from 3000 to 5000 mg/dl with the interval of 100 mg/dl in step. Results obtained shown the linear dependency of the reference and predicted glucose concentration, with RMSECV and R2CV value are 104.92 mg/dl and 0.9728, respectively. The RMSECV shown the lowest error present and R2CV were close to one, indicates that the PLS model suited to accurately predict the variability glucose concentration.

Original languageEnglish
Title of host publicationThird International Seminar on Photonics, Optics, and Its Applications, ISPhOA 2018
EditorsAgus Muhammad Hatta, Aulia Nasution
PublisherSPIE
ISBN (Electronic)9781510627543
DOIs
Publication statusPublished - 2019
Event3rd International Seminar on Photonics, Optics, and Its Applications, ISPhOA 2018 - Surabaya, Indonesia
Duration: 1 Aug 20182 Aug 2018

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11044
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference3rd International Seminar on Photonics, Optics, and Its Applications, ISPhOA 2018
Country/TerritoryIndonesia
CitySurabaya
Period1/08/182/08/18

Keywords

  • Absorbance
  • Glucose
  • NIR Spectroscopy
  • PLS
  • Regression
  • Spectra

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