Title :
Determination of glucose concentration from near infrared spectra using least square support vector machine
Author :
Malik, Bilal Ahmad
Author_Institution :
Sci. & Instrum. Centre, Univ. of Kashmir, Srinagar, India
Abstract :
One of the many challenges for translating noninvasive glucose measurement into clinical practice is the calibration of the measuring instrument. In this work, least squares support vector regression (LS-SVR) has been used to develop a multivariate calibration model for determination of glucose concentration from near infra-red (NIR) spectra. The behaviour of developed model is studied on NIR spectra of a mixture composed of glucose, urea, and triacetin which spans from 2100 nm to 2400 nm with a spectral resolution of 1nm. The proposed model improved the standard error of prediction (SEP) from 49.4 mg/dL in case of Principal Component Regression (PCR) and 27.5 mg/dL in case of Principal Least Squares Regression (PLSR) to 19.4mg/dL.
Keywords :
biochemistry; calibration; chemical variables measurement; infrared spectra; least squares approximations; medical computing; principal component analysis; regression analysis; sugar; support vector machines; LS-SVR; NIR spectra; PCR; PLSR; SEP; clinical practice; glucose concentration; least square support vector machine; least squares support vector regression; mixture; multivariate calibration; near infrared spectra; noninvasive glucose measurement; principal component regression; principal least squares regression; spectral resolution; standard error-of-prediction; Calibration; Diabetes; Kernel; Predictive models; Spectroscopy; Sugar; Support vector machines; Calibration; LS-SVM; Machine Learning; NIR; Non-invasive glucose measurement; SEC; SEP;
Conference_Titel :
Industrial Instrumentation and Control (ICIC), 2015 International Conference on
Conference_Location :
Pune
DOI :
10.1109/IIC.2015.7150789
