Predictive models in near-infrared spectroscopic analysis for blood hemoglobin prediction

Determination of blood hemoglobin concentration is important to diagnose anaemia. Common clinical practice used to measure blood hemoglobin is by drawn some blood from the patient to be mixed with reagent chemicals for analysis. Alternatively, near-infrared spectroscopy (NIRS) technology can be used...

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Main Author:	Mohd Idrus, Mohd Nazrul Effendy
Format:	Thesis
Language:	English English English
Published:	2018
Subjects:	QD241-441 Organic chemistry
Online Access:	http://eprints.uthm.edu.my/430/1/24p%20MOHD%20NAZRUL%20EFFENDY%20MOHD%20IDRUS.pdf http://eprints.uthm.edu.my/430/2/MOHD%20NAZRUL%20EFFENDY%20MOHD%20IDRUS%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/430/3/MOHD%20NAZRUL%20EFFENDY%20MOHD%20IDRUS%20WATERMARK.pdf
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spelling	my-uthm-ep.4302021-06-22T01:26:13Z Predictive models in near-infrared spectroscopic analysis for blood hemoglobin prediction 2018-11 Mohd Idrus, Mohd Nazrul Effendy QD241-441 Organic chemistry Determination of blood hemoglobin concentration is important to diagnose anaemia. Common clinical practice used to measure blood hemoglobin is by drawn some blood from the patient to be mixed with reagent chemicals for analysis. Alternatively, near-infrared spectroscopy (NIRS) technology can be used to measure blood hemoglobin level. NIRS is based on molecular overtone and combination vibrations produce absorption bands typically very broad, leading to complex spectral data makes the NIRS useful for analysis. Different types of predictive models such as linear, nonlinear, and hybrid predictive models were commonly used to predict component of interest from NIRS spectral data. However, different predictive model approached may achieve different accuracy of performance in predicting component of interest from NIRS spectral data. The aims of this study is to investigate the accuracy of linear partial least square (PLS), nonlinear artificial neural network (ANN), and hybrid partial least square - artificial neural network (PLS-ANN) predictive modelling in NIRS analysis. These predictive models were coupled with Savitzky-Golay (SG) preprocessing to remove unwanted signal from spectral data. The optimal numbers of frame length, latent variables, and hidden neurons used in SG preprocessing and predictive models were investigated. Results show ANN coupled with first order SG derivatives achieved the best prediction of performance with root mean square error of prediction (RMSEP) and the coefficient of determination of prediction ( 2018-11 Thesis http://eprints.uthm.edu.my/430/ http://eprints.uthm.edu.my/430/1/24p%20MOHD%20NAZRUL%20EFFENDY%20MOHD%20IDRUS.pdf text en public http://eprints.uthm.edu.my/430/2/MOHD%20NAZRUL%20EFFENDY%20MOHD%20IDRUS%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/430/3/MOHD%20NAZRUL%20EFFENDY%20MOHD%20IDRUS%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QD241-441 Organic chemistry
spellingShingle	QD241-441 Organic chemistry Mohd Idrus, Mohd Nazrul Effendy Predictive models in near-infrared spectroscopic analysis for blood hemoglobin prediction
description	Determination of blood hemoglobin concentration is important to diagnose anaemia. Common clinical practice used to measure blood hemoglobin is by drawn some blood from the patient to be mixed with reagent chemicals for analysis. Alternatively, near-infrared spectroscopy (NIRS) technology can be used to measure blood hemoglobin level. NIRS is based on molecular overtone and combination vibrations produce absorption bands typically very broad, leading to complex spectral data makes the NIRS useful for analysis. Different types of predictive models such as linear, nonlinear, and hybrid predictive models were commonly used to predict component of interest from NIRS spectral data. However, different predictive model approached may achieve different accuracy of performance in predicting component of interest from NIRS spectral data. The aims of this study is to investigate the accuracy of linear partial least square (PLS), nonlinear artificial neural network (ANN), and hybrid partial least square - artificial neural network (PLS-ANN) predictive modelling in NIRS analysis. These predictive models were coupled with Savitzky-Golay (SG) preprocessing to remove unwanted signal from spectral data. The optimal numbers of frame length, latent variables, and hidden neurons used in SG preprocessing and predictive models were investigated. Results show ANN coupled with first order SG derivatives achieved the best prediction of performance with root mean square error of prediction (RMSEP) and the coefficient of determination of prediction (
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Mohd Idrus, Mohd Nazrul Effendy
author_facet	Mohd Idrus, Mohd Nazrul Effendy
author_sort	Mohd Idrus, Mohd Nazrul Effendy
title	Predictive models in near-infrared spectroscopic analysis for blood hemoglobin prediction
title_short	Predictive models in near-infrared spectroscopic analysis for blood hemoglobin prediction
title_full	Predictive models in near-infrared spectroscopic analysis for blood hemoglobin prediction
title_fullStr	Predictive models in near-infrared spectroscopic analysis for blood hemoglobin prediction
title_full_unstemmed	Predictive models in near-infrared spectroscopic analysis for blood hemoglobin prediction
title_sort	predictive models in near-infrared spectroscopic analysis for blood hemoglobin prediction
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Electrical and Electronic Engineering
publishDate	2018
url	http://eprints.uthm.edu.my/430/1/24p%20MOHD%20NAZRUL%20EFFENDY%20MOHD%20IDRUS.pdf http://eprints.uthm.edu.my/430/2/MOHD%20NAZRUL%20EFFENDY%20MOHD%20IDRUS%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/430/3/MOHD%20NAZRUL%20EFFENDY%20MOHD%20IDRUS%20WATERMARK.pdf
_version_	1747830608167436288

Predictive models in near-infrared spectroscopic analysis for blood hemoglobin prediction

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