{"id":{"repo_id":"uthm","oai_identifier":"oai:eprints.uthm.edu.my:430"},"canonical_url":"https://search.dev.ndltd.org/etd/uthm/oai:eprints.uthm.edu.my:430","repository":{"repo_id":"uthm","name":"Universiti Tun Hussein Onn Malaysia","base_url":"http://eprints.uthm.edu.my/cgi/oai2"},"display":{"title":"Predictive models in near-infrared spectroscopic analysis for blood hemoglobin prediction","abstract":"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 (","abstract_html":"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 (","abstract_has_math":false,"creators":["Mohd Idrus, Mohd Nazrul Effendy"],"institution":"Universiti Tun Hussein Onn Malaysia","degree_name":"mphil","degree_level":"masters","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-11","date_published":"2018-11","updated_at":"2026-07-24T05:47:54Z","subjects":["QD241-441 Organic chemistry"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Mohd Idrus, Mohd Nazrul Effendy"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-11"]},{"key":"dc:date.issued","label":"Date","values":["2018-11"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Faculty of Electrical and Electronic Engineering"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["Universiti Tun Hussein Onn Malaysia"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["http://eprints.uthm.edu.my/430/"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["masters"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["mphil"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["QD241-441 Organic chemistry"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["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"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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 ("]},{"key":"dc:format","label":"Dc Format","values":["text"]},{"key":"dc:title","label":"Title","values":["Predictive models in near-infrared spectroscopic analysis for blood hemoglobin prediction"]}]}],"canonical_facts":{"dc:creator":["Mohd Idrus, Mohd Nazrul Effendy"],"dc:date":["2018-11"],"dc:date.issued":["2018-11"],"dc:description.abstract":["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 ("],"dc:format":["text"],"dc:identifier.uri":["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"],"dc:language":["en"],"dc:publisher.department":["Faculty of Electrical and Electronic Engineering"],"dc:publisher.institution":["Universiti Tun Hussein Onn Malaysia"],"dc:relation.isreferencedby":["http://eprints.uthm.edu.my/430/"],"dc:subject":["QD241-441 Organic chemistry"],"dc:title":["Predictive models in near-infrared spectroscopic analysis for blood hemoglobin prediction"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["masters"],"dc:type.qualificationname":["mphil"]},"updated_at":"2026-07-24T05:47:54Z"}