Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/6587
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dc.contributorSchool of Nursing-
dc.creatorSo, CF-
dc.creatorChung, J-
dc.creatorSiu, SMM-
dc.creatorWong, TKS-
dc.date.accessioned2014-12-11T08:24:13Z-
dc.date.available2014-12-11T08:24:13Z-
dc.identifier.issn0712-4813 (print)-
dc.identifier.issn1875-922X (online)-
dc.identifier.urihttp://hdl.handle.net/10397/6587-
dc.language.isoenen_US
dc.publisherHindawi Publishing Corporationen_US
dc.rightsCopyright © 2011 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.subjectBlood glucoseen_US
dc.subjectPartial least squaresen_US
dc.subjectNear infrareden_US
dc.subjectPrediction modelen_US
dc.subjectSpectroscopyen_US
dc.titleImproved stability of blood glucose measurement in humans using near infrared spectroscopyen_US
dc.typeJournal/Magazine Articleen_US
dc.description.otherinformationAuthor name used in this publication: C. F. Soen_US
dc.description.otherinformationAuthor name used in this publication: Joanne W. Y. Chungen_US
dc.description.otherinformationAuthor name used in this publication: Maggie S. M. Siuen_US
dc.identifier.spage137-
dc.identifier.epage145-
dc.identifier.volume25-
dc.identifier.issue3-4-
dc.identifier.doi10.3233/SPE-2011-0507-
dcterms.abstractNear infrared (NIR) spectroscopy has become a promising technique for blood glucose monitoring. However, an appropriate model of spectral response in humans is yet to be determined because of the reliability problem. In this study, 48 subjects were recruited. The subjects' left forearms were scanned using near infrared spectroscopy to obtain NIR spectra. Simultaneously, a blood sample of glucose was drawn. A new method based on Monte Carlo approach is applied for partial least squares (PLS), named as PLS[sub MC], is proposed. A large numbers of models are built from calibration subsets which are randomly selected from the whole calibration set in order to minimize the noises. It is then determining the mean value over the models with high correlation and small prediction errors. The results show that the method can enhance the stability of PLS model. Also, the performance of the PLS[sub MC] shows more accurate prediction results as compared with conventional PLS.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationSpectroscopy, 2011, v. 25, no. 3-4, p. 137-145-
dcterms.isPartOfSpectroscopy-
dcterms.issued2011-
dc.identifier.isiWOS:000290959700001-
dc.identifier.scopus2-s2.0-79959232160-
dc.identifier.rosgroupidr54534-
dc.description.ros2010-2011 > Academic research: refereed > Publication in refereed journal-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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