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Article: Prediction of rice starch quality parameters by near-infrared reflectance spectroscopy

TitlePrediction of rice starch quality parameters by near-infrared reflectance spectroscopy
Authors
KeywordsNear-infrared reflectance spectroscopy
NIR
Rice
Starch quality
Issue Date2001
PublisherWiley-Blackwell Publishing, Inc. The Journal's web site is located at http://www.wiley.com/bw/journal.asp?ref=0022-1147
Citation
Journal Of Food Science, 2001, v. 66 n. 7, p. 936-939 How to Cite?
AbstractA rapid predictive method based on near-infrared spectroscopy (NIR), was developed to measure rice starch quality parameters. A calibration set of 100 samples and validation set of 62 samples of rice flour of Chinese genotypes was used. Results of partial least squares modeling indicated that NIR was reasonably accurate in predicting apparent amylose content (AAC) (standard error of prediction [SEP]=1.39 percentage units, coefficient of determination [R2]=0.91); pasting parameters of setback (SB) (SEP=13.6 RVU, R2=0.92), and breakdown (BD) (SEP=10.2 RVU, R2=0.88); and gelatinization peak temperature (Tp) (SEP=1.33°C, R2=0.89). Gel consistency (GC), cool paste viscosity (CPV), gelatinization onset temperature (To), and textural properties of chewiness, hardness and gumminess, were modeled less well with R2 between 0.75 and 0.86. NIR analysis is sufficiently accurate for routine screening of large numbers of samples in early generation selection in rice breeding programs.
Persistent Identifierhttp://hdl.handle.net/10722/68534
ISSN
2015 Impact Factor: 1.649
2015 SCImago Journal Rankings: 0.839
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorBao, JSen_HK
dc.contributor.authorCai, YZen_HK
dc.contributor.authorCorke, Hen_HK
dc.date.accessioned2010-09-06T06:05:28Z-
dc.date.available2010-09-06T06:05:28Z-
dc.date.issued2001en_HK
dc.identifier.citationJournal Of Food Science, 2001, v. 66 n. 7, p. 936-939en_HK
dc.identifier.issn0022-1147en_HK
dc.identifier.urihttp://hdl.handle.net/10722/68534-
dc.description.abstractA rapid predictive method based on near-infrared spectroscopy (NIR), was developed to measure rice starch quality parameters. A calibration set of 100 samples and validation set of 62 samples of rice flour of Chinese genotypes was used. Results of partial least squares modeling indicated that NIR was reasonably accurate in predicting apparent amylose content (AAC) (standard error of prediction [SEP]=1.39 percentage units, coefficient of determination [R2]=0.91); pasting parameters of setback (SB) (SEP=13.6 RVU, R2=0.92), and breakdown (BD) (SEP=10.2 RVU, R2=0.88); and gelatinization peak temperature (Tp) (SEP=1.33°C, R2=0.89). Gel consistency (GC), cool paste viscosity (CPV), gelatinization onset temperature (To), and textural properties of chewiness, hardness and gumminess, were modeled less well with R2 between 0.75 and 0.86. NIR analysis is sufficiently accurate for routine screening of large numbers of samples in early generation selection in rice breeding programs.en_HK
dc.languageengen_HK
dc.publisherWiley-Blackwell Publishing, Inc. The Journal's web site is located at http://www.wiley.com/bw/journal.asp?ref=0022-1147en_HK
dc.relation.ispartofJournal of Food Scienceen_HK
dc.rightsThe definitive version is available at www3.interscience.wiley.com-
dc.subjectNear-infrared reflectance spectroscopyen_HK
dc.subjectNIRen_HK
dc.subjectRiceen_HK
dc.subjectStarch qualityen_HK
dc.titlePrediction of rice starch quality parameters by near-infrared reflectance spectroscopyen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0022-1147&volume=66&issue=7&spage=936&epage=939&date=SEP&atitle=Prediction+of+rice+starch+quality+parameters+by+near-infrared+reflectance+spectroscopyen_HK
dc.identifier.emailCai, YZ: yzcai@hkucc.hku.hken_HK
dc.identifier.emailCorke, H: harold@hku.hken_HK
dc.identifier.authorityCai, YZ=rp00661en_HK
dc.identifier.authorityCorke, H=rp00688en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/j.1365-2621.2001.tb08215.x-
dc.identifier.scopuseid_2-s2.0-0034775450en_HK
dc.identifier.hkuros65892en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0034775450&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume66en_HK
dc.identifier.issue7en_HK
dc.identifier.spage936en_HK
dc.identifier.epage939en_HK
dc.identifier.isiWOS:000171508300005-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridBao, JS=7201398486en_HK
dc.identifier.scopusauthoridCai, YZ=8684149300en_HK
dc.identifier.scopusauthoridCorke, H=7007102942en_HK

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