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dc.contributor.authorSakunna Wongsaipunen_US
dc.contributor.authorChanida Krongchaien_US
dc.contributor.authorJaroon Jakmuneeen_US
dc.contributor.authorSila Kittiwachanaen_US
dc.date.accessioned2018-09-05T04:20:09Z-
dc.date.available2018-09-05T04:20:09Z-
dc.date.issued2018-02-01en_US
dc.identifier.issn1936976Xen_US
dc.identifier.issn19369751en_US
dc.identifier.other2-s2.0-85029156476en_US
dc.identifier.other10.1007/s12161-017-1031-yen_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85029156476&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/58118-
dc.description.abstract© 2017, Springer Science+Business Media, LLC. This research developed a rapid and accurate method based on the use of rapid visco analyzer (RVA) for predicting the storage time of rice grain. Freshly harvested rice samples, five waxy and five non-waxy rice grains, were stored in paddy form at ambient room temperature (28–32 °C) for 1 year. During storage, the RVA profiles of the rice samples were recorded every month. In addition, physicochemical properties, such as alkali spreading value (ASV), amylose content, gel consistency, stickiness, and hardness, were measured. Chemometric models including partial least squares (PLS) regression and supervised self-organizing map (supervised SOM) were employed for predicting the storage time based on the use of the RVA profiles, the physicochemical parameters, and both of the datasets simultaneously. In most cases, PLS outperformed supervised SOM. The PLS models established using the RVA profiles provided the best predictive results with root mean square error of cross validation (RMSECV) = 1.2, cross-validated explained variance (Q2) = 0.90, and the ratio of prediction to deviation (RPD) = 3.2. Based on partial least squares-variable influence on projection (PLS-VIP), pasting properties, including peak viscosity (PV) and final viscosity (FV), were identified as the parameters having strong influence on the prediction models. The developed method detecting the rheological change of the stored rice samples was simple and could be performed quickly with no additional chemicals required.en_US
dc.subjectAgricultural and Biological Sciencesen_US
dc.subjectChemistryen_US
dc.subjectEngineeringen_US
dc.subjectImmunology and Microbiologyen_US
dc.subjectSocial Sciencesen_US
dc.titleRice Grain Freshness Measurement Using Rapid Visco Analyzer and Chemometricsen_US
dc.typeJournalen_US
article.title.sourcetitleFood Analytical Methodsen_US
article.volume11en_US
article.stream.affiliationsChiang Mai Universityen_US
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