Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Agriculture / Faculty of Agriculture >
Peer-reviewed Journal Articles, etc >
Combined analysis of near-infrared spectra, colour, and physicochemical information of brown rice to develop accurate calibration models for determining amylose content
This item is licensed under:Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Title: | Combined analysis of near-infrared spectra, colour, and physicochemical information of brown rice to develop accurate calibration models for determining amylose content |
Authors: | Diaz, Edenio Olivares Browse this author | Kawamura, Shuso Browse this author →KAKEN DB | Matsuo, Miki Browse this author | Kato, Mizuki Browse this author | Koseki, Shigenobu Browse this author →KAKEN DB |
Keywords: | Rice quality | Amylose content | Near-infrared spectroscopy | Calibration model accuracy | Chemometric techniques |
Issue Date: | 15-Jul-2019 |
Publisher: | Elsevier |
Journal Title: | Food Chemistry |
Volume: | 286 |
Start Page: | 297 |
End Page: | 306 |
Publisher DOI: | 10.1016/j.foodchem.2019.02.005 |
PMID: | 30827610 |
Abstract: | Amylose content is an important determinant of rice quality. Accurate non-destructive determination of amylose content remains a primary challenge for the rice industry. Here, we analysed the accuracy of three models for the non-destructive determination of amylose content. The models were developed by combining near-infrared spectra, colour, and physicochemical information relative to 832 brown rice samples from ten varieties produced between 2009 and 2017 in various regions of Hokkaido, Japan. Models describing low and ordinary amylose varieties were developed individually, merged, and validated using production year samples (2016-2017) different from the calibration set (2009-2015). The resulting accuracy was suitable for industrial application. With standard error of prediction = 0.70% and ratio of performance deviation = 3.56, the combination of near-infrared spectra and physicochemical information produced the most robust model, enabling more precise rice quality screening at grain elevators. |
Rights: | © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ | https://creativecommons.org/licenses/by-nc-nd/4.0/ |
Type: | article (author version) |
URI: | http://hdl.handle.net/2115/78880 |
Appears in Collections: | 農学院・農学研究院 (Graduate School of Agriculture / Faculty of Agriculture) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
|
Submitter: Edenio Olivares Díaz
|