@article{Matsuo_Kawamura_Kato_Diaz_Koseki_2018, title={Practical Application of Near-Infrared Spectroscopy for Determining Rice Amylose Content at Grain Elevator}, volume={14}, url={https://azojete.com.ng/index.php/azojete/article/view/124}, abstractNote={<p>The major chemical constituent contents of rice are moisture, protein and starch (amylose and amylopectin). Those constituent contents associate with eating quality of rice. Near-infrared (NIR) spectroscopy is one of the non-destructive methods for determining grain chemical contents. At grain elevator, moisture and protein contents can be measured with high accuracy using an NIR spectrometer by the effort of our research activities in Japan. However, the accuracy to determine amylose content is not sufficient. Thus, the objective of this study was to develop non-destructive method to determine rice amylose content for practical use at grain elevator. Milled rice amylose content measurement was performed using an auto-analyzer for reference (chemical) analysis. Spectra data of milled rice were obtained using an NIR spectrometer with a wavelength range of 850 to 1048 nm. Calibration model to determine amylose content was developed using non-waxy Japonica-type rice samples. Partial least squares (PLS) regression analysis was used to develop calibration model. The accuracy of the model was validated and the validation statistics were shown: coefficient of determination (r<sup>2</sup>) was 0.72, bias was -0.04%, standard error of prediction (SEP) was 0.92%, and ratio of SEP to standard deviation of reference data (RPD) was 1.90. Production year of the validation set (2017) was different from that of the calibration set (2008 to 2016). This means the same condition as practical use of this method at grain elevator. The result obtained in this study indicated that this calibration model enables non-destructive determination of rice amylose content at grain elevator. &nbsp;</p&gt;}, number={SP.i4}, journal={ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY AND ENVIRONMENT}, author={Matsuo, M. and Kawamura, S. and Kato, M. and Diaz, E. O. and Koseki, S.}, year={2018}, month={Dec.}, pages={95-100} }