%T Eating quality evaluation of Khao Dawk Mali 105 rice using near-infrared
%T  spectroscopy
%A Pornarree Siriphollakul
%A Kazuhiro Nakano
%A Sirichai Kanlayanarat
%A Shintaroh Ohashi
%A Ryosuke Sakai
%A Ronnarit Rittiron
%A Phonkrit Maniwara
%V © 2017 Elsevier Ltd Eating quality evaluation of Khao Dawk Mali 105 rice
%V  (KDML105) based on near infrared spectroscopy (NIRS) of single kernels 
%V was developed to measure the amylose content of uncooked rice, and textu
%V re of cooked rice. The rice samples were scanned using near infrared tra
%V nsmittance spectrometry over the wavelengths of 940–2222 nm before cooki
%V ng. Calibration models of amylose content and cooked rice texture were g
%V enerated by partial least squares (PLS) regression based on first deriva
%V tive upon logarithms of transmittance. The PLS regression for amylose co
%V ntent (AC) which were expressed as coefficients of determination (R2) we
%V re 0.95 and 0.92 for calibration and prediction, respectively. Root mean
%V  square error of prediction (RMSEP) was 9.9 g/kg, dry weight. The textur
%V e of cooked rice was expressed in springiness (H1), resilience (A1), def
%V ormation (H2) and cohesiveness (A2) from low and high compression tests.
%V  The PLS prediction results (R2pre) for H1, A1, H2 and A2 were 0.61, 0.8
%V 6, 0.87 and 0.91, respectively. The RMSEP (and bias) were 0.03 (0.004), 
%V 0.01 (0.001), 0.02 (0.005) and 0.01 (0.000), correspondingly. The validi
%V ty of each calibration model was statistically evaluated. The use of NIR
%V S was feasible to predict amylose content of uncooked rice, and eating q
%V uality (texture) of cooked rice before cooking.
%K Agricultural and Biological Sciences
%V https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85009188
%V 493&origin=inward;http://cmuir.cmu.ac.th/jspui/handle/6653943832/56510
%D 2017
%V 00236438
%0 CMU e-Journals Article