Bagchi Torit Baran, Sharma Srigopal, Chattopadhyay Krishnendu
Div. of Crop Physiology and Biochemistry, Central Rice Research Institute, P.O. Box 753006, Cuttack, Odisha, India.
Div. of Crop Physiology and Biochemistry, Central Rice Research Institute, P.O. Box 753006, Cuttack, Odisha, India.
Food Chem. 2016 Jan 15;191:21-7. doi: 10.1016/j.foodchem.2015.05.038. Epub 2015 May 11.
With the escalating persuasion of economic and nutritional importance of rice grain protein and nutritional components of rice bran (RB), NIRS can be an effective tool for high throughput screening in rice breeding programme. Optimization of NIRS is prerequisite for accurate prediction of grain quality parameters. In the present study, 173 brown rice (BR) and 86 RB samples with a wide range of values were used to compare the calibration models generated by different chemometrics for grain protein (GPC) and amylose content (AC) of BR and proximate compositions (protein, crude oil, moisture, ash and fiber content) of RB. Various modified partial least square (mPLSs) models corresponding with the best mathematical treatments were identified for all components. Another set of 29 genotypes derived from the breeding programme were employed for the external validation of these calibration models. High accuracy of all these calibration and prediction models was ensured through pair t-test and correlation regression analysis between reference and predicted values.
随着稻米籽粒蛋白质和米糠（RB）营养成分在经济和营养方面重要性的不断提升，近红外光谱（NIRS）可成为水稻育种计划中高通量筛选的有效工具。NIRS的优化是准确预测谷物品质参数的前提条件。在本研究中，使用了173份糙米（BR）样品和86份具有广泛数值范围的RB样品，来比较不同化学计量学方法针对BR的籽粒蛋白质（GPC）、直链淀粉含量（AC）以及RB的近似组成（蛋白质、原油、水分、灰分和纤维含量）所生成的校准模型。针对所有成分，确定了与最佳数学处理方法相对应的各种改进偏最小二乘（mPLSs）模型。从育种计划中选取的另一组29个基因型用于对这些校准模型进行外部验证。通过参考值与预测值之间的配对t检验和相关回归分析，确保了所有这些校准和预测模型的高精度。
