Cheng Weimin, Xu Zhuopin, Fan Shuang, Zhang Pengfei, Xia Jiafa, Wang Hui, Ye Yafeng, Liu Binmei, Wang Qi, Wu Yuejin
Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.
Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China.
Foods. 2022 Aug 30;11(17):2634. doi: 10.3390/foods11172634.
The chemical composition of individual hybrid rice (F2) varieties varies owing to genetic differences between parental lines, and the effects of these differences on eating quality are unclear. In this study, based on a self-developed near-infrared spectroscopy platform, we explored these effects among a set of 143 hybrid indica rice varieties with different eating qualities. The single-grain amylose content (SGAC) and single-grain protein content (SGPC) models were established with coefficients of determination (R) of 0.9064 and 0.8847, respectively, and the dispersion indicators (standard deviation, variance, extreme deviation, quartile deviation, and coefficient of variation) were proposed to analyze the variations in the SGAC and SGPC based on the predicted results. Our correlation analysis found that the higher the variation in the SGAC and SGPC, the lower the eating quality of the hybrid indica rice. Moreover, the addition of the dispersion indicators of the SGAC and SGPC improved the R of the eating quality model constructed using the composition-related physicochemical indicators (amylose content, protein content, alkali-spreading value, and gel consistency) from 0.657 to 0.850. Therefore, this new method proved to be useful for identifying high-eating-quality hybrid indica rice based on single near-infrared spectroscopy prior to processing and cooking.
由于亲本系之间的遗传差异，各个杂交水稻（F2）品种的化学成分有所不同，而这些差异对食味品质的影响尚不清楚。在本研究中，基于自主研发的近红外光谱平台，我们在一组143个具有不同食味品质的杂交籼稻品种中探究了这些影响。建立了单粒直链淀粉含量（SGAC）和单粒蛋白质含量（SGPC）模型，其决定系数（R）分别为0.9064和0.8847，并基于预测结果提出了离散指标（标准差、方差、极差、四分位数偏差和变异系数）来分析SGAC和SGPC的变化。我们的相关性分析发现，SGAC和SGPC的变化越高，杂交籼稻的食味品质越低。此外，添加SGAC和SGPC的离散指标后，使用与成分相关的理化指标（直链淀粉含量、蛋白质含量、碱消值和胶稠度）构建的食味品质模型的R值从0.657提高到了0.850。因此，这种新方法被证明对于在加工和烹饪前基于单粒近红外光谱识别高食味品质的杂交籼稻很有用。
