首页> 外文期刊>农业科学学报(英文版) >Comparison of Near Infrared Spectroscopy Models for Determining Protein and Amylose Contents Between Calibration Samples of Recombinant Inbred Lines and Conventional Varieties of Rice
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Comparison of Near Infrared Spectroscopy Models for Determining Protein and Amylose Contents Between Calibration Samples of Recombinant Inbred Lines and Conventional Varieties of Rice

机译:水稻自交系与常规品种校准标本间近红外光谱模型测定蛋白质和直链淀粉含量的比较。

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摘要

The near infrared spectra of 178 recombinant inbred lines (RILs) from the cross of Ⅱ-32B/Yuezaoxian 6 (YZX6) and 511 varieties in rice were acquired. A total of 80 RILs and 96 cultivars were selected as modeling samples by comparing the spectra similarity primarily. Three partial least square (PLS) regression models were developed, based on the RILs (RIL-model), the varieties (Var-model) and their mixture (Mix-model), for protein content (PC) and amylose content (AC),respectively. Cross validation and outer prediction showed that the models were largely influenced by the range and distribution of modeling samples. The regression model of PC based on the cultivars and the model of AC based on RILs had higher coefficient of determination (r2 ≥ 0.9) and lower root mean square error of cross validation (RMSECVs). The disadvantages of RIL samples for PC model and variety samples for AC model were probably caused by the narrow range of variance. Aberrant predictions were obtained for outer sample with PC or AC outside the range or within the distribution gap of modeling samples. The Mix-models gave more reliable prediction as the distribution of RIL and variety modeling samples were complementary to each other.
机译:获得了水稻Ⅱ-32B/月枣鲜6号(YZX6)与511个品种杂交后的178个重组自交系(RILs)的近红外光谱。通过主要比较光谱相似性,总共选择了80个RIL和96个品种作为模型样品。基于RIL(RIL模型),品种(Var模型)及其混合物(Mix模型),针对蛋白质含量(PC)和直链淀粉含量(AC),开发了三个偏最小二乘(PLS)回归模型,分别。交叉验证和外部预测表明,模型在很大程度上受到建模样本的范围和分布的影响。基于品种的PC回归模型和基于RIL的AC回归模型具有较高的测定系数(r2≥0.9)和较低的交叉验证均方根误差(RMSECVs)。 PC模型的RIL样本和AC模型的各种样本的缺点可能是由于方差范围狭窄所致。对于外部样品,如果PC或AC超出模型样品的范围或分布范围,则获得了异常的预测。由于RIL的分布和品种建模样本相互补充,因此Mix-models提供了更可靠的预测。

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