紫苏种子品质的近红外光谱分析
发布时间：2018-10-05 20:51
【摘要】：为加快紫苏优质育种进程,采用近红外光谱(NIRS)技术,结合线性偏最小二乘法(PLS),以250份全国范围内收集的紫苏资源为研究材料,分别较好的建立其种子中含油量,棕榈酸(C16∶0),硬脂酸(C18∶0),油酸(C18∶1),亚油酸(C18∶2),a-亚麻酸(C18∶3)含量的六个近红外光谱校正模型。结果显示,六个模型的校正决定系数(RSQ1)分别为:0.98,0.91,0.92,0.92,0.85,0.93;交叉验证决定系数(1-VR)分别为:0.97,0.89,0.89,0.91,0.85和0.91;外部验证相关系数(RSQ)分别为:0.98,0.91,0.89,0.90,0.80和0.89,且定标标准误差(SEC)分别为0.99,0.21,0.1,0.94,0.81,0.92;交叉验证标准误差(SECV)分别为1.16,0.23,0.11,1.05,0.92,1.02和预测标准误差(SEP)分别为0.97,0.21,0.11,1.12,0.99,1.14。结果表明,此六个校正模型质量均较高。这些首次建立的快速无损的近红外分析模型,可为紫苏资源开发提供指导,对紫苏油分品质育种具有重要意义。
[Abstract]:In order to speed up the quality breeding process of perilla, the seed oil content was established by using near infrared spectroscopy (NIRS) technique and linear partial least square method (PLS),) with 250 perilla resources collected throughout the country. Six near infrared spectral correction models for the contents of palmitic acid (C 16: 0), stearic acid (C 18: 0), oleic acid (C 18: 1), linoleic acid (C 18: 2) and linoleic acid (C 18: 3). The results show that The calibration decision coefficients (RSQ1) of the six models were: 0.980.92 / 0.920.92 / 0.92 / 0.93, respectively; the cross-validation decision coefficients (1-VR) were 0.970.89 / 0.990.85 and 0.91respectively; the external verification correlation coefficients (RSQ) were 0.9880.910.890.900.80 and 0.89respectively, and the calibration standard error (SEC) was 0.99 / 0.21 / 0.94 / 0.84 / 0.92, respectively; and the cross validation standard error (SECV) was 0.992 / 0.91 / 0.94 / 0.92, respectively, and the correlation coefficient of external verification was 0.989 / 0.991 / 0.900.80 and 0.89 respectively, and the calibration standard error (SEC) was 0.99 / 0.21 / 0.94 / 0.94 / 0.92, respectively. The (SEP) of the prediction standard error is 0.97 ~ 0.21 / 0.111.120.99 / 1.14, respectively, and the (SEP) of the prediction standard error is 0.97 ~ (0.21) ~ (0.11) ~ (1.12) ~ (1.12) ~ (1) ~ (1.14) ~ (-1), respectively. The results show that the quality of the six calibration models is high. These fast and lossless near infrared analysis models can provide guidance for the development of perilla resources and have important significance for seed oil quality breeding.
【作者单位】： 贵州省油菜研究所;贵阳市花溪区农业局;
【基金】：贵州省农业科学院专项资金项目[黔科合农科院专项(2011)017] 国家自然科学基金项目(31360067) 贵州省科技厅省农科院联合基金项目[黔科合LH字(2015)7062]资助
【分类号】：O657.33;S567.219
本文编号：2254831
[Abstract]:In order to speed up the quality breeding process of perilla, the seed oil content was established by using near infrared spectroscopy (NIRS) technique and linear partial least square method (PLS),) with 250 perilla resources collected throughout the country. Six near infrared spectral correction models for the contents of palmitic acid (C 16: 0), stearic acid (C 18: 0), oleic acid (C 18: 1), linoleic acid (C 18: 2) and linoleic acid (C 18: 3). The results show that The calibration decision coefficients (RSQ1) of the six models were: 0.980.92 / 0.920.92 / 0.92 / 0.93, respectively; the cross-validation decision coefficients (1-VR) were 0.970.89 / 0.990.85 and 0.91respectively; the external verification correlation coefficients (RSQ) were 0.9880.910.890.900.80 and 0.89respectively, and the calibration standard error (SEC) was 0.99 / 0.21 / 0.94 / 0.84 / 0.92, respectively; and the cross validation standard error (SECV) was 0.992 / 0.91 / 0.94 / 0.92, respectively, and the correlation coefficient of external verification was 0.989 / 0.991 / 0.900.80 and 0.89 respectively, and the calibration standard error (SEC) was 0.99 / 0.21 / 0.94 / 0.94 / 0.92, respectively. The (SEP) of the prediction standard error is 0.97 ~ 0.21 / 0.111.120.99 / 1.14, respectively, and the (SEP) of the prediction standard error is 0.97 ~ (0.21) ~ (0.11) ~ (1.12) ~ (1.12) ~ (1) ~ (1.14) ~ (-1), respectively. The results show that the quality of the six calibration models is high. These fast and lossless near infrared analysis models can provide guidance for the development of perilla resources and have important significance for seed oil quality breeding.
【作者单位】： 贵州省油菜研究所;贵阳市花溪区农业局;
【基金】：贵州省农业科学院专项资金项目[黔科合农科院专项(2011)017] 国家自然科学基金项目(31360067) 贵州省科技厅省农科院联合基金项目[黔科合LH字(2015)7062]资助
【分类号】：O657.33;S567.219
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