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。
To enhance quality breeding in Perilla f rutescens ,250 lines of purple perillas collected from whole China were select-ed as material in the present study ,combined with the technology of near infrared reflectance spectroscopy (NIRS) and partial least square method ,NIRS Calibration Models for determination the content of oil .Palmitate (C16:0) ,stearic acid (C18:0) , oleic acid (C18:1) ,linoleic acid (C18:2) and a-linolenic acid (C18:3) were established ,respectively .The results showed that ,the coefficients of determination of all the models for calibration (RSQ1) were 0.98 ,0.91 ,0.92 ,0.92 ,0.85 ,0.93 ,re-spectively .In addition ,the cross validation correlation coefficient (1-VR) were 0.97 ,0.89 ,0.89 ,0.91 ,0.85 and 0.91 ,re-spectively while the external validation correlation coefficient (RSQ) were 0.98 ,0.91 ,0.89 ,0.90 ,0.80 and 0.89 ,respective-ly .All models above have proven credible as the low value for Calibration standard error (SEC) were 0.99 ,0.21 ,0.1 ,0.94 , 0.81 ,0.92 ,respectively ;Cross validation standard error (SECV) were 1.16 ,0.23 ,0.11 ,1.05 ,0.92 ,1.02 ,respectively ;and Standard error of prediction (SEP) were 0.97 ,0.21 ,0.11 ,1.12 ,0.99 ,1.14 ,respectively ,suggesting that these calibra-tion models are accurate ,feasible and highly efficient .The establishment of these NIRS Calibration Models can provide guidance in resource development and quality breeding of Perilla f rutescens L and specifically are of great significance for breeding varie-ties with high oil content .
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2017年第12期3719-3724,共6页
Spectroscopy and Spectral Analysis
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
关键词
紫苏
近红外光谱(NIRS)
脂肪酸
含油量
分析模型
Perilla
Near Infrared Reflectance Spectroscopy (NIRS)
Fatty acid
Oil content
Analytical mode