光谱学与光谱分析, 2017, 37 (12): 3719, 网络出版: 2018-01-04
紫苏种子品质的近红外光谱分析
Quality Analysis with Near Infrared Spectroscopy in Perilla Seed
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
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
To enhance quality breeding in Perilla frutescens, 250 lines of purple perillas collected from whole China were selected 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, respectively. In addition, the cross validation correlation coefficient (1-VR) were 0.97, 0.89, 0.89, 0.91, 0.85 and 0.91, respectively while the external validation correlation coefficient (RSQ) were 0.98, 0.91, 0.89, 0.90, 0.80 and 0.89, respectively. 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 calibration 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 frutescens L and specifically are of great significance for breeding varieties with high oil content.
商志伟, 赵云, 沈奇, 王仙萍, 徐静, 杨森, 田世刚, 温贺. 紫苏种子品质的近红外光谱分析[J]. 光谱学与光谱分析, 2017, 37(12): 3719. SHANG Zhi-wei, ZHAO Yun, SHEN Qi, WANG Xian-ping, XU Jing, YANG Sen, TIAN Shi-gang, WEN He. Quality Analysis with Near Infrared Spectroscopy in Perilla Seed[J]. Spectroscopy and Spectral Analysis, 2017, 37(12): 3719.