【作者】 荣正勤；

【导师】 吴建国；

【作者基本信息】 浙江大学 ， 作物遗传育种， 2011， 硕士

【摘要】 近红外光谱技术是上世纪80年代后期迅速发展起来的新型物理测试技术,其具有速度快、无污染、低消耗、非破坏性、多组分同时测定等优点,已被列为多种国际或行业标准,在许多领域得到广泛应用。甜菊二萜糖甙是一类高甜度、无能量、天然甜味剂,在食品、医药、化妆品等领域有广阔的前景。本研究旨在建立甜叶菊叶片中重要二萜糖甙组分的近红外光谱预测模型,并通过改进的算法,优化模型的稳健性,提高模型的适用性和预测能力,为甜叶菊味质检测和育种选择提供快速、简捷、有效地方法。甜叶菊二萜糖甙含量近红外光谱预测模型的创建及应用研究中,利用多年份的单株选择材料,随机选出508份甜叶菊叶粉样品,取样品量约3g扫描其近红外光谱。参考分析方法采用改良的液相色谱法测定甜菊糖甙、瑞鲍迪甙A及两者的总百分含量。采用标准正态变换、二阶微分和Savitzky-Golay卷积平滑处理原始光谱。联用基于蒙特卡罗的无信息变量消除法和连续性投影法筛选光谱变量,剔除了大量无信息变量和冗余变量,同时降低光谱变量的共线性,提高了模型预测的准确性和稳健性,并在一定程度上克服了过拟合现象。建模过程中采用迭代的加权最小二乘支持向量机剔除异常值,并对剔除了异常值的训练集采用支持向量机回归算法,仿真数据和二萜糖甙总含量数据均表明,此混合方法的预测性能优于其他方法,对二萜糖甙总含量的预测均方根误差,决定系数,剩余预测偏差分别为0.843%、0.907和3.256。同时,通过PLS方法对筛选后的变量空间进行特征提取,提高了计算的效率,其预测均方根误差,决定系数和剩余预测偏差分别达到0.845%、0.906和3.249。对单个组分的甜菊糖甙和瑞鲍迪甙A,采用相同的建模方法,并对第一个偏最小二乘成分进行正交信号校正以提高预测的准确性。预测结果表明,应用所构建的近红外光谱预测模型进行大规模育种材料的筛选是可行的。应用此模型共筛选出70个瑞鲍迪甙A绝对含量较高和63个瑞鲍迪甙A相对含量较高的单株材料。其杂交F1子代50个品系的瑞鲍迪甙A绝对含量和相对含量均显著提高,甜叶菊味质得到显著改良。本研究所发展的近红外光谱技术还可为甜叶菊遗传研究,数量性状定位,种质资源评价以及甜叶菊叶片现场收购提供有利的检测工具。更多还原

【Abstract】 Near infared reflectance spectroscopy (NIRS) has been rapidly developed as a novel physical analysis technique in the late of 1980s in last century. Because the NIRS is nondestructive, fast, cost effective, environmentally safe, and allows the simultaneous estimation of several traits in a unique measurement, this technique was used widely in many areas and considered as international stardards. The diterpene glycosides in Stevia rebaudiana leaves are considered as a potential source of natural non caloric sweeteners and used widely in the food, medicine, cosmetic, etc. The present study aimed for assessing the potential of NIRS technique to estimate the stevioside, rebaudioside A and their total contents in Stevia rebaudiana leaves and to optimize the suitable regression method and variables space to develop a robust and accurate regression-model.A total of 508 samples selected randomly from the individual plants with good agronomic traits from 2008 to 2009. The percentage contens of stevioside and rebaudioside A of the leaf samples were determined by the reference method of HPLC. About 3g leaf powder of each sample was scanned from 400nm to 2498nm at the interval of 0.5nm. The entire spectrum was pretreated with the standard normalized variate, second derivatived and Savitzky-Golay convolution smoothing. For the pretreated spectrum in 350 samples in train set, Monte-Carlo uninformative variables elimination and successive projections algorithm were used to optimize the variable space, reduce the collinearity and overcome the overfitting.Based on the optimized variables space, the prediction model was developed by the insensitive loss function-support vector regression method after the outliers removed by using iterative reweighted least squares support vector regression. The hybrid method is superior to other methods, which has been certificated by the simulation data and the total glycosides content data with the smaller prediction risk and the better generalization. Further more, the extracted feature extracted by partial least squares was used as the inputs to construct the NIR calibration model. It is feasible to determine the stevioside, rebaudioside A and their total contents of them in Stevia leaves with the low root mean square error of prediction, high determination coefficient, and satisfactory residual predictive deviation. By using the developed models to screen the individual plants,133 parental materials and 50 F1 lines with absolutely or relatively high rebaudioside A content were primarily identified and tested subsequently by HPLC. Briefly, the developed model could be directly to predict the diterpene glycosides in Stevia leaves and had good performance in breeding project.更多还原