白三叶营养丰富,蛋白质含量高,是最重要的牧草之一。文章对SPAD及FT-NIR光谱法筛选白三叶种质蛋白质性状进行了探讨。采用Chlorophyll Meter SPAD-502,测定白三叶叶片SPAD值,从而评估其蛋白质含量。在营养生长期内,叶片蛋白质含量与SPA...白三叶营养丰富,蛋白质含量高,是最重要的牧草之一。文章对SPAD及FT-NIR光谱法筛选白三叶种质蛋白质性状进行了探讨。采用Chlorophyll Meter SPAD-502,测定白三叶叶片SPAD值,从而评估其蛋白质含量。在营养生长期内,叶片蛋白质含量与SPAD值呈正相关(y=0.422x+4.984,R2=0.737);在开花期内,两者之间呈负相关(y=-0.345x+37.50,R2=0.711)。应用傅里叶变换近红外(FT-NIR)光谱技术,用偏最小二乘法建立了白三叶蛋白质的预测模型,并对模型进行了交叉验证和外部验证。结果表明,用NIRS法得到的预测值与用凯氏定氮法得到的测定值间的交叉验证决定系数R2cv为0.904,交叉检验标准误差RMSECV为0.988(%DM),外部验证的相关系数为0.987。所建立的近红外模型具有良好的准确性和预测能力。FT-NIR法较SPAD法能更准确的评估白三叶蛋白质状况。NIRS作为一种白三叶粗蛋白质快速分析的技术是可行的,在白三叶蛋白质品质育种中,可快速进行种质资源筛选,提高育种效率。展开更多
With the technique of Fourier transform near infrared (FT-NIR) spectroscopy, the calibration models for quantitative analysis of sucrose and polarization in sugarcane juice were developed by using transmission mode an...With the technique of Fourier transform near infrared (FT-NIR) spectroscopy, the calibration models for quantitative analysis of sucrose and polarization in sugarcane juice were developed by using transmission mode and calibrating with partial least square (PLS) algorithm. The determination coefficients (R2) of the predicted models for sucrose and polarization in juice were 0. 9980 and 0. 9979 respectively; the root mean square errors of cross validation (RMSECV) were 0. 143 and 0. 155% for sucrose and polarization in juice respectively. The predictive errors measured by FT-NIR were close to those by routine laboratory methods. The results demonstrated that the FT-NIR methods had high accuracy and they were able to replace the routine laboratory analysis. It was also demonstrated that as a rapid and accurate measurement, the FT-NIR technique had potential applications in quality control of mill sugarcane, establishment of payment system based on sugarcane quality, and selection of clones in sugarcane breeding.展开更多
文摘白三叶营养丰富,蛋白质含量高,是最重要的牧草之一。文章对SPAD及FT-NIR光谱法筛选白三叶种质蛋白质性状进行了探讨。采用Chlorophyll Meter SPAD-502,测定白三叶叶片SPAD值,从而评估其蛋白质含量。在营养生长期内,叶片蛋白质含量与SPAD值呈正相关(y=0.422x+4.984,R2=0.737);在开花期内,两者之间呈负相关(y=-0.345x+37.50,R2=0.711)。应用傅里叶变换近红外(FT-NIR)光谱技术,用偏最小二乘法建立了白三叶蛋白质的预测模型,并对模型进行了交叉验证和外部验证。结果表明,用NIRS法得到的预测值与用凯氏定氮法得到的测定值间的交叉验证决定系数R2cv为0.904,交叉检验标准误差RMSECV为0.988(%DM),外部验证的相关系数为0.987。所建立的近红外模型具有良好的准确性和预测能力。FT-NIR法较SPAD法能更准确的评估白三叶蛋白质状况。NIRS作为一种白三叶粗蛋白质快速分析的技术是可行的,在白三叶蛋白质品质育种中,可快速进行种质资源筛选,提高育种效率。
文摘With the technique of Fourier transform near infrared (FT-NIR) spectroscopy, the calibration models for quantitative analysis of sucrose and polarization in sugarcane juice were developed by using transmission mode and calibrating with partial least square (PLS) algorithm. The determination coefficients (R2) of the predicted models for sucrose and polarization in juice were 0. 9980 and 0. 9979 respectively; the root mean square errors of cross validation (RMSECV) were 0. 143 and 0. 155% for sucrose and polarization in juice respectively. The predictive errors measured by FT-NIR were close to those by routine laboratory methods. The results demonstrated that the FT-NIR methods had high accuracy and they were able to replace the routine laboratory analysis. It was also demonstrated that as a rapid and accurate measurement, the FT-NIR technique had potential applications in quality control of mill sugarcane, establishment of payment system based on sugarcane quality, and selection of clones in sugarcane breeding.
文摘本文以76份青稞为研究对象,利用近红外光谱仪采集青稞4000~10000 cm-1波段光谱,并联合其水分、β-葡聚糖、直链淀粉、蛋白质实测含量数值,构建了基于近红外光谱技术的青稞特征营养成分含量快速检测模型。结果显示,SG卷积平滑(Savitzky Golay,SG)是水分、直链淀粉、β-葡聚糖含量的偏最小二乘法(Partial Least Squares,PLS)预测模型的最优光谱预处理方法,而SG卷积平滑+多元散射校正(Multiplicative Scatter Correction,MSC)是蛋白质含量的偏最小二乘法(PLS)预测模型的最优光谱预处理方法。为进一步提高青稞各成分含量预测模型的准确性,考察了竞争性自适应重加权法(Competitive Adaptive Reweighted Sampling,CARS)、连续投影算法(Successive Projections Algorithm,SPA)和变量组合集群分析混合迭代保留信息变量法(Variables Combination Population Analysis and Iterative Retained Information Variable,VCPA-IRIV)特征波长选择算法对模型预测结果的影响。结果表明,VCPA-IRIV处理可有效提高水分、直链淀粉、蛋白质含量预测模型的预测决定系数,降低预测均方根误差;CARS对β-葡聚糖含量预测模型优化效果显著。基于上述最优方法建立的青稞水分、β-葡聚糖、直链淀粉、蛋白质实测含量预测模型,其预测相关系数分别为0.9868、0.9808、0.9701、0.9879;预测均方根误差分别为0.2042、0.1846、0.8135、0.2095。综上,本研究建立的基于近红外光谱的青稞特征营养成分含量快速检测模型具有较高的准确性,对加工企业快速了解原料品质及高效筛选合格原料有一定指导意义。
