FT-NIR光谱法测定紫花苜蓿青干草的6项品质指标  

Quality Prediction of Alfalfa Hay Using Fourier Transform Near Infrared Reflectance Spectroscopy

作　　者：聂志东[1] 韩建国[1] 玉柱[1] 张录达[2] 李军会[3] 仲勇 刘富渊 

机构地区：[1]中国农业大学草地研究所,北京100094 [2]中国农业大学理学院,北京100094 [3]中国农业大学信息与电气工程学院,北京100094 [4]成都大业国际投资股份有限公司甘肃总部,甘肃酒泉735000

出　　处：《光谱学与光谱分析》2007年第7期1308-1311,共4页Spectroscopy and Spectral Analysis

基　　金：农业部"948"项目(2006-G38);国家科技攻关计划课题(2006BAD16B01)资助

摘　　要：紫花苜蓿青干草营养价值非常高,能够为家畜提供大量优质的蛋白质饲料,也是目前我国最主要的草产品类型之一。文章应用傅里叶变换近红外（FT-NIR）光谱技术,使用偏最小二乘法首次建立了国内紫花苜蓿青干草粗蛋白、粗灰分、中性洗涤纤维、酸性洗涤纤维和酸性洗涤木质素以及体外干物质消化率等6项品质指标的预测模型,并对模型进行了交互验证和外部验证。结果表明：该试验所建立的紫花苜蓿青干草6项主要品质指标的近红外模型具有良好的准确性和预测能力,交互验证相关系数rval为0.953 88~0.990 19,交互验证均方根RMSECV分别为0.345%~1.980%,外部验证的相关系数为0.963~0.990。该方法快速、准确,无需化学试剂,对我国紫花苜蓿草产品的质量检测,以及科研中紫花苜蓿草产品品质分析、种质资源评价、育种世代的鉴定和筛选都有非常重要的意义。Alfalfa hay has high nutritive value, and it is one of the most important protein feed for domestic animals. The quality parameters of alfalfa hay, including CP, Ash, NDF, ADF, ADL and IVDMD, were predicted using Fourier transform near infrared reflectance spectroscopy with PLS regression in this test. Then the 6 models were validated by cross-validation and external-validation. The results indicated that FT-NIR models of alfalfa hay quality have considerable accuracy and precision： the correlation coefficient of cross-validation is 0. 953 88 to 0. 990 19, and the RMSECV is 1. 980-0. 345 ; The correlation coefficient of external-validation is 0. 963-0. 990. By using FF-NIR, analysis can rapidly and accurately determine the quality of alfalfa without any chemical reagent. This method is of great significance for analysing the trait of alfalfa production, the quality determination, the estimation of germ plasm resource, and the identifying and selecting of hybridized generations in alfalfa research of China.

关 键 词：傅里叶变换 近红外 模型 紫花苜蓿 品质 

分 类 号：O657.3[理学—分析化学]