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花生种质资源品质的近红外分析与评价 被引量:10

Analysis and Evaluation of Quality Characters of Peanut Varieties with Near Infrared Spectroscopy
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摘要 本研究利用近红外光谱分析技术对291份花生种质资源的含油量、蛋白质含量、脂肪酸含量、氨基酸含量等多个品质性状进行了分析。结果表明,不同花生种质资源的粗脂肪含量变幅为44.54%~61.17%;油酸含量变幅为29.75%~85.79%;蛋白质含量平均为21.78%,最高为31.24%;总氨基酸含量平均为19.28%。利用主成分分析技术,将18个品质性状综合成5个主成分因子,分别为蛋白质因子、不饱和脂肪酸因子、脯氨酸组氨酸负因子、苏氨酸负因子及粗脂肪因子,这5个因子反映了原始数据信息量的87.648%,可用于花生品质的综合评价。该结果可为新品种选育中的亲本选择提供有价值的参考,也可为发掘特异材料或特异基因、开展相关的遗传研究提供基础材料。 The contents of oil,protein,fatty acids and amino acids of 291 peanut varieties were analyzed using near infra-red spectroscopy in this study.The results showed that the oil content ranged from 44.54%~61.17%;the oleic acid content was from 29.75%~85.79%;the mean and the highest protein content was 21.78% and 31.24%,respectively;the mean content of total amino acids was 19.28%.Principal compo-nent analysis indicated that 18 quality traits were integrated into five principal component factors including pro-tein factor,unsaturated fatty acid factor,proline and histidine negative factors,threonine negative factor and crude fat factor.These five factors represented 87.648%of the original data,which could be used for evalua-ting peanut quality.These results provided important informations for parental selection in breeding program, and laid foundations for exploring special materials or genes to conduct relevant genetics research.
作者 李长生 石素华 孙金波 厉广辉 赵传志 王兴军 赵术珍 Li Changsheng;Shi Suhua;Sun Jinbo;Li Guanghui;Zhao Chuanzhi;Wang Xingjun;Zhao Shuzhen(Key Laboratory of Biology and Genetic Improvement of Oil Crops,Ministry of Agriculture,Wuhan 430062,China;Biotechnology Researeh Center,Shandong Academy of Agricultural Sciences/Shandong Provineial Key Laboratory of Crop Genetic Improvement,Ecology and Physiology,Jinan 250100,China;College of Life Scienees,Shandong Normal University,Jinan 250014,China)
出处 《山东农业科学》 2018年第6期154-158,共5页 Shandong Agricultural Sciences
基金 国家自然科学基金项目(31471526) 农业部油料作物生物学与遗传育种重点实验室开放课题(2017003) 山东省重点研发计划项目(GG201709280076)
关键词 花生 品质性状 近红外光谱技术 主成分分析 Peanut Quality characters Near infrared spectroscopy Principal component analysis
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