Abstract: Amino acid variation and nutritional value of Xinjiang Jun jujube from 11 counties of Xinjiang were preliminarily verified, which provided theoretical basis for better utilization of jujube resources. Using Jiaocheng Jun jujube from Shanxi province as a control, the amino acid data of Xinjiang Jun jujube were analyzed by fuzzy recognition method, variability, and system clustering method. The results suggested the total content of amino acid in Xinjiang Jun jujube was superior to that of Jiaocheng Jun jujube. There is a big difference in amino acid composition content of Jun jujube from 12 different production areas, especially amino acids of Val, Leu, Thr, Lys and Phe. The closeness degrees of protein amino acid respect to model amino acid of different production areas were basically the same. The scores of ratio coefficient of amino acid (SRC) of Xinjiang Jun jujube were superior to that of Jiaocheng Jun jujube. The ratio coefficient of amino acid (RC) of Val from different production areas was the lowest, which mean Val was the first limited amino acid. The Jun jujubes from different production areas could be divided into three categories by cluster analysis. The better ones after protein quality evaluation were Awat, Qira and Akesu. The amino acid composition of Jun jujube protein in 11 counties of Xinjiang have their own characteristics, and the protein composition of Jun jujube in Awat, Qira and Akesu meet the nutrition requirement for protein of human, which should be further utilized.

Key words: Jun jujube from Xinjiang province, amino acid, nutrition evaluation, amino acid score, cluster analysis

摘要： 为了初步探明新疆主栽骏枣氨基酸差异性及营养价值，为合理优化利用骏枣资源提供理论依据。以山西交城骏枣为参照，通过模糊识别法、系统聚类法，对新疆11县市骏枣氨基酸指标数据进行分析。结果表明新疆不同产区供试骏枣的氨基酸总量较高，且各产区骏枣中所含缬氨酸（Val）、亮氨酸（Leu）、苏氨酸（Thr）、赖氨酸（Lys）、苯丙氨酸（Phe）5种必需氨基酸含量间存在较大差异；不同产区骏枣中氨基酸蛋白模式贴近度基本一致；新疆骏枣氨基酸比值系数分（SRC）整体优于交城骏枣，不同产区骏枣氨基酸中Val的氨基酸比值系数(RC)值最低，为第一限制性氨基酸；不同产区经聚类可分为3类，骏枣蛋白质品质评价较好的为阿瓦提县、策勒县和阿克苏市。新疆11县市骏枣蛋白质中氨基酸组成各具特点，且阿瓦提县、策勒县、阿克苏市骏枣蛋白质组成更符合人体蛋白营养需求，宜深化利用。

关键词: 新疆骏枣, 氨基酸, 营养评价, 氨基酸评分, 聚类分析