期刊文献+

基于主成分分析法的橙汁特征指标分类 被引量:2

Classification of Orange Juice Characteristic Indexes Based on——Principal Component Analysis
下载PDF
导出
摘要 对不同品牌的橙汁营养成分指标进行了调查统计,并在此基础上运用主成分分析法进行了分类分析。结果表明:橙汁的营养成分指标主要有:能量、蛋白质、脂肪、碳水化合物、钠和维生素C。其中能量和碳水化合物为同一类指标,蛋白质和脂肪为一类指标,钠和维生素C为同一类指标.根据相关资料和实际情况,此结论基本上是合理的,为橙汁的质量控制和有关部门对橙汁的检验工作提供了有利依据,具有一定的指导作用,可以减少检测步骤,提高检测效率,节约检测成本,为选用步骤少、效率高、成本低的检测对象来反应饮用水质量提供了理论依据,同时也为消费者对橙汁品牌的选择提供了依据。 This article carries out a survey of the different brands of orange juice nutrients indicators, and then uses the method of principal component analysis to classify the orange juice characteristic indexes. The results show that the contents of energy, protein, fat, carbohydrate, sodium ion and vitamin C constitute the orange juice characteristic indexes; the contents of energy and carbohydrate belong to the same kind of composition, the contents of protein and fat belong to the same kind of composition and the content of sodi- um ion and the vitamin c are the same type of composition. According to the relevant information and the ac tual situation, the conclusion is basically exact and can play a guiding role in the quality control of commodi ty drinking water and in the preliminary test of the quality inspection department. And it can also provide a reference for consumers to select the brands of orange juice.
作者 刘湘云
出处 《绿色科技》 2013年第1期170-172,共3页 Journal of Green Science and Technology
关键词 橙汁 主成分分析 营养成分指标 orange juice principal component analysis characteristics indexes
  • 相关文献

参考文献3

  • 1汪应洛.系统工程[M]北京:机械工业出版社,200954-60.
  • 2方开泰.实用多元统计分析[M]上海:华东师范大学出版社,1989.
  • 3王学仁;王松桂.实用多元统计分析[M]上海:上海科学技术出版社,1990.

同被引文献36

  • 1张媛,张燕平.一种PCA算法及其应用[J].微机发展,2005,15(2):67-68. 被引量:21
  • 2陈军辉,谢明勇,王远兴,张中伟,彭日煌,王小如.主成分分析法用于西洋参样品分类研究[J].天然产物研究与开发,2006,18(2):193-197. 被引量:24
  • 3陈爱华,焦必宁.常见果汁掺假检测技术的研究进展[J].中国食品添加剂,2007,18(5):153-156. 被引量:12
  • 4高尚.三种计算层次分析法中权值的方法[J].科学技术与工程,2007,7(20):5204-5207. 被引量:36
  • 5Johnson D E. Applied Multivariate Methods for Data Analysts. Beijing: Higher Education Press, 2005, 93-111.
  • 6Pearson K, Mag P. On lines and planes of closest fit to systems of points in space. 1901(2):559-572.
  • 7Niu Zhiguo, Qiu Xuehong. Facial expression recognition based on weighted principal component analysis and support vector machines. International Conference on Advanced Computer Theory and Engineering, Chengdu: IEEE, 2010:174-178.
  • 8Patra S, Acharya S K. Dimension reduction of feature vectorsusing WPCA for robust speaker identification system. IEEE International Conference on Recent Trends in Information Technology. Kunming: IEEE, 2011: 28-32.
  • 9Jonathnn Shlens. A Tutorial on Principal Component Analysis. Version I, 2009.
  • 10Nan Liu, Han Wang. Weighted principal component extraction with genetic algorithms. Applied Soft Computing, 2011.

引证文献2

二级引证文献4

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部