食品科学

• 基础研究 • 上一篇    下一篇

基于主成分分析和聚类分析的海沃德猕猴桃品质指标综合评价

傅隆生,宋思哲,邵玉玲,李平平,王海峰,崔永杰   

  1. 1.西北农林科技大学机械与电子工程学院,陕西 杨凌 712100;2.青岛中科昊泰新材料科技有限公司,山东 青岛 266326
  • 出版日期:2014-10-15 发布日期:2014-10-17

Comprehensive Evaluation of Kiwifruit Quality Based on Principal Component and Cluster Analysis

FU Long-sheng, SONG Si-zhe, SHAO Yu-ling, LI Ping-ping, WANG Hai-feng, CUI Yong-jie   

  1. 1. College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China;
    2. Qingdao Zhong Ke Hao Tai New Material Science & Technology Co. Ltd., Qingdao 266326, China
  • Online:2014-10-15 Published:2014-10-17

摘要:

为更准确评价猕猴桃品质,选取陕西省眉县的157 个海沃德猕猴桃,对果实的单果质量、长轴、短轴、厚度、体积、果皮颜色和糖度、酸度、硬度9 个分级指标进行了描述统计和相关分析,采用主成分分析法建立综合得分数学模型,对综合得分进一步做K-means聚类分析,最后利用Fisher判别分析法对样品重新进行聚类以验证K-means聚类分析方法的可靠性。结果表明,除体积与单果质量间差异不明显外,其余各分级指标之间均存在显著差异;按综合得分将样品聚为3 类:优为0.10~1.39,中为-0.44~0.09,差为-1.27~-0.46;判别分析对聚类结果的正确率达到98.72%,所以两者具有较高的一致性。

关键词: 猕猴桃, 质量评价, 主成分分析, 聚类分析, 判别分析

Abstract:

In this study, 157 kiwifruits from Meixian county, Shaanxi were selected and investigated for accurate evaluation
of kiwifruit quality. Nine indices including weight, long axis, short axis, thickness, volume, color, sugar content, acidity,
and firmness were measured and analyzed by descriptive statistics and correlation analysis. Principal component analysis
was employed to build a composite score mathematical model, and then the composite scores were analyzed by K-means
cluster. Fisher discriminate analysis was applied to re-cluster fruits for evaluating the reliability of K-means cluster analysis.
Results showed that all other grading indices except weight and volume presented significant differences. The 157 samples
were classified into three clusters according to their composite scores: good, 0.10-1.39; moderate, –0.44-0.09; and bad,
–1.27-0.46. The correct discrimination rate reached 98.72% when compared with the clustering analysis, suggesting that
there is a good consistency.

Key words: kiwifruits, quality evaluation, principal component analysis, cluster analysis, discriminant analysis

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