基于主成分与聚类分析的苹果加工品质评价

公丽艳; 孟宪军; 刘乃侨; 毕金峰

doi:10.3969/j.issn.1002-6819.2014.13.034

基于主成分与聚类分析的苹果加工品质评价

1.
沈阳农业大学食品学院，沈阳 110866
2.
农业部农产品加工综合性重点实验室，中国农业科学院农产品加工研究所，北京 100193
3.
辽宁经济管理干部学院生物工程系，沈阳 110122

基金项目: 国家公益性行业（农业）科研专项（200903043-01-03）；沈阳市科技计划项目资助（F11-121-3-00）

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出版历程
- 收稿日期: 2013-12-11
- 修回日期: 2014-04-01
- 发布日期: 2014-06-30

Evaluation of apple quality based on principal component and hierarchical cluster analysis

1.
College of Food Science, Shenyang Agricultural University, Shenyang 110866, China
2.
Key Laboratory of Agricultural Product Processing and Quality Control, Ministry of Agriculture, Institute of Agro-products Processing Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
3.
Department of biological engineering, Liaoning Economic Management Cadre Institute, Shenyang 110122, China

摘要

摘要: 为了探讨苹果品种间理化品质的差异，给选育新品种和苹果合理加工利用提供理论支持，采集了30个苹果品种为试材进行模式识别分析。该试验所用苹果在2011年8－10月份按照苹果可采成熟度（九成熟）在辽宁省兴城市国家种质资源圃进行采集。试验测定了单果质量、体积、密度、果皮颜色、硬度、糖酸比、维生素C等20项理化品质指标，采用描述性统计、主成分和聚类分析方法对苹果品种和品质关系进行了分析。结果显示，30个品种苹果的密度、果型指数和含水率3项指标未表现出差异。对剩余17项品质指标进行了主成分分析，依据主成分解释总变量和碎石图提取了6个主成分反映原变量的82.3%的信息。第一主成分主要综合了可滴定酸、糖酸比及固酸比的信息即口尝品质因子；第二主成分主要综合了L值，a值和b值的信息，命名为颜色因子；其余主成分依次为甜度因子、质构因子、内在品质因子和果个大小因子。结合主成分得分图直观地显示了苹果品种和理化指标间关系：辽伏、理想、早金冠和瑞光分布在PC1和PC2的正向区间，糖酸比和固酸比值较大、口感好，但是果皮颜色较绿，是品质较好的早熟苹果；寒富、赤阳和红富士依次落在PC1和PC2第四区间，食用品质好、果皮颜色较红，是较为常见的晚熟苹果。分布在第二区间的普利阿姆，白星，Pvma果皮颜色绿且口感酸涩，不适宜鲜食，这些品种可能较为适宜进行加工。第三区间品种固酸比、甜酸比取值较小但是果皮颜色红，为选育果皮颜色受消费者喜爱且内在品质好的早熟品种提供了理论支持。聚类分析将30个品种苹果分为5类，聚类结果与主成分得分图结果基本一致，该试验初步判定30个品种苹果是否适宜鲜食，为苹果品种选育和加工应用利用提供理论依据。
- 主成分分析 /
- 聚类分析 /
- 水果 /
- 苹果 /
- 理化品质
Abstract: Abstract: The purpose of this study was to investigate the variations in physical and chemical characteristics of apple fruit from 30 varieties grown in the same place using pattern recognition tools. Twenty quality parameters of apple samples (e.g. weight, volume, density, color, hardness, sugar-acid ratio, Vitamin C, etc.) were analyzed. Interrelationships between the parameters and the apple variety were investigated by descriptive statistics, principal component analysis (PCA) and hierarchical cluster analysis (HCA). PCA is a mathematical tool which performs a reduction in data dimensionality and allows the visualisation of underlying structure in experimental data and relationships between data and samples. In hierarchical cluster analysis, samples are grouped on the basis of similarities, without taking into account the information about the class membership. The results obtained following HCA are shown as a dendrogram in which five well-defined clusters are visible. Samples will be grouped in clusters in terms of their nearness or similarity. Cluster analysis uses less information (distances only) than PCA. It is interesting to observe what kind of classification can be made on the basis of distances only. The results showed that density, fruit shape index and water content of 30 apple varieties were not significantly different. The remaining seventeen measurements were investigated by principal component analysis. The first six components represented 83.56% of the total variability on the base of the total variance explained and screen plot of principal component analysis. The first principal component was related to titratable acidity, sugar-acid ratio and solid-acid ratio attributes, which were the taste quality factor. The second principal component was related to L, a, and b attributes, which were the color factor. Following that were sweetness factor, texture factor, quality factor and size factor. The sample score plots visually displayed the relationship between measurements and apple varieties. The varieties of Liaofu, Lixiang, Golden Delicious and Rome Beauty were located on the first quadrant of the plot for PC1 vs. PC2. They had better quality of among the mid-early maturity apples with high content of sugar-acid and solid-acid ratios which were good characteristics of taste even though their skin color was green. The varieties of Hanfu, Rainier and Fuji were located on the fourth quadrant of the same plot. They were common late maturity apples with red color and good quality for eating. Priam, Baixing and Pvma were located on the second quadrant of the plot, which may be suitable for processing not for eating, and they had a green skin color and sour taste. The varieties located on third quadrant were red but had low sugar-acid and solid-acid ratios, which can be used as breeding materials for red color and good quality of the early maturity apple. HCA classified 30 varieties into five main groups on the basis of the measured parameters, which was consistent with the results of PCA score plots. These results can be used preliminarily to determine whether the 30 apple varieties were appropriate for eating or for processing or for use as a breeding stock for future apple variety development.
- principal component analysis /
- cluster analysis /
- fruits /
- apples /
- physical chemical characterization

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基于主成分与聚类分析的苹果加工品质评价

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出版历程

Evaluation of apple quality based on principal component and hierarchical cluster analysis

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