摘要
本试验探索一套适合加工型苹果果实品质的评价方法,得出影响苹果果实品质评价的主要因子,为北疆地区苹果深加工产业的发展提供参考。通过测定21项加工型苹果的果实品质指标,采用隶属函数法对各项指标数据进行标准化,在此基础上使用SPSS17.0对各项指标进行因子分析,通过7次迭代后的旋转得到各因子的载荷值,并以各公因子的权重计算各成分的分值和综合得分。数据经因子分析,提取出5个特征根大于1的公因子,累计贡献率达98.55%,对原始数据的信息能够充分表达。综合评价得出加工型苹果HT2得分最高,红勋1号次之,二者兼具营养价值高和耐贮藏的优点,可作为加工苹果原材料,HT3、冰心1号再次之,果实较小但果实密度较高,糖酸比较低不适宜鲜食,但可作为加工苹果原料,小冬果和秋力蒙排名最后,果实大且糖酸比高,但营养元素含量低且不耐贮藏,只可作为鲜食苹果不宜用作加工苹果原料。
The objective of this study is to establish a synthetical evaluation method of Malus fruit quality,find out the primary factors in evaluating Malus fruit quality,and provide reference for developing the deep processing industry of apples in Northern Xinjiang. Twenty-one quality indices of processed apple were measured,and the data were standardized by subordinate function. The software of SPSS17. 0 was used to analyze the factors of various indicators,and the rotated component matrix was obtained with‘Sevenimax'method. The comprehensive scores and each component scores were calculated based on the weight of each factor. According to the analysis of factors,five factors( characteristic root>1) hading98. 55% cumulative contribution rate were extracted from the converted data matrix,which could fully represent the information of original data. HT2 had the highest score,followed by Hongxun 1. Both of them had the advantages of highly nutritional value and long-storage,which could be used as raw material for processed apple. The fruit of HT3 and Malus robusta Bingxin 1 was smaller,but the density was higher,the sugar-acid ratio of those was lower,so they weren't suitable for eating. Xiaodongguo and Qiulimeng had bigger fruit,higher sugar-acid ratio,lower nutrition content and shorter storage period,they were suitable for eating but couldn't be used as raw material for processed apple.
出处
《江苏农业学报》
CSCD
北大核心
2018年第1期130-137,共8页
Jiangsu Journal of Agricultural Sciences
基金
新疆自治区林业发展补助资金项目(2017011)
国家科技部国际科技合作项目(2014DFR31070)
新疆维吾尔自治区公益性科研院所基本科研业务费专项(KYGY2016050)
关键词
加工型苹果
品质指标
隶属函数法
因子分析
聚类分析
主成分分析
processed apple
quality index
subordinate function
factor analysis
cluster analysis
principal component analysis
