中国农业科学 ›› 2022, Vol. 55 ›› Issue (4): 641-652.doi: 10.3864/j.issn.0578-1752.2022.04.002

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

花生烘烤食用品质评价及指标筛选

卞能飞(),孙东雷,巩佳莉,王幸,邢兴华,金夏红,王晓军()   

  1. 江苏徐淮地区徐州农业科学研究所,江苏徐州 221131
  • 收稿日期:2021-08-20 接受日期:2021-10-15 出版日期:2022-02-16 发布日期:2022-02-23
  • 通讯作者: 王晓军
  • 作者简介:卞能飞,E-mail: biannf@163.com
  • 基金资助:
    财政部和农业农村部:国家现代农业产业技术体系(CARS-13);江苏省农业科技自主创新资金(cx182015);徐州市科技项目(KC19114);亚夫科技服务项目(KF201005);苏北科技专项(XZ-SZ201224)

Evaluation of Edible Quality of Roasted Peanuts and Indexes Screening

BIAN NengFei(),SUN DongLei,GONG JiaLi,WANG Xing,XING XingHua,JIN XiaHong,WANG XiaoJun()   

  1. Xuzhou Institute of Agricultural Sciences of the Xuhuai District, Xuzhou 221131, Jiangsu
  • Received:2021-08-20 Accepted:2021-10-15 Online:2022-02-16 Published:2022-02-23
  • Contact: XiaoJun WANG

摘要:

【目的】探索花生烘烤食用品质评价方法,筛选评价指标,建立预测模型,为花生食用品质育种提供依据。【方法】以51个不同品质类型花生品种(系)的籽仁为试验材料,测定烘烤籽仁的食味指标、外观指标、质构指标和营养指标等食用品质相关指标27项,运用相关分析、主成分分析对花生烘烤食用品质进行综合评价,通过聚类分析对51个花生品种(系)的烘烤食用品质进行分类,通过回归分析建立花生烘烤食用品质预测模型和鉴定指标的筛选。【结果】51个花生品种(系)27项指标的变异幅度不同,变异系数为5.86%—39.65%。各单项指标均存在与之显著或极显著相关的指标,175组相关系数达显著水平,140组达极显著水平。主成分分析将27个单项指标转换为5个相互独立的综合指标,其贡献率分别为35.70%、20.63%、10.07%、8.19%和6.38%,代表了全部数据80.97%的信息量。综合评价分析表明,51个花生品种(系)烘烤食用品质综合值F平均为0.76,徐花甜29的F最高(F=1.51),烘烤食用品质最好,徐花15号的F最低(F=0.03),烘烤食用品质最差。相关性分析结果表明,21项指标与F显著相关。对花生烘烤食用品质综合值F进行聚类分析,将51个品种(系)划分为3类,第一类属于烘烤食用品质较好,包含徐花甜29、冀花甜1号、临花16号和徐花甜30共4个品种;第二类属于烘烤食用品质一般,包含33个品种(系);第三类属于烘烤食用品质较差,包含14个品种(系)。采用逐步回归分析方法,建立花生烘烤食用品质预测模型为:Y=0.979+0.021X7+0.081X21+0.009X20-0.034X19-0.074X27R2=0.953),筛选出5个烘烤食用品质鉴定指标,分别为百仁重、蔗糖含量、蛋白质含量、脂肪含量和山嵛酸含量。对不同类别花生特征分析可知,4个烘烤食用品质较好品种的百仁重中至高,蔗糖含量高,蛋白质含量中,脂肪含量低,山嵛酸含量低至中,根据预测模型,此类品种仍需进一步改良以提高蛋白质含量和降低山嵛酸含量。【结论】百仁重、蔗糖含量、蛋白质含量、脂肪含量和山嵛酸含量可作为鉴定花生烘烤品质的指标,可以确定优质烘烤花生品种应具备籽仁中大粒、高油酸、高蔗糖、高蛋白、低脂肪和低山嵛酸等性状。

关键词: 花生, 烘烤, 食用品质, 综合评价, 指标筛选

Abstract:

【Objective】The objective of this study was to explore the evaluation methods of edible quality of roasted peanuts, screen identification indexes, establish prediction model, and provide basis for peanut edible quality breeding. 【Method】The kernels of 51 peanut varieties (lines) with different quality types were used as experimental materials. A total of 27 edible quality indexes related to tastes, appearances, textures and nutrition of roasted kernels were measured. Correlation analysis and principal component analysis were used to comprehensively evaluate the edible quality of roasted peanut kernels. Cluster analysis was used to classify edible quality of 51 peanut varieties (lines). Regression analysis were used to establish predictive model and index screening. 【Result】27 indexes had different ranges of variation in 51 peanut varieties (lines), with coefficients of variation ranging from 5.86% to 39.65%. There were significant or extremely significant related indexes for each individual index. The correlation coefficients of 175 groups reached significant level, and the 140 groups reached extremely significant level. The 27 individual indexes were converted into 5 independent comprehensive indexes through principal component analysis, and their contribution rates respectively were 35.70%, 20.63%, 10.07%, 8.19% and 6.38%, representing the information of 80.97% of all data. The comprehensive evaluation analysis showed that the average F value of the roasted edible quality of 51 peanut varieties (lines) was 0.76. Xuhuatian 29 had the highest F value (F=1.51) and the best roasting edible quality. Xuhua 15 had the lowest F value (F=0.03) and the worst roasting edible quality. The correlation analysis showed that 21 indexes were significantly correlated with F value. Cluster analysis was performed on the comprehensive value F of peanut roasting edible quality, and 51 varieties (lines) were divided into 3 categories. The first category was of good edible quality, including 4 varieties of Xuhuatian 29, Jihuatian 1, Linhua 16 and Xuhuatian 30. The second category was of general edible quality and contained 33 varieties (lines). The third category was of poor edible quality and contained 14 varieties (lines). Using stepwise regression analysis method, the prediction model of roasting edible quality was established as: Y=0.979+ 0.021X7+0.081X21+0.009X20-0.034X19-0.074X27 (R2=0.953). Then, 5 identification indexes were screened, which were the hundred kernel weight, sucrose content, protein content, fat content and behenic acid content. The analysis of characteristics showed that the four varieties with good roasting edible quality had medium to high hundred kernel weight, high sucrose content, medium protein content, low fat content, and low to medium behenic acid content. According to the prediction model, this category varieties still need to be improved to increase protein content and reduce behenic acid content. 【Conclusion】Hundred kernel weight, sucrose content, protein content, fat content and behenic acid content could be used to identify the edible quality of roasted peanuts. It is determined that high-quality roasted peanut varieties should have the characteristics of medium to large kernels, high oleic acid content, high sucrose content, high protein content, low fat content and low behenic acid content.

Key words: peanut, roast, edible quality, comprehensive evaluation, index screening