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园艺学报 ›› 2026, Vol. 53 ›› Issue (5): 1301-1314.doi: 10.16420/j.issn.0513-353x.2025-0127

• 遗传育种·种质资源·分子生物学 • 上一篇    下一篇

适宜腌制加工的萝卜品种筛选与评价

刘辰1, 薛蕊1, 刘贤娴1, 徐文玲1, 常立春1, 魏子豪1, 杨猛2, 王淑芬1,*()   

  1. 1 山东省农业科学院蔬菜研究所农业农村部黄淮设施园艺工程重点实验室,山东省大宗露地蔬菜育种重点实验室, 济南 250100
    2 潍坊郭牌农业科技有限公司, 山东潍坊 261100
  • 收稿日期:2025-12-09 修回日期:2026-01-25 出版日期:2026-05-26 发布日期:2026-05-26
  • 通讯作者:
  • 基金资助:
    山东省农业良种工程项目(2022LZGC008); 山东省农业良种工程项目(2022LZGCQY013); 山东省农业良种工程项目(2024LZGC014); 山东省蔬菜产业技术体系项目(SDARS-05); 济南市“新高校20条”资助项目(202228092); 山东省政府公派出国留学项目(201802015)

Screening and Evaluation of Radish Cultivars Suitable for Pickling

LIU Chen1, XUE Rui1, LIU Xianxian1, XU Wenling1, CHANG Lichun1, WEI Zihao1, YANG Meng2, WANG Shufen1,*()   

  1. 1 Institute of VegetablesShandong Academy of Agricultural Sciences,Ministry of Agriculture and Rural Affairs Key Laboratory of Huang Huai Protected Horticulture Engineering,Shandong Key Laboratory of Bulk Open-field Vegetable Breeding, Jinan 250100, China
    2 Weifang Guo Brand Agricultural Technology Co.Ltd,Weifang, Shandong 261100, China
  • Received:2025-12-09 Revised:2026-01-25 Published:2026-05-26 Online:2026-05-26
  • Contact:

摘要:

以30份不同类型萝卜品种为试验材料,对腌制加工前后的萝卜13项指标进行检测,并利用相关分析、主成分分析等方法对指标进行评价,通过聚类分析对30份萝卜材料进行分类,采用回归分析建立腌制加工萝卜品质评价模型。通过切片观察分析细胞形态与萝卜鲜样硬度的关系。结果表明,30个品种的13项指标均存在不同幅度的变异,变异系数的范围为17.17% ~ 71.83%。除加工前萝卜中的果胶含量和加工后萝卜中的维生素C含量外,其余指标均至少与1项其他指标存在显著或极显著相关,其中,7组指标间达到显著相关,9组达到极显著相关。主成分分析前5个主成分的累计贡献率为75.64%。聚类分析将30个品种分为两大类,第一大类综合得分较高,以绿皮绿肉类型的品种为主,第二大类以红皮白肉和白皮白肉类型的品种为主。逐步回归分析得到最优的回归方程为:Y =-2.559 + 0.096 × 硬度 + 0.034 × 加工前萝卜中的维生素C含量 + 0.695 × 加工前萝卜中的游离氨基酸含量 + 0.064 × 加工前萝卜中的纤维素含量,其中硬度对模型评价得分的影响最大。切片观察显示,硬度与皮层细胞的面积和周长呈显著负相关,与皮层细胞的长宽比呈显著正相关,相关系数分别为-0.41,-0.42和0.43。综合来看,绿皮绿肉类型的品种更适宜腌制加工,其中‘沙窝青’和‘圣萝翠玉’的加工特性最好。

关键词: 萝卜, 腌制加工, 品质评价, 主成分分析, 指标筛选

Abstract:

To provide basis for cultivars selection and breeding for dried radish pickling,30 radish cultivars of different types were analyzed in this study. A total of 13 quality indexes of radish before or after pickling were detected and then analyzed by correlation analysis and principal component analysis. The radish materials were classified by clustering analysis,and the evaluation model was establish by regression analysis. In addition,the relationships between cellular morphology and hardness of fresh radish were analyzed through paraffin observation. The result showed that there were different variation coefficients of the 13 quality indexes in 30 radish cultivars,ranging from 17.17% to 71.83%. Except for pectin content in radish before processing and vitamin C content in radish after processing,all other indexes showed significant or extreme significant correlation to at least one other index. Among which,7 pairs were significantly correlated and 9 pairs were extremely significantly correlated. Further,5 principal components were extracted by principal component analysis,and the cumulative contribution rate reached to 75.64%. Through clustering analysis,30 radish cultivars were divided into two classes. The first class,which obtained higher scores,was mainly consisted by cultivars with green peel and green flesh,and the second class was mainly consisted by red radishes(red peel and white flesh)and white radishes(white peel and white flesh). The optimal regression equation was established as:Y =-2.559 + 0.096 × Hardness + 0.034 × Vitamin C content of radish before processing + 0.695 × Free amino acid content of radish before processing + 0.064 × Cellulose ontent of radish before processing. Among the indexes,hardness had the greatest impact on model evaluation score. By section observation,hardness of sample was found significantly negatively correlated to the area,perimeter of cortical cell,and significantly positively correlated to the length-width ratio of cortical cell,the correlation coefficients were-0.41,-0.42 and 0.43,respectively. Using the evaluation model established in this study,the cultivars with green peel and green flesh were more suitable for pickling,among which‘Shawoqing’and‘Shengluocuiyu’were the best cultivars.

Key words: radish, pickling, quality evaluation, principal component analysis, index screening