Chen Lu, Liu Yansong, Gao Huan, Cao Jiale, Qian Jiquan, Zheng Kexin, Jia Dongfeng, Gao Zhu, Xu Xiaobiao
Jiangxi Key Laboratory of Subtropical Forest Resources Cultivation, College of Forestry/Landscape Architecture and Art, Jiangxi Agricultural University, Nanchang 330045, China.
College of Agronomy, Kiwifruit Institute, Jiangxi Agricultural University, Nanchang 330045, China.
Foods. 2024 Dec 12;13(24):4014. doi: 10.3390/foods13244014.
The evaluation of quality traits is an important procedure for kiwifruit breeding and comprehensive utilization. The present study aimed to establish a comprehensive system to assess germplasms by analyzing 22 quality traits on kiwifruit samples collected from a wild population of 236 plants grown in the Jiangxi Province, China. Variability, correlation, principal components, and cluster analyses were carried out using the data collected from fruit quality evaluations. The coefficients of variation (CV) of fruit quality traits ranged from 11.66 to 66.16% (average coefficient = 35.09%), indicating a high level of variation among the 236 plants. In addition, different degrees of correlations were found between the traits, with similar traits demonstrating strong correlations. Principal component analysis (PCA) generated eight comprehensive and independent principal components, accounting for 77.93% of the original fruit quality information. Furthermore, an extensive evaluation from PCA ranked the plants based on cluster analysis and grouped them into seven categories. A stepwise regression analysis generated a prediction model, demonstrating a good fit (0.945) with the principal components of the comprehensive evaluation score. Overall, this study identifies nine quality traits, representing fruit appearance, sweetness, acidity, flavor, and nutritional attributes, as important traits for a comprehensive evaluation of fruits.
果实品质性状的评价是猕猴桃育种和综合利用的重要环节。本研究旨在建立一个综合体系，通过分析从中国江西省236株野生植株群体采集的猕猴桃样本的22个品质性状来评估种质资源。利用果实品质评价收集的数据进行了变异性、相关性、主成分和聚类分析。果实品质性状的变异系数（CV）在11.66%至66.16%之间（平均系数 = 35.09%），表明这236株植株间存在高度变异。此外，各性状之间存在不同程度的相关性，相似性状表现出较强的相关性。主成分分析（PCA）产生了8个综合且独立的主成分，占原始果实品质信息的77.93%。此外，基于主成分分析的广泛评价通过聚类分析对植株进行排名，并将它们分为七类。逐步回归分析生成了一个预测模型，与综合评价得分的主成分显示出良好的拟合度（0.945）。总体而言，本研究确定了9个品质性状，代表果实外观、甜度、酸度、风味和营养属性，为果实的综合评价提供了重要依据。
