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Evaluation of Areca Quality Based on Principal Component and Hierarchical Cluster Analyses in Hainan, China

文献类型： 外文期刊

作者： Wang, Shiping 1 ; Kang, Xiaoning 1 ; Dai, Jiahui 1 ; Dai, Wenting 1 ; Zhang, Jing 1 ; Ji, Jianbang 1 ;

作者机构： 1.Hainan Acad Agr Sci, Inst Agroprod Proc & Design, Haikou, Peoples R China

2.Haikou Key Lab Areca Proc Res, Haikou 571100, Peoples R China

关键词： Areca catechu; hierarchical cluster analysis; physicochemical characteristics; principal component analysis; quality

期刊名称：HORTSCIENCE （ 影响因子：1.9； 五年影响因子：1.9 ）

ISSN： 0018-5345

年卷期： 2023 年 58 卷 6 期

页码：

收录情况： SCI

摘要： Areca (Areca catechu L.) is one of the most important cash crops in China and is considered the fourth most widely used addictive substance. In addition, areca is widely used in traditional and herbal medicines. The major characteristics of the fruit are affected by its genetic background and growth environment. The growing environment in different regions will impact the quality of agricultural products and the processing quality. The quality of areca is not only the basis of its commercialization development and processing quality, but also is an important basis for the scientific planting of areca. Therefore, deter-mining the quality of areca will provide evidence for scientific planting and more optimal applications. We evaluated the quality of areca by comparing the differences in physico-chemical characteristics using principal component analysis (PCA) and hierarchical cluster analysis. A total of 165 arecas, in the same growth period, were collected from 11 main producing regions in Hainan Province. Our results illustrate that the physicochemical char-acteristics of areca in different regions were significantly different. The PCA was conducted using 10 quality indexes, and three principal components were extracted to reflect 80% of the original variables. The first principal component mainly reflected the fruit shape qual-ity, the second principal component mainly reflected the hardness quality, and the third principal component mainly reflected the functional component quality. The relationship between each producing region and the principal component could be obtained intuitively from the principal component score plots. The arecas in Wanning and Wenchang were larger and their cellulose content was greater than in other areas, indicating that they were more suitable for processing. In contrast, the arecas in Baoting, Wuzhishan, Danzhou, Tunchang, and Dongfang had a greater arecoline content than the other areas, making them more suitable for use as medicinal materials. Hierarchical cluster analysis classified the 11 producing regions into five categories based on the measured parameters, which was consistent with the results of the PCA score plots. These results could provide informa-tion to improve the use of areca in China.

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