Comprehensive Evaluation of Chestnut Quality Based on Principal Component and Cluster Analysis
To develop an appropriate method for evaluating the quality of chestnut resources. The 21 quality indicators of 25 chestnut varieties were detected and analyzed. The key indicators of affecting the quality of chestnut were selected through principal component analysis (PCA) coupled with correlation...
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Articolo |
| Lingua: | zho |
| Pubblicazione: |
The editorial department of Science and Technology of Food Industry
2025-01-01
|
| Serie: | Shipin gongye ke-ji |
| Soggetti: | |
| Accesso online: | http://www.spgykj.com/cn/article/doi/10.13386/j.issn1002-0306.2024020255 |
| _version_ | 1826883804106588160 |
|---|---|
| author | Yanqi YU Mingyuan YANG Chunmao LÜ Shaoci BAI Qunfang ZHANG Chenyang ZOU Han JIANG |
| author_facet | Yanqi YU Mingyuan YANG Chunmao LÜ Shaoci BAI Qunfang ZHANG Chenyang ZOU Han JIANG |
| author_sort | Yanqi YU |
| collection | DOAJ |
| description | To develop an appropriate method for evaluating the quality of chestnut resources. The 21 quality indicators of 25 chestnut varieties were detected and analyzed. The key indicators of affecting the quality of chestnut were selected through principal component analysis (PCA) coupled with correlation analysis and descriptive statistical analysis. The weights of these key indicators were calculated based on the entropy weight method to construct the gray correlation evaluation model. Our findings revealed notable differences (P<0.05) in various quality indicators among the different chestnut varieties and observed significant correlations among several of these indicators. The key indicators identified by PCA included moisture, the ratio of amylose to amylopectin (AA), total flavonoids, good fruiting rate, fruit shape index, hardness, soluble sugar, and reducing sugar. The weights of these key indicators obtained by entropy weighting methods were 14.08%, 14.64%, 15.64%, 7.74%, 9.41%, 9.11%, 18.90%, and 10.48%, respectively. The gray correlation analysis indicated that the overall qualities of the varieties Danli No.1, Dandong 9113, and qX-005 ranked among the top three. The 25 chestnut varieties were categorized into four groups by cluster analysis. The first group was ideal for developing functional beverages, the second was suited for kernel processing into canned food, preserved fruit, or chestnut powder for baked goods. The third group served as a high-quality food ingredient and the fourth group was best for frying and as a direct-sale nut. This study provides valuable insights for selecting superior resources and breeding high-quality chestnut varieties, while also laying a theoretical foundation for the comprehensive utilization of each type of chestnut. |
| first_indexed | 2025-02-17T03:53:30Z |
| format | Article |
| id | doaj.art-2c0bd6d45d304c0cb1558f9c11056411 |
| institution | Directory Open Access Journal |
| issn | 1002-0306 |
| language | zho |
| last_indexed | 2025-02-17T03:53:30Z |
| publishDate | 2025-01-01 |
| publisher | The editorial department of Science and Technology of Food Industry |
| record_format | Article |
| series | Shipin gongye ke-ji |
| spelling | doaj.art-2c0bd6d45d304c0cb1558f9c110564112025-01-10T06:49:30ZzhoThe editorial department of Science and Technology of Food IndustryShipin gongye ke-ji1002-03062025-01-0146228029110.13386/j.issn1002-0306.20240202552024020255-2Comprehensive Evaluation of Chestnut Quality Based on Principal Component and Cluster AnalysisYanqi YU0Mingyuan YANG1Chunmao LÜ2Shaoci BAI3Qunfang ZHANG4Chenyang ZOU5Han JIANG6College of Food Science, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Food Science, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Food Science, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Food Science, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Food Science, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Food Science, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Food Science, Shenyang Agricultural University, Shenyang 110866, ChinaTo develop an appropriate method for evaluating the quality of chestnut resources. The 21 quality indicators of 25 chestnut varieties were detected and analyzed. The key indicators of affecting the quality of chestnut were selected through principal component analysis (PCA) coupled with correlation analysis and descriptive statistical analysis. The weights of these key indicators were calculated based on the entropy weight method to construct the gray correlation evaluation model. Our findings revealed notable differences (P<0.05) in various quality indicators among the different chestnut varieties and observed significant correlations among several of these indicators. The key indicators identified by PCA included moisture, the ratio of amylose to amylopectin (AA), total flavonoids, good fruiting rate, fruit shape index, hardness, soluble sugar, and reducing sugar. The weights of these key indicators obtained by entropy weighting methods were 14.08%, 14.64%, 15.64%, 7.74%, 9.41%, 9.11%, 18.90%, and 10.48%, respectively. The gray correlation analysis indicated that the overall qualities of the varieties Danli No.1, Dandong 9113, and qX-005 ranked among the top three. The 25 chestnut varieties were categorized into four groups by cluster analysis. The first group was ideal for developing functional beverages, the second was suited for kernel processing into canned food, preserved fruit, or chestnut powder for baked goods. The third group served as a high-quality food ingredient and the fourth group was best for frying and as a direct-sale nut. This study provides valuable insights for selecting superior resources and breeding high-quality chestnut varieties, while also laying a theoretical foundation for the comprehensive utilization of each type of chestnut.http://www.spgykj.com/cn/article/doi/10.13386/j.issn1002-0306.2024020255chestnutprincipal component analysisentropy weight methodgrey relational degree analysiscluster analysis |
| spellingShingle | Yanqi YU Mingyuan YANG Chunmao LÜ Shaoci BAI Qunfang ZHANG Chenyang ZOU Han JIANG Comprehensive Evaluation of Chestnut Quality Based on Principal Component and Cluster Analysis Shipin gongye ke-ji chestnut principal component analysis entropy weight method grey relational degree analysis cluster analysis |
| title | Comprehensive Evaluation of Chestnut Quality Based on Principal Component and Cluster Analysis |
| title_full | Comprehensive Evaluation of Chestnut Quality Based on Principal Component and Cluster Analysis |
| title_fullStr | Comprehensive Evaluation of Chestnut Quality Based on Principal Component and Cluster Analysis |
| title_full_unstemmed | Comprehensive Evaluation of Chestnut Quality Based on Principal Component and Cluster Analysis |
| title_short | Comprehensive Evaluation of Chestnut Quality Based on Principal Component and Cluster Analysis |
| title_sort | comprehensive evaluation of chestnut quality based on principal component and cluster analysis |
| topic | chestnut principal component analysis entropy weight method grey relational degree analysis cluster analysis |
| url | http://www.spgykj.com/cn/article/doi/10.13386/j.issn1002-0306.2024020255 |
| work_keys_str_mv | AT yanqiyu comprehensiveevaluationofchestnutqualitybasedonprincipalcomponentandclusteranalysis AT mingyuanyang comprehensiveevaluationofchestnutqualitybasedonprincipalcomponentandclusteranalysis AT chunmaolu comprehensiveevaluationofchestnutqualitybasedonprincipalcomponentandclusteranalysis AT shaocibai comprehensiveevaluationofchestnutqualitybasedonprincipalcomponentandclusteranalysis AT qunfangzhang comprehensiveevaluationofchestnutqualitybasedonprincipalcomponentandclusteranalysis AT chenyangzou comprehensiveevaluationofchestnutqualitybasedonprincipalcomponentandclusteranalysis AT hanjiang comprehensiveevaluationofchestnutqualitybasedonprincipalcomponentandclusteranalysis |