Content area

Abstract

Fresh wet rice noodles is one of the delicacies for young and old in East and Southeast Asia. Its main material is rice. However, there are many varieties of rice. Not all rice is suitable for processing into fresh wet rice noodles. In this study, we used 22 rice varieties as raw materials to produce fresh wet rice noodles. Multiple analysis methods, including correlation analysis, principal component analysis, affiliation function analysis, cluster analysis, stepwise regression, and gray correlation analysis, were used to evaluate and classify the quality of rice and fresh wet rice noodles. Identification indices and measurement standards were determined for the evaluation of the quality of the fresh wet rice noodles. The results showed that the 22 rice varieties had large coefficients of variation for rice gel consistency, amylose content, broken noodle rate, and adhesiveness. There were highly significant correlations between all of the individual indices in terms of fresh wet rice noodle quality. Four principal components (composite decision indices) were extracted using principal component analysis, and the cumulative contribution reached 91.442%. In this study, the composite decision index F3 referred to the amylose content and index F4 was the gel consistency. The results of the principal component composite scores and affiliation functions showed that the quality of the rice and fresh wet rice noodles made from Zhuliangyou 4026 and Yuliangyou 22 was excellent. On the basis of the D-value of the combined evaluation of rice quality and fresh wet rice noodle quality, the 22 rice varieties were clustered into four categories at a distance of 0.20. By combining stepwise regression analysis, correlation analysis, and gray correlation analysis, it was determined that amylose content and gel consistency could be used as rice quality indices for evaluating the quality of fresh wet rice noodles. Moreover, the screening conditions for varieties with an amylose content of between 20% and 25% and a gel consistency of less than 40 mm were found to be suitable for fresh wet rice noodle processing. Therefore, multivariate statistical analysis can be an effective means of evaluating flour rice. This study provides a foundation for the standardization, scalability, and industrialization of fresh wet rice noodle production.

Details

1009240
Title
Key quality traits in rice grains determining fresh wet rice noodles quality: a multivariate statistical methods across varieties
Author
Li, Yufei 1 ; Liu, Ling 1 ; Shu, Xin 1 ; Zhu, Fan 1 ; Xiang, Xinpeng 1 ; Wu, Chenhao 1 ; Tang, Bocheng 1 ; Hu, Yajun 1 ; Chen, Guanghui 2 ; Wang, Yue 3 

 Department of Agronomy, College of Agronomy, Hunan Agricultural University, Changsha, China 
 Department of Agronomy, College of Agronomy, Hunan Agricultural University, Changsha, China, Yuelu Mountain Laboratory of Hunan Province, Hunan Agricultural University, Changsha, China 
 The Key Laboratory of Crop Germplasm Innovation and Resource Utilization of Hunan Province, Hunan Agricultural University, Changsha, China 
Publication title
Volume
16
First page
1685290
Number of pages
20
Publication year
2025
Publication date
Oct 2025
Section
Crop and Product Physiology
Publisher
Frontiers Media SA
Place of publication
Lausanne
Country of publication
Switzerland
Publication subject
e-ISSN
1664462X
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-10-15
Milestone dates
2025-08-13 (Recieved); 2025-09-16 (Accepted)
Publication history
 
 
   First posting date
15 Oct 2025
ProQuest document ID
3273797856
Document URL
https://www.proquest.com/scholarly-journals/key-quality-traits-rice-grains-determining-fresh/docview/3273797856/se-2?accountid=208611
Copyright
© 2025. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-12-18
Database
ProQuest One Academic