Prediction Model of Flavonoids Content in Ancient Tree Sun−Dried Green Tea under Abiotic Stress Based on LASSO−Cox

Lei Li, Yamin Wu, Houqiao Wang, Junjie He, Qiaomei Wang, Jiayi Xu, Yuxin Xia, Wenxia Yuan, Shuyi Chen, Lin Tao, Xinghua Wang () and Baijuan Wang ()
Additional contact information
Lei Li: College of Tea Science, Yunnan Agricultural University, Kunming 650201, China
Yamin Wu: College of Tea Science, Yunnan Agricultural University, Kunming 650201, China
Houqiao Wang: College of Tea Science, Yunnan Agricultural University, Kunming 650201, China
Junjie He: College of Tea Science, Yunnan Agricultural University, Kunming 650201, China
Qiaomei Wang: College of Tea Science, Yunnan Agricultural University, Kunming 650201, China
Jiayi Xu: College of Tea Science, Yunnan Agricultural University, Kunming 650201, China
Yuxin Xia: College of Mechanical and Electrical Engineering, Yunnan Agricultural University, Kunming 650201, China
Wenxia Yuan: College of Tea Science, Yunnan Agricultural University, Kunming 650201, China
Shuyi Chen: College of Tea Science, Yunnan Agricultural University, Kunming 650201, China
Lin Tao: Pu’er Wenbang Tea Co., Ltd., Pu’er 666500, China
Xinghua Wang: College of Tea Science, Yunnan Agricultural University, Kunming 650201, China
Baijuan Wang: College of Tea Science, Yunnan Agricultural University, Kunming 650201, China

Agriculture, 2024, vol. 14, issue 2, 1-17

Abstract: To investigate the variation in flavonoids content in ancient tree sun–dried green tea under abiotic stress environmental conditions, this study determined the flavonoids content in ancient tree sun−dried green tea and analyzed its correlation with corresponding factors such as the age, height, altitude, and soil composition of the tree. This study uses two machine−learning models, Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression, to build a predictive model based on the selection of effective variables. During the process, bootstrap was used to expand the dataset for single−factor and multi−factor comparative analyses, as well as for model validation, and the goodness−of−fit was assessed using the Akaike information criterion ( AIC ). The results showed that pH, total potassium, nitrate nitrogen, available phosphorus, hydrolytic nitrogen, and ammonium nitrogen have a high accuracy in predicting the flavonoids content of this model and have a synergistic effect on the production of flavonoids in the ancient tree tea. In this prediction model, when the flavonoids content was >6‰, the area under the curve of the training set and validation set were 0.8121 and 0.792 and, when the flavonoids content was >9‰, the area under the curve of the training set and validation set were 0.877 and 0.889, demonstrating good consistency. Compared to modeling with all significantly correlated factors ( p < 0.05), the AIC decreased by 32.534%. Simultaneously, a visualization system for predicting flavonoids content in ancient tree sun−dried green tea was developed based on a nomogram model. The model was externally validated using actual measurement data and achieved an accuracy rate of 83.33%. Therefore, this study offers a scientific theoretical foundation for explaining the forecast and interference of the quality of ancient tree sun−dried green tea under abiotic stress.

Keywords: flavonoids content; LASSO; nomogram; Cox regression; prediction model (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/2/296/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/2/296/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:2:p:296-:d:1337790

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().