Machine learning-based prediction of total phenolic and flavonoid in horticultural products

Kusumiyati, Kusumiyati; Asikin, Yonathan

doi:10.1515/opag-2022-0163

Cited by 5 publications

(1 citation statement)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results revealed low accuracy in predicting protein and total flavonoids content, which may be due to the limited sample size, and no appropriate methods were applied to address this problem. Kusumiyati et al [16] used near-infrared spectroscopy (NIRS) combined with machine learning to predict the total phenolic content (TPC) and total flavonoids content (TFC) in several horticultural products and attained positive outcomes. Nonetheless, currently, there is no available prediction of flavonoids content in ancient tree sun−dried green tea.…”

Section: Introductionmentioning

confidence: 99%

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

Li,

Wu,

Wang

et al. 2024

Agriculture

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%