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Source and Route of Pyrrolizidine Alkaloid Contamination in Tea Samples

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Tea quality estimation based on multi-source information from leaf and soil using machine learning algorithm.

Bin Yang¹, Jie Jiang¹, Huan Zhang¹

¹College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.

Food Chemistry: X

|December 25, 2023

Summary

Soil and tea leaf mineral nutrients significantly impact tea quality. Predictive models, particularly Random Forest, accurately estimate key tea compounds like EGCG and amino acids, highlighting the value of multi-source data for tea quality assessment.

Keywords:

Biochemical component Mineral element Multiple linear regression Random forest Tea

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Area of Science:

Agricultural Science
Food Chemistry
Plant Nutrition

Background:

Tea quality is influenced by mineral nutrients in soil and tea plants.
Understanding these relationships is crucial for optimizing tea production and quality.
Previous studies have explored nutrient impacts, but comprehensive predictive modeling is needed.

Purpose of the Study:

To quantify the relationship between soil and leaf mineral elements and key tea quality components.
To develop and compare predictive models for tea quality constituents using multi-source mineral data.
To assess the accuracy of different models in predicting EGCG, amino acids, tea polyphenols, caffeine, and soluble sugars.

Main Methods:

Collected soil and tea leaf samples from 160 plantations for 'Baiyeyihao' and 'Huangjinya' cultivars.
Analyzed 16 soil mineral elements, 16 leaf nutrient elements, and 10 tea quality compositions.
Applied linear regression, multiple linear regression (MLR), and Random Forest (RF) models for prediction.

Main Results:

Mineral element data from soil and leaves improved the accuracy of tea quality predictions.
Random Forest (RF) model showed high accuracy for EGCG, amino acids, tea polyphenols, and caffeine.
Multiple Linear Regression (MLR) performed well for predicting soluble sugars.
Multi-source mineral information provided superior predictive accuracy compared to individual elements.

Conclusions:

Mineral composition of soil and tea plants are key determinants of tea quality.
Advanced predictive models like RF and MLR can accurately estimate major tea quality components.
Integrating multi-source mineral data enhances the prediction of biochemical tea constituents, offering valuable insights for tea cultivation and quality control.