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February 5, 2026FoodsOpen Access

Prediction of Quality Substance Content of Hakka Stir-Fried Green Tea Based on Multiple Features of Near-Infrared Spectroscopy

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Authors

YQYipeng QiuTTTao TangJGJiacheng Guo

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Overview

Demonstrates the prediction of biochemical components in Hakka stir-fried green tea, suggesting rapid detection methods for quality assessment.

Key Points

  • This research aims to establish prediction models for key biochemical components in Hakka stir-fried green tea using near-infrared spectroscopy data.
  • Collected 171 HSGT samples for analysis.
  • Utilized near-infrared spectroscopy (NIRS) to assess biochemical components.
  • Preprocessed NIRS data and extracted features using various techniques.
  • Developed ridge regression and partial least squares regression models for prediction.
  • The combined feature model (CARS + AFD + BC) showed the best overall performance.
  • The ridge regression model provided accurate predictions for theanine, tea polyphenols, and soluble sugar.
  • The partial least squares regression model performed best for predicting water extract.

Cite This Study

Qiu et al. (2026) studied this question.

synapsesocial.com/papers/69843553f1d9ada3c1fb3ff3https://doi.org/10.3390/foods15030531
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