Integrating Hyperspectral Data and Deep Learning for Non-Destructive Prediction of Tea Quality Parameters Across Different Physical States of Tea Leaves and Growth Periods.

Plants (Basel) · 2026 · PMC13074506 · PMID 41977730

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highly significantp < 0.001actually significant

One-way analysis of variance (ANOVA) showed that season had a highly significant effect on tea polyphenol content (F(2,165) = 88.065, p < 0.001), as well as on catechin content (F(2,161) = 29.358, p < 0.001).

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a decreasing trendno p-value reported

Overall, both tea polyphenol and catechin contents exhibited a decreasing trend from summer to autumn and further to spring( Figure 1 ). 2.2.

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