Underlying characteristic aroma of white tea from diverse geographical origins and its prediction.

BACKGROUND

White tea, an agriculturally distinctive product, exhibits significant aroma variations across different regions. Nevertheless, the mechanisms driving these differences, and distinguishing methods suitable for specific origins, have been scarcely reported. In this study, we analyzed the aroma characteristics and volatile components of 100 white tea samples from ten regions, utilizing sensory evaluation, headspace solid-phase microextraction-gas chromatography-mass spectrometry and chemometrics, then established a discrimination model.

RESULTS

A total of 66 volatile compounds were identified, with alcohols and esters being the most important. Linalool and geranyl alcohol were common and relatively abundant volatile compounds across all ten regions, significantly contributing to the aroma characteristics of white tea. The relative content of volatile compounds differed notably across regions, where 33 key compounds, including (E)-2-phenylbut-2-enal and methyl 2,5-octadecadiynoate, were crucial for regional prediction. Employing machine learning algorithms, such as random forest and support vector machine for regional prediction, yielded accuracies of 93.33% and 90.00%, respectively.

CONCLUSION

This study unveils new insights into aroma variation in white tea across different origins, proposing an innovative way of origin determination. © 2025 Society of Chemical Industry.