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Unveiling green-to-reddish-brown color transition mechanisms of star anise (Illicium verum Hook. f.) during drying: From oxidative polymerization of catechin to intelligent quality monitoring

Journal 2026 · IF 7.1 · 发表于 2026 年 8 月 28 日
作者: Silei Zhang,Qingbo Huang,Xiangmin Kuang,Chaofan Guo,Yongli Jiang,Fujie Zhang,Sakamon Devahastin,Junjie Yi
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摘要

Oxidative polymerization of catechins drove the green-to-brown color transition. • Electrolyzed water with ultrasound improved drying efficiency and flavor retention. • More extensive cellular disruption contributed to accelerated moisture migration. • Color, aroma, and moisture changes closely reflected quality changes during drying. • Artificial neural network most accurately predicted moisture and catechin contents. Color is critical determinant of market grade and commercial value of star anise ( Illicium verum Hook. f.). However, mechanisms underlying its characteristic green-to-reddish-brown transformation during processing remain insufficiently understood. The present study investigated color evolution of star anise by comparing ultrasound-assisted electrolyzed water treatment (UEWT) with conventional hot-water blanching (HWB) using framework combining drying kinetics, E-nose, LF-NMR, untargeted LC-MS data as well as machine learning modeling. UEWT significantly enhanced drying efficiency, shortening drying time to 8 h compared to 13 h in case of HWB. LF-NMR spectra revealed that UEWT maintained distinct water mobility profiles, effectively reducing internal mass transfer resistance. UEWT also intensified the chromatic transition, as evidenced by a decline in L* , rapid increases in a* and BI, a greater Δ E , an overall decrease in H , and higher late-stage C* values than those observed under HWB. Catechin depletion, accompanied by formation of oligomeric oxidation products, represents key transformation pathway associated with color development. Among the evaluated machine learning models, ANN-based model exhibited superior performance, achieving high prediction accuracy for both moisture content ( R 2  > 0.98) and catechin content ( R 2  > 0.92). These findings provide new insights into physicochemical basis of color formation and offer practical strategy for real-time quality control during industrial drying of aromatic crops. Download: Download high-res image (294KB) Download: Download full-size image

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