摘要
目的 探索一种方便、快捷的方法来判断普洱茶的发酵程度。方法 使用目标容量的发酵罐,对普洱茶原料进行接种发酵,收集多组茶叶pH,没食子酸(gallicacid,GA)和茶褐素(theabrownin,TB)含量的动态变化数据,基于上述数据分别进行线性拟合并组合,得到TB含量随茶叶pH变化的回归模型。结果 在普洱茶发酵过程的特定阶段,分别建立了TB-GA、GA-茶叶pH的一元线性回归模型;然后将两个一元线性回归模型线性组合,最终建立了TB含量与茶叶pH的回归模型,只需根据测得的茶叶pH,即可快速对普洱茶发酵体系中TB含量进行预测,从而判定普洱茶发酵程度。结论 基于pH定量高效判断普洱茶发酵程度,保证了发酵过程参数调控的时效性和准确性,使产品稳定生产。
Objective To explore a convenient and rapid method to determine the degree of fermentation of Pu-erh tea. Methods Raw materials of Pu-erh tea were inoculated and fermented with a fermenting tank with target capacity, and the dynamic change data of pH, gallic acid(GA) and theabrownin(TB) content were collected from several groups of tea. Based on the above data, linear fitting combination was carried out respectively to obtain the regression model of the change of TB content with pH of tea. Results In the specific stage of fermentation of Pu-erh tea, the unary linear regression models of TB-GA and GA-tea pH were established, respectively. Then the linear combinations of the 2 unary linear regression models were used to establish the regression model of TB content and pH of tea. Only according to the measured pH of tea, the TB content in the fermentation system of Pu-erh tea could be predicted quickly, to determine the fermentation degree of Pu-erh tea. Conclusion Based on pH quantification to efficiently determine the degree of fermentation of Pu-erh tea, it ensures the timeliness and accuracy of fermentation process parameter regulation and enables stable production of products.
作者
杨子玺
朱圆敏
谢燕霞
陈雪敏
余龙江
YANG Zi-Xi;ZHU Yuan-Min;XIE Yan-Xia;CHEN Xue-Min;YU Long-Jiang(College of Life Science and Technology,Huazhong University of Science and Technology,Wuhan 430074,China;Key Laboratory of Molecular Biophysics,Ministry of Education,Wuhan 430074,China;Hubei Engineering Research Center for Both Edible and Medicinal Resources,Wuhan 430074,China)
出处
《食品安全质量检测学报》
CAS
北大核心
2023年第1期74-80,共7页
Journal of Food Safety and Quality
基金
中央高校基本科研经费项目(2019kfyXKJC049、2020kfyXJJS120)
湖北省食药两用资源工程技术研究中心项目(2018BEC463)
华中科技大学研究生创新基金项目(YCJJ202203005)。
关键词
普洱茶
发酵程度
PH
没食子酸
茶褐素
回归模型
预测
Pu-erh tea
fermentation degree
pH
gallic acid
theabrownin
regression model
predict