ATP生物发光技术结合统计分析方法应用于柴胡饮片微生物污染快速判断

Rapid determination of microbial contamination of Bupleurum chinense DC.decoction slices by ATP bioluminescence technology combined with statistical analysis methods

本研究聚焦中药饮片微生物质量控制,针对传统培养法检测速度慢和无法检测不可培养微生物等缺陷建立一个基于ATP生物发光技术结合统计分析方法快速预判和定量检测柴胡饮片中污染需氧菌总数(TAMC)和霉菌酵母菌总数(TYMC)的新方法。基于优化的ATP生物发光检测体系,可实现对大肠埃希菌、枯草芽孢杆菌和金葡菌纯菌菌液的准确检测,检测限分别为47.86、89.13和1862.09 CFU·mL^(-1),检测时间为6.5 h,检测成本低。分别以10%和20%误判率划定TAMC预警上限和预警下限,以20%误判率划定TYMC预警限,提出的十字交叉法可对柴胡饮片微生物污染数量进行快速预判。构建的偏最小二乘回归(PLSR)模型可以对柴胡饮片微生物污染数量进行准确定量,其中,对TAMC总量的最优PLSR预测模型的校正系数(R2)为0.826,校正集均方根误差(RMSE_(E))为0.468,交叉验证集均方根误差(RMSE_(CV))为0.465,对TYMC总量的预测模型中R2为0.778,RMSE_(E)为0.543,RMSE_(CV)为0.541。本研究旨在建立一套中药材和中药饮片微生物限度快速检测方法和预测模型,为中药产品微生物质量过程控制提供一项更为便捷和灵敏的检测技术。

This study focuses on the microbial quality control of the Chinese herbal decoction pieces.In view of the shortcomings of traditional culture methods such as slow detection speed and inability to detect unculturable microorganisms,a new method based on ATP bioluminescence technology combined with statistical analysis methods was established to rapidly predict and quantitatively detect the total aerobic microbial count(TAMC)and total yeast and mold count(TYMC)contaminated Bupleurum chinense DC.decoction pieces.Based on the optimized ATP bioluminesence detection system,accurate detection of pure bacterial solution of Escherichia coli,Bacillus subtilis and Staphylococcus aureus can be achieved,with detection limits of 47.86,89.13 and 1862.09 CFU·mL^(-1),respectively.The detection time was 6.5 h,and the detection cost was as low as 2 yuan/time.The upper and lower warning limits of TAMC were determined by the misjudgment rates of 10%and 20%,respectively.And the warning limit of TYMC was determined by the misjudgment rate of 20%.The proposed crossing method could quickly predict the amount of microbial contamination in Bupleurum chinense DC.decoction pieces.The constructed partial least squares regression(PLSR)model could accurately quantify the quantity of microbial contamination in Bupleurum chinense DC.decoction pieces.The optimal PLSR prediction model for TAMC had a correction coefficient(R)of 0.826,a root mean square error of correction set(RMSE_(E))of 0.468 and a root mean square error of cross-validation set(RMSE_(CV))of 0.465.The R2,RMSE_(E),and RMSE_(CV) in the prediction model of TYMC were 0.778,0.543 and 0.541,respectively.The aim of this study is to establish a kind of rapid detection method and prediction models for the microbial limit of traditional Chinese medicine and Chinese herbal decoction pieces,and to provide a more convenient and sensitive detection technology for the microbial quality process control of traditional Chinese medicine products.