Optimization of the processing technology of schizonepetae herba carbonisata using response surface methodology and artificial neural network and comparing the chemical profiles between raw and charred schizonepetae herba by UPLC-Q-TOF-MS

采用响应面法和人工神经网络优化荆芥炭的加工工艺,并用UPLC-Q-TOF-MS比较生荆芥和炭的化学成分

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作者:Xiaoying Ding, Huaiyou Wang, Hengyang Li, Tao Wang, Shenghui Hao, Wenjie Li, Chengyue Wang, Lei Wang, Yuguang Zheng, Qi An, Long Guo, Dan Zhang

Abstract

In this study, response surface methodology (RSM) and artificial neural network (ANN) were used to predict and validate the optimal processing method of Schizonepetae Herba Carbonisata (SHC). The highest overall desirability (OD) value of the total flavonoids content (TFC), total tannin content (TTC), and adsorption capacity (AC) were used as response values. The optimal processing technology processing time lasted 10 min at a processing temperature of 178 °C and the herbs/machine had a volume of 77 g/5 L. The Ultra Performance Liquid Chromatography/Quadrupole Time-of-Flight Mass Spectrometry (UPLC-Q-TOF-MS), combined with chemometrics, was used to investigate the changes of compounds in Schizonepetae Herba (SH) before and after being charred. A total of 104 compounds were tentatively identified in SH and 83 in SHC. Fifteen differential compounds were found between by chemometrics SH and SHC. Altogether, our findings can provide a practical approach to the processing technology of carbonizing by stir-frying SH.

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