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

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.

Keywords

• Schizonepetae herba
• Schizonepetae herba carbonisata
• Response surface methodology
• Artificial neural network
• Ultra performance liquid chromatography/quadrupole time-of-flight mass spectrometry