Change Pattern of Ergosterol during Mildew of Polygonati Rhizoma and Establishment of Rapid Prediction Model Based on Electronic Nose
Objective:Ergosterol is one of the most important indicators in the process of mildew.It is of great significance to explore a rapid determination method of ergosterol.Methods:In this paper,artificial simulated culture was used to obtain Polygonati Rhizoma samples with different mildew degrees.The traditional character identification method was used to observe,identify,and classify the external properties of Polygonati Rhizoma.The content of ergosterol was determined by ultra-high performance liquid chromatography.The odor was quantified with an electronic nose.Results:35 samples of Polygonati Rhizoma with different mildew degrees were prepared.According to the sensory evaluation,Polygonati Rhizoma samples were divided into three groups:no mildew,slight mildew,and noticeable mildew.The ergosterol in 35 samples content ranged from 0-90.435 6 mg·kg-1,and the content of ergosterol increased with the worsening of the mildew degree of Polygonati Rhizoma.The absolute value of the maximum response value of electronic nose S1-S16 sensors decreased with the worsening of the mildew degree of Polygonati Rhizoma,but the maximum response value of S18 sensor increased with the increase in mildew degree of Polygonati Rhizoma.Correlation analysis showed that ergosterol content was significantly negatively correlated with the response values of S1 and S7-S16 sensors and positively correlated with the response values of S2-S6,S17,and S18 sensors.A regression equation was established as follows:Y=67.577-78.453X(r=0.634),in which Y stands for ergosterol content,and X refers to the maximum response value of sensor PA/2.Conclusion:The odor of Polygonati Rhizoma is closely related to its intrinsic chemical component,ergosterol.The electronic nose technique combined with regression analysis is promising to provide a feasible and effective method for preliminary prediction of ergosterol content in Polygonati Rhizoma.
万方数据
维普
