scholarly journals Untargeted Metabolomics and Targeted Quantitative Analysis of Temporal and Spatial Variations in Specialized Metabolites Accumulation in Poria cocos (Schw.) Wolf (Fushen)

2021 ◽  
Vol 12 ◽  
Author(s):  
Mei Yang ◽  
Yujiao Zhao ◽  
Yuejian Qin ◽  
Rui Xu ◽  
Zhengyang Yang ◽  
...  

Poria cocos (Schw.) Wolf is a saprophytic fungus that grows around the roots of old, dead pine trees. Fushen, derived from the sclerotium of P. cocos but also containing a young host pine root, has been widely used as a medicine and food in China, Japan, Korea, Southeast Asian countries, and some European countries. However, the compound variations at the different growth periods and in the different parts of Fushen have not previously been investigated. In this study, an untargeted metabolomics approach based on ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS) and targeted quantitative analysis was utilized to characterize the temporal and spatial variations in the accumulation of specialized metabolites in Fushen. There were 119 specialized metabolites tentatively identified using the UPLC-Q/TOF-MS. The nine growth periods of Fushen were divided into four groups using partial least squares discrimination analysis (PLS-DA). Four different parts of the Fushen [fulingpi (FP), the outside of baifuling (BO), the inside of baifuling (BI), and fushenmu (FM)] were clearly discriminated using a PLS-DA and orthogonal partial least squares discrimination analysis (OPLS-DA). Markers for the different growth periods and parts of Fushen were also screened. In addition, the quantitative method was successfully applied to simultaneously determine 13 major triterpenoid acids in the nine growth periods and four parts. The quantitative results indicated that the samples in January, March, and April, i.e., the late growth period, had the highest content levels for the 13 triterpenoid acids. The pachymic acid, dehydropachymic acid, and dehydrotumulosic acid contents in the FM were higher than those in other three parts in March, whereas the poricoic acid B, poricoic acid A, polyporenic acid C, dehydrotratrametenolic acid, dehydroeburicoic acid, and eburicoic acid in FP were higher beginning in October. These findings reveal characteristics in temporal and spatial distribution of specialized metabolites in Fushen and provide guidance for the identification of harvesting times and for further quality evaluations.

2019 ◽  
Vol 9 (24) ◽  
pp. 5336 ◽  
Author(s):  
Qi XIA ◽  
Lei-ming YUAN ◽  
Xiaojing CHEN ◽  
Liuwei MENG ◽  
Guangzao HUANG

Methanol gasoline blends are a more economical, and environmentally friendly fuels than gasoline alone, and are widely used in the transportation industry. The content of methanol in methanol gasoline plays an important role in ensuring the quality of gasoline. In some solutions, due to the shortage of energy and illegal profits, the problem of gasoline adulteration and its fineness, has received more and more attention, which would seriously affect the operating condition and service life of internal combustion engines. Therefore, it is very important to identify the correct level of gasoline. However, the traditional detection method is complex and time-consuming. To this end, the feasibility of using attenuated total reflectance Fourier transform infrared (ATR-FTIR) methods coupled with chemometrics methods were investigated to quantitatively and qualitatively analyze methanol gasoline. The qualitative analysis result of partial least squares discriminant analysis (PLS-DA) obtained 100% and 98.66% accuracy in the calibration set and the prediction set, respectively. As for quantitative analysis; two regression algorithms of partial least squares regression (PLSR) and the least square support vector machine (LS-SVM), as well as two variables selection methods of the successive projections algorithm (UVE) competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE) were combined to establish the quantitative model. By comparing the performance of the optimal models; the UVE-PLSR model performed best with a residual predictive deviation (RPD) value of 6.420. The qualitative and quantitative analysis results demonstrate the feasibility of using ATR-FTIR spectra to detect the methanol in methanol gasoline. It is believed that the promising IR spectra will be widely used in gasoline energy quality control in the further.


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