Evaluation of sample extracting methods of FCSM by Lactobacillus acidophilus based on a UPLC-Q-TOF-MS global metabolomics analysis

Abstract

The study of metabolomics requires extracting as many metabolites as possible from a biological sample. This study aimed to determine the optimal method for the extraction of metabolites from solid-state fermented cottonseed meal (FCSM). The UPLC-Q-TOF-MS global metabolomics technology was used to detect the metabolites in FCSM, and the extraction quantity and extraction efficiency of seven different extraction methods, specifically the WA, 50MeOH, 50MeOHB, 50MeCNB, 80MeOHB, 80MeOH and AMF methods were evaluated. The results showed that the number of VIP metabolites extracted by AMF method are 196 and 184 in ESI+ and ESI− mode respectively, it is the largest number of all exacted methods; and the AMF methods also provided a higher extraction efficiency compared with the other methods, especially in indoleacrylic acid, dl-tryptophan and epicatechin (p < 0.01). As a result, AMF/−4 °C method was identified as the best method for the extraction of metabolites from FCSM by Lactobacillus acidophilus. Our study establishes a technical basis for future metabolomics research of fermented feed.

Comparison of chemical constituents of Bupleurum marginatum var. stenophyllum and Bupleurum chinense DC. using UHPLC–Q‐TOF–MS based on a metabonomics approach

“…Among the several LC–MS combinations, analysis based on ultra‐high performance liquid chromatography–quadrupole time‐of‐flight mass spectrometry (UHPLC–Q‐TOF–MS) technology has advantages of a short turnaround time, high resolution and high sensitivity, enabling the qualitative and quantitative analysis of crude drug components without relying on reference materials. Combining the advantages of high rapid separation efficiency of LC with the high sensitivity and high specificity of MS, this method can analyze metabolites very quickly and accurately and accelerate the discovery process of potential biomarkers (Guo et al, 2017; Wang et al, 2018). Thus, it is the most promising metabonomics technology at present (Zhao et al, 2017).…”