Zhipeng Liu,Kangzhuo Yang,Zhanglan He,Dong Zhao,Jia Zheng,Michael C. Qian
Whisky, brandy, rum, and Chinese baijiu are popular distilled spirits globally, and the volatile components play an essential role in the aroma of these distilled liquors. Volatile compounds in whisky, brandy, rum, and three main aroma types of Chinese baijiu (strong, light, and sauce) were investigated using comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC–TOFMS). Two significant variable detection approaches, the variable importance in the projection (VIP) and nonparametric test ( Mann–Whitney U test ), were compared to determine the volatile markers among these samples. It was found that the VIP model was more efficient in screening significant variants than the U test . A total of 117 common markers were selected by both the VIP and U test methods with potential aroma contributions. Esters and acids were the main aroma markers for baijiu, diethyl esters were the main aroma markers for brandy, whereras pyrazines, lactones, and furans were the main aroma markers for whisky. Based on the chosen markers, different unknown distilled liquors were successfully classified in the model validation. This study provided a feasible methodology for spirit sample speculation based on volatile composition obtained by GC×GC-TOFMS.
Aims Status epilepticus (SE) is the most common neurological emergency in pediatric patients. This study aimed to screen for prognostic biomarkers of SE in the cerebrospinal fluid (CSF) using metabolomics. Methods Ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UPLC-QTOF-MS) was conducted to identify prognostic biomarkers in CSF metabolomics by comparing the poor outcome group ( N = 13) with the good outcome group ( N = 15) of children with SE. Differentially expressed metabolites were identified using Mann–Whitney U test corrected by Benjamini-Hochberg and partial least squares discriminant analysis (PLS-DA). Results The PLS-DA model identified and validated significant metabolic differences between the poor and good outcome groups of children with SE (PLS-DA with R 2 Y = 0.992 and Q 2 = 0.798). A total of 49 prognosis-related metabolites were identified. Of these metabolites, 20 including glutamyl-glutamine, 3-iodothyronamine, and L-fucose had an area under the curve (AUC) ≥ 80% in prognostic prediction of SE. The logistic regression model combining glutamyl-glutamine and 3-iodothyronamine produced an AUC value of 0.976, with a sensitivity of 0.863 and specificity of 0.956. Pathway analysis revealed that dysregulation of the citrate cycle (TCA) and arginine biosynthesis may contribute to poor SE prognosis. Conclusions This study highlighted the prognosis-related metabolomic disturbances in the CSF of children with SE and identified potential prognostic biomarkers. A prognostic prediction model combining glutamyl-glutamine and 3-iodothyronamine with high predictive value was established.
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