GC-MS Analysis and Volatile Profile Comparison for the Characteristic Smell from Liang-wai Gan Cao (Glycyrrhiza uralensis) and Honey-Roasting Products.

The characteristic smell of Liang-wai Gan Cao (Glycyrrhiza uralensis) and honey-roasting products was comprehensively analyzed using gas chromatography-mass spectrometry (GC-MS). Steam distillation and headspace solid-phase microextraction (HS-SPME) were used to extract volatile organic compounds (VOCs). Multiple fibers of SPME may reflect the samples' comprehensive information to the greatest extent, depending on their chemical characters. After chemometric resolution and spectra interpretation, many aroma compounds could be identified from GC-MS data. As a result, principal component analysis was set for the differentiation of several G. uralensis samples in different regions, and some important peaks could be found. Next, VOCs' profiles of honey-roasting products suggested that the flavors could be influenced by honey and pharmaceutical technologies.

Exploring metabolic syndrome serum profiling based on gas chromatography mass spectrometry and random forest models.

Metabolic syndrome (MetS) is a constellation of the most dangerous heart attack risk factors: diabetes and raised fasting plasma glucose, abdominal obesity, high cholesterol and high blood pressure. Analysis and representation of the variances of metabolic profiles is urgently needed for early diagnosis and treatment of MetS. In current study, we proposed a metabolomics approach for analyzing MetS based on GC-MS profiling and random forest models. The serum samples from healthy controls and MetS patients were characterized by GC-MS. Then, random forest (RF) models were used to visually discriminate the serum changes in MetS based on these GC-MS profiles. Simultaneously, some informative metabolites or potential biomarkers were successfully discovered by means of variable importance ranking in random forest models. The metabolites such as 2-hydroxybutyric acid, inositol and d-glucose, were defined as potential biomarkers to diagnose the MetS. These results obtained by proposed method showed that the combining GC-MS profiling with random forest models was a useful approach to analyze metabolites variances and further screen the potential biomarkers for MetS diagnosis.