No abstract

Ginseng roots are an important herbal resource worldwide, and the adulteration of ginseng with age is recognized as a serious problem. It is therefore crucial to develop objective criteria or standard protocols for differentiating ginseng root samples according to their cultivation age. The reported study used GC/MS combined with multivariate statistical analysis with variable selection to obtain metabolic profiling and an optimal partial least squares-discriminant analysis (PLS-DA) model for the differentiation of ginseng according to cultivation age. Relative levels of various metabolites, such as amino acids, alcohols, fatty acids, organic acids, and sugars, were measured for various ginseng cultivation ages. Increasing cultivation age resulted in the production of higher levels of panaxynol and panaxydol, which are active polyacetylene compounds in ginseng. In addition, optimized PLS-DA models for the prediction of ginseng age were obtained by selecting variables based on a variable importance in the projection cut-off value of 1.3. Proline, glucaric acid, mannose, gluconic acid, glucuronic acid, myoinositol, panaxydol, and panaxynol are suggested as key and relevant compounds with which to differentiate the age of ginseng samples. The findings of this study suggest that GC/MS-based metabolic profiling can be used to differentiate ginseng samples according to cultivation age.

Guava leaves were classified and the free radical scavenging activity (FRSA) evaluated according to different harvest times by using the (1)H-NMR-based metabolomic technique. A principal component analysis (PCA) of (1)H-NMR data from the guava leaves provided clear clusters according to the harvesting time. A partial least squares (PLS) analysis indicated a correlation between the metabolic profile and FRSA. FRSA levels of the guava leaves harvested during May and August were high, and those leaves contained higher amounts of 3-hydroxybutyric acid, acetic acid, glutamic acid, asparagine, citric acid, malonic acid, trans-aconitic acid, ascorbic acid, maleic acid, cis-aconitic acid, epicatechin, protocatechuic acid, and xanthine than the leaves harvested during October and December. Epicatechin and protocatechuic acid among those compounds seem to have enhanced FRSA of the guava leaf samples harvested in May and August. A PLS regression model was established to predict guava leaf FRSA at different harvesting times by using a (1)H-NMR data set. The predictability of the PLS model was then tested by internal and external validation. The results of this study indicate that (1)H-NMR-based metabolomic data could usefully characterize guava leaves according to their time of harvesting.

The quantitative performance of a simple home-built preparative gas chromatography (prep-GC) arrangement was tested, incorporating a micro-fluidic Deans switch, with collection of the target compound in a deactivated uncoated capillary tube. Repeat injections of a standard solution and peppermint sample were made into the prep-GC instrument. Individual compounds were eluted from the trapping capillary, and made up to constant volume. Chloronaphthalene internal standard was added in some cases. Recovered samples were quantitatively assayed by using GC-MS. Calibration linearity of GC-MS for menthol standard area response against number of injections (2-20 repeat injections) was excellent, giving R(2) of 0.996. For peppermint, menthol correlation over 2-20 repeated injections was 0.998 for menthol area ratio (versus IS) data. Menthone calibration for peppermint gave an R(2) of 0.972. (1) H NMR spectroscopy was conducted on both menthol and menthone. Good correspondence with reference spectra was obtained. About 80 μg of isolated menthol and menthone solute was collected over a sequence of 80 repeat injections from the peppermint sample, as assayed by 600 MHz (1) H NMR analysis (∼100% recovery for menthol from peppermint). A procedure is proposed for prediction of number of injections required to acquire sufficient material for NMR detection.

Metabolic syndrome (MetS) is associated with psoriasis, but it remains unclear whether risk of psoriasis remains in patients whose MetS diagnosis changes. To assess the relationship between risk of psoriasis and changes in MetS components. We obtained data from the National Health Insurance Service of Korea and divided the participants into four groups: individuals without MetS (control); individuals with MetS in 2009, but without MetS in 2012 (pre-MetS); individuals without MetS in 2009, but with newly diagnosed MetS in 2012 (post-MetS); and individuals with MetS during the 2009–2012, period (continuous-MetS). We calculated the risk of psoriasis for each group. Risk of psoriasis was similar in the control and pre-MetS groups but was significantly higher in the post-MetS group (hazard ratio [HR], 1.08; 95% confidence interval [CI], 1.04–1.12) and in the continuous-MetS group (HR, 1.11; 95% CI, 1.07–1.15) than in the control group. Among MetS components, waist circumference showed the strongest association with psoriasis, followed by high-density lipoprotein and triglyceride levels. Risk of psoriasis was higher in patients with continuous- or post-MetS than in those with pre-MetS (regardless of prior MetS status).