Rice is one of the most important food crops in the world. Metabolite composition in rice seeds varies significantly depending on genetic variety, climatic alternation and agricultural practice. Metabolomics is a powerful tool to reveal the metabolic response of rice to various conditions. In this work, a rice seed sample-directed pseudotargeted metabolomics method was first established and validated based on ultra high performance liquid chromatography with triple quadrupole mass spectrometry in the multiple reaction monitoring mode. A total of 749 and 617 ion pairs in positive and negative modes were achieved, respectively. Among them, about 200 metabolites were identified or tentatively identified. The developed method showed better linearity and repeatability than those of non-targeted metabolomics method. Good intra-day and inter-day precisions, recoveries and wide linear range were also obtained. Furthermore, the method was applied for the investigation of metabolic variation of rice seeds with two wild cultivars and their transgenic lines that were grown in two locations. Principal component analysis indicated that the effects of cultivar and location on metabolic variations were far more than those of gene modification. The nonparametric Mann–Whitney U test revealed that most metabolites were influenced by cultivar, location and gene modifications together.

Cured tobacco is an important plant material. Component studies are a big challenge for its significantly diverse chemical properties and vastly different concentrations. In this work, liquid chromatography with quadrupole time-of-flight mass spectrometry was used to perform a metabolomics study of cured tobacco owing to its efficient separation and detection of semipolar metabolites. A solvent of methanol/water (8:2, v/v) and 30 min of ultrasound time were found to be optimal to perform extraction. 95, 92, and 93% of metabolite features had within 20% of coefficient of variation for repeatability, intraday and interday precision analysis, respectively, indicating a good stability of the method developed. 113 metabolites were identified in cured tobacco based on accurate mass, retention time, and MS/MS fragments. The developed method was applied to a metabolomics study of cured tobacco from three growing regions. Forty three metabolites were found to be contributed to the classification. It is shown that the developed method can be applied to metabolomics analysis of plant materials.

Amino acids are one of the most important metabolites of organisms. They play an important role in plant growth, development, and product quality. A method based on RP ultra-performance LC with single quadrupole MS and 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate precolumn derivatization was developed for the analysis of free amino acids in flue-cured tobacco leaves. Unlike the corresponding UV detection method, this method avoids matrix interference of complicated tobacco components, and the quantitative accuracy and resolution were improved. Twenty free amino acids were detected in flue-cured tobacco leaves. The method showed a good linearity with correlation coefficients of 0.9966–0.9998. The LODs for derivatized amino acids were 0.2–9.7 fmol/μL. Good repeatability with an RSD of 2.5–8.6% and satisfactory intra- and interday precisions were obtained. The developed method was used to investigate free amino acids in flue-cured tobacco leaves in China. The effects of aroma type, variety, and growing regions on free amino acids were investigated. The results showed that free amino acids in tobacco were affected by growing regions and varieties.

Discovery of differential metabolites is the focus of metabonomics study. It has very important applications in pathogenesis and disease classification. The aim of this work is to identify differential metabolites for classifying the patients with hepatocellular carcinoma, cirrhosis and hepatitis based on metabolic profiling data analyzed by gas chromatography-time of flight mass spectrometry. A two-stage feature selection algorithm, F-SVM, combining F-score in analysis of variance and support vector machine (SVM), was applied in discovering discriminative metabolites for three different types of liver diseases. The results show that the accuracy rate of the double cross-validation was 73.68±2.98%. 22 important differential metabolites selected by F-SVM were identified and related pathophysiological process of liver diseases was set forth. We conclude that F-SVM is quite feasible to be applied in the selection of biologically relevant features in metabonomics.

Metabolomics influences many aspects of life sciences including microbiology. Here, we describe the systematic optimization of metabolic quenching and a sample derivatization method for GC–MS metabolic fingerprint analysis. Methanol, ethanol, acetone, and acetonitrile were selected to evaluate their metabolic quenching ability, and acetonitrile was regarded as the most efficient agent. The optimized derivatization conditions were determined by full factorial design considering temperature, solvent, and time as parameters. The best conditions were attained with N,O-bis(trimethylsiyl) trifluoroacetamide as derivatization agent and pyridine as solvent at 75°C for 45 min. Method validation ascertained the optimized method to be robust. The above method was applied to metabolomic analysis of six different strains and it is proved that the metabolic trait of an engineered strain can be easily deduced by clustering analysis of metabolic fingerprints.

Comprehensive 2-D GC (GC×GC) coupled with TOF MS or flame ionization detector (FID) was employed to characterize and quantify the chemical composition of volatile oil in the radixes of Panax ginseng C. A. Mey. (ginseng) at different ages. Thirty-six terpenoids were tentatively identified based on the MS library search and retention index in a ginseng sample at the age of 3 years. An obvious group-type separation was obtained in the GC×GC-TOF MS chromatogram. The data collected by GC×GC-FID were processed using a principal component analysis (PCA) method to classify the samples at different ages. The compounds responsible for the significant differentiation among samples were defined. It was found that the relative abundances of α-cadinol, α-bisabolol, thujopsene, and n-hexadecanoic acid significantly rise with the increase in age.

In the present study, we developed a novel online hydrophilic interaction chromatography (HILIC)-RPLC separation system for simultaneous separation of both hydrophilic and hydrophobic solutes in a complex sample with one injection. A HILIC Si column and a C18 column were hyphenated with an interface including two electronic 2-position valves, a solvent pump, and a solute transfer column. By using column-switching technique, the nonretained hydrophobic solutes were eluted out of the HILIC column and transferred into the C18 column to perform further separation. The hydrophilic solutes were separated on the HILIC column at the same time. One detector was equipped for each column to record the individual chromatograms. By separating a standard mixture and a traditional Chinese medicine (TCM) extract, the developed HILIC-RPLC system demonstrates its potential for “entire-components” separation of complex samples with improved peak capacity and throughput compared with the common single-column LC.

A method for simultaneous determination of seven nonvolatile organic acids (NVOAs), five semivolatile organic acids (SVOAs), and twelve volatile organic acids (VOAs) in tobacco by synchronous SIM (Selected Ion Monitoring)-scan mode GC–MS was developed. The collection of NVOAs and SVOAs data was performed in full-scan mode, and that of VOAs data was carried out with SIM. Recoveries of NVOAs and SVOAs varied from 90.0 to 103.0%, and RSDs were less than 4.0%; recoveries of VOAs ranged from 89.5 to 99.3%, and their RSDs were less than 3.0%. Twelve tobacco samples were analyzed by the described method, and the results show that the method is applicable for the simultaneous determination of VOAs, NVOAs, and SVOAs in tobacco.