Wai Siang Law

Metabonomics investigation of human urine after ingestion of green tea with gas chromatography/mass spectrometry, liquid chromatography/mass spectrometry and 1H NMR spectroscopy

A method using gas chromatography/mass spectrometry (GC/MS), liquid chromatography/mass spectrometry (LC/MS) and (1)H NMR with pattern recognition tools such as principle components analysis (PCA) was used to study the human urinary metabolic profiles after the intake of green tea. From the normalized peak areas obtained from GC/MS and LC/MS and peak heights from (1)H NMR, statistical analyses were used in the identification of potential biomarkers. Metabolic profiling by GC/MS provided a different set of quantitative signatures of metabolites that can be used to characterize the molecular changes in human urine samples. A comparison of normalized metabonomics data for selected metabolites in human urine samples in the presence of potential overlapping peaks after tea ingestion from LC/MS and (1)H NMR showed the reliability of the current approach and method of normalization. The close agreements of LC/MS with (1)H NMR data showed that the effects of ion suppression in LC/MS for early eluting metabolites were not significant. Concurrently, the specificity of detecting the stated metabolites by (1)H NMR and LC/MS was demonstrated. Our data showed that a number of metabolites involved in glucose metabolism, citric acid cycle and amino acid metabolism were affected immediately after the intake of green tea. The proposed approach provided a more comprehensive picture of the metabolic changes after intake of green tea in human urine. The multiple analytical approach together with pattern recognition tools is a useful platform to study metabolic profiles after ingestion of botanicals and medicinal plants.

Systematic optimisation of coupled microwave-assisted extraction-solid phase extraction for the determination of pesticides in infant milk formula via LC-MS/MS.

An extraction method consisting of both microwave-assisted extraction (MAE) and solid phase extraction (SPE) was developed to extract pesticides of different polarities in infant milk formula. An experimental central face composite design was established to evaluate the effects of several MAE variables simultaneously such as temperature and pH of extractant. With Derringer desirability function, all pesticides were optimally extracted at 102 °C within 20 min with 4.56 mL of extractant consisting of 0.1% of water in methanol at pH 12. Extraction efficiency was subsequently analysed with LC-MS/MS. The extraction recoveries ranging from 72% to 111% were obtained for all pesticides at fortification level of 5-100 μg kg(-1) with relative standard deviations (RSDs) of less than 8%. Compared with Soxhlet/SPE, the MAE/SPE method utilised less solvent and higher recoveries were achieved with a shorter extraction time.

Determination of pharmaceuticals classified as emerging pollutants using capillary electrophoresis with capacitively coupled contactless conductivity detection

A study on the simultaneous separation of 13 pharmaceutical products by capillary electrophoresis with capacitively coupled contactless conductivity detection was presented. The parameters of the background electrolyte, such as pH, organic additives as well as types and concentrations of cyclodextrins (CD) were studied. The optimal separation conditions were achieved with a background electrolyte consisting of 9u2009mM Tris/5u2009mM lactic acid at pH 8.0, containing 5% n‐propanol, 0.025% γ‐CD, 0.075% hydroxyl‐β‐CD and 0.15% dimethyl‐β‐CD. Limits of detections ranged from 61 to 1676u2009μg/L (S/N=3) and the relative standard deviations for migration time and peak area were below 2 and 6%, respectively. This demonstrated the potential of the capillary electrophoresis‐capacitively coupled contactless conductivity detection method for biomedical and environmental analysis, as shown in the determination of pharmaceuticals identified as emerging pollutants in water samples.

Optimization of separation of heavy metals by capillary electrophoresis with contactless conductivity detection

The separation of four toxic metal ions (Cr3+, Pb2+, Hg2+, Ni2+) was achieved by optimizing the composition of the histidine/tartaric acid background electrolyte. An on‐column preconcentration technique, viz. field amplified sample injection, was performed to improve the sensitivity. This method afforded an enhancement factor of up to 91u2009800 fold with the LODs ranging from 0.005 to 2.32u2009μg/L, which were well below the maximum contaminant levels set by the United States Environmental Protection Agency. The robustness of this method was demonstrated with its application to the analysis of real samples including tap water, drain water, and reservoir water with recoveries between 90 and 120%.

Combination of 1H nuclear magnetic resonance spectroscopy and liquid chromatography/mass spectrometry with pattern recognition techniques for evaluation of metabolic profile associated with albuminuria.

A method using 1H NMR and LC/MS with pattern recognition tools such as principal component analysis (PCA) and orthogonal projection to latent structure discriminant analysis (O-PLS-DA) was used to study the urinary metabolic profiles associated with an increase in urinary albumin in a general population. The normalized peak intensities obtained from 1H NMR and LC/MS with nonparametric two-tailed Mann-Whitney analysis was used for the identification of network of potential biomarkers corresponding to the increase of albumin in urine. The specificity of detecting the stated metabolites by 1H NMR and LC/MS was demonstrated. Our preliminary data obtained demonstrated that LC/MS may produce more distinctive metabolic profiles. For the patient group, changes in alanine, kyneurnic acid, and xanthurenic acid might be associated with changes in the tryptophan metabolism. At the same time, other metabolites that were involved in citric acid cycle, amino acid metabolism, and cellular functions were affected in the patient group. The proposed approach provided a comprehensive picture of the metabolic changes induced by the increase of protein in urine and demonstrated the advantages of using multiple diagnostic biomarkers. At the same time, the current work was demonstrated as a potential cost-effective solution of high-throughput analysis with pattern recognition tools as applied here in a real clinical situation.