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The prevalence of type 2 diabetes continuously increases globally. A personalized strategy applied in the pre-diabetic stage is vital for diabetic prevention and management. The personalized diagnosis of Chinese Medicine (CM) may help to stratify the diabetics. Metabolomics is regarded as a potential platform to provide biomarkers for disease-subtypes. We designed an explorative study of 50 pre-diabetic males, combining GC-MS urine metabolomics with CM diagnosis in order to identify diagnostic biomarkers for pre-diabetic subtypes. Three CM physicians reached 85% diagnosis consistency resulting in the classification of 3 pre-diabetic groups. The urine metabolic patterns of groups 1 'Qi-Yin deficiency' and 2 'Qi-Yin deficiency with dampness' (subtype A) and group 3 'Qi-Yin deficiency with stagnation' (subtype B) were clearly discriminated. The majority of metabolites (51%), mainly sugars and amino acids, showed higher urine levels in subtype B compared with subtype A. This indicated more disturbances of carbohydrate metabolism and renal function in subtype B compared with subtype A. No differences were found for hematological and biochemical parameters except for levels of glucose and γ-glutamyltransferase that were significantly higher in subtype B compared with subtype A. This study proved that combining metabolomics with CM diagnosis can reveal metabolic signatures for pre-diabetic subtypes. The identified urinary metabolites may be of special clinical relevance for non-invasive screening for subtypes of pre-diabetes, which could lead to an improvement in personalized interventions for diabetics.

The data presented herein proved that both molecular profiling platforms can be used for antidoping screening. The mass accuracies are excellent in both instruments; however, the GC/Q-Orbitrap performs better as it provides higher resolution than the GC/Q-TOF platform.

A method for residue analysis of pesticides and polychlorinated biphenyls in cereals and feed ingredients based on QuEChERS extraction, programmed temperature vaporizer large-volume injection, and GC with electron ionization (EI) quadrupole Orbitrap full-scan high-resolution MS (60 000 full width at half-maximum at m/z 200) has been developed. In addition to full-scan acquisition, simultaneous full-scan and selected-ion monitoring acquisition was used to improve detectability in incidental cases in which analytes coeluted with intense signals from coextractants. The method was successfully validated down to 10 µg/kg for a single commodity (wheat) using matrix-matched calibration, and for multiple-feed matrixes using standard addition. Identification according to European Union requirements was achieved in >90% of the analyte/matrix combinations, and suggestions for further increasing identification rates have been made. Performance characteristics were compared to an existing method for residue analysis based on GC with EI tandem MS (triple quadrupole).

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A novel probabilistic Bayesian strategy is proposed to resolve highly coeluting peaks in high-resolution GC-MS (Orbitrap) data. Opposed to a deterministic approach, we propose to solve the problem probabilistically, using a complete pipeline. First, the retention time(s) for a (probabilistic) number of compounds for each mass channel are estimated. The statistical dependency between m/z channels was implied by including penalties in the model objective function. Second, Bayesian Information Criterion (BIC) is used as Occam's razor for the probabilistic assessment of the number of components. Third, a probabilistic set of resolved spectra, and their associated retention times are estimated. Finally, a probabilistic library search is proposed, computing the spectral match with a high resolution library. More specifically, a correlative measure was used that included the uncertainties in the least square fitting, as well as the probability for different proposals for the number of compounds in the mixture. The method was tested on simulated high resolution data, as well as on a set of pesticides injected in a GC-Orbitrap with high coelution. The proposed pipeline was able to detect accurately the retention times and the spectra of the peaks. For our case, with extremely high coelution situation, 5 out of the 7 existing compounds under the selected region of interest, were correctly assessed. Finally, the comparison with the classical methods of deconvolution (i.e., MCR and AMDIS) indicates a better performance of the proposed algorithm in terms of the number of correctly resolved compounds.