Acylation of chiral alcohols: a simple procedure for chiral GC analysis

The use of iodine as a catalyst and either acetic or trifluoroacetic acid as a derivatizing reagent for determining the enantiomeric composition of acyclic and cyclic aliphatic chiral alcohols was investigated. Optimal conditions were selected according to the molar ratio of alcohol to acid, the reaction time, and the reaction temperature. Afterwards, chiral stability of chiral carbons was studied. Although no isomerization was observed when acetic acid was used, partial isomerization was detected with the trifluoroacetic acid. A series of chiral alcohols of a widely varying structural type were then derivatized with acetic acid using the optimal conditions. The resolution of the enantiomeric esters and the free chiral alcohols was measured using a capillary gas chromatograph equipped with a CP Chirasil-DEX CB column. The best resolutions were obtained with 2-pentyl acetates (α = 3.00) and 2-hexyl acetates (α = 1.95). This method provides a very simple and efficient experimental workup procedure for analyzing chiral alcohols by chiral-phase GC.

Catalytic gasification of glycerol in supercritical water

The conversion of glycerol in supercritical water (SCW) was studied at 510-550 °C and a pressure of 350 bars using both a bed of inert and non-porous ZrO2 particles (hydrothermal experiments), and a bed of a 1% Ru/ZrO2 catalyst. Experiments were conducted with a glycerol concentration of 5 wt% in a continuous isothermal fixed-bed reactor at a residence time between 2 and 10 s. Hydrothermolysis of glycerol formed water-soluble products such as acetaldehyde, acetic acid, hydroxyacetone and acrolein, and gases like H2, CO and CO2. The catalyst enhanced the formation of acetic acid, inhibited the formation of acrolein, and promoted gasification of the glycerol decomposition products. Hydrogen and carbon oxides were the main gases produced in the catalytic experiments, with minor amounts of methane and ethylene. Complete glycerol conversion was achieved at a residence time of 8.5 s at 510 °C, and at around 5 s at 550 °C with the 1 wt% Ru/ZrO2 catalyst. The catalyst was not active enough to achieve complete gasification since high yields of primary products like acetic acid and acetaldehyde were still present. Carbon balances were between 80 and 60% in the catalytic experiments, decreasing continuously as the residence time was increased. This was attributed partially to the formation of methanol and acetaldehyde, which were not recovered and analyzed efficiently in our set-up, but also to the formation of carbon deposits. Carbon deposition was not observed on the catalyst particles but on the surface of the inert zirconia particles, especially at high residence time. This was related to the higher concentration of acetic acid and other acidic species in the catalytic experiments, which may polymerize to form tar-like carbon precursors. Because of carbon deposition, hydrogen yields were significantly lower than expected; for instance at 550 °C the hydrogen yield potential was only 50% of the stoichiometric value.

Nutrimetabolomics fingerprinting to identify biomarkers of bread exposure in a free-living population from the PREDIMED study cohort

Bread is one of the most widely consumed foods. Its impact on human health is currently of special interest for researchers. We aimed to identify biomarkers of bread consumption by applying a nutrimetabolomic approach to a free-living population. An untargeted HPLC q-TOF-MS and multivariate analysis was applied to human urine from 155 subjects stratified by habitual bread consumption in three groups: non-consumers of bread (n = 56), white-bread consumers (n = 48) and whole-grain bread consumers (n = 51). The most differential metabolites (variable importance for projection ≥1.5) included compounds originating from cereal plant phytochemicals such as benzoxazinoids and alkylresorcinol metabolites, and compounds produced by gut microbiota (such as enterolactones, hydroxybenzoic and dihydroferulic acid metabolites). Pyrraline, riboflavin, 3-indolecarboxylic acid glucuronide, 2,8-dihydroxyquinoline glucuronide and N-α-acetylcitrulline were also tentatively identified. In order to combine multiple metabolites in a model to predict bread consumption, a stepwise logistic regression analysis was used. Receiver operating curves were constructed to evaluate the global performance of individual metabolites and their combination. The area under the curve values [AUC (95 % CI)] of combined models ranged from 77.8 % (69.1 86.4 %) to 93.7 % (89.4 98.1 %), whereas the AUC for the metabolites included in the models had weak values when they were evaluated individually: from 58.1 % (46.6 69.7 %) to 78.4 % (69.8 87.1 %). Our study showed that a daily bread intake significantly impacted on the urinary metabolome, despite being examined under uncontrolled free-living conditions. We further concluded that a combination of several biomarkers of exposure is better than a single biomarker for the predictive ability of discriminative analysis.

Nutrimetabolomics fingerprinting to identify biomarkers of bread exposure in a free-living population from the PREDIMED study cohort

Bread is one of the most widely consumed foods. Its impact on human health is currently of special interest for researchers. We aimed to identify biomarkers of bread consumption by applying a nutrimetabolomic approach to a free-living population. An untargeted HPLC q-TOF-MS and multivariate analysis was applied to human urine from 155 subjects stratified by habitual bread consumption in three groups: non-consumers of bread (n = 56), white-bread consumers (n = 48) and whole-grain bread consumers (n = 51). The most differential metabolites (variable importance for projection ≥1.5) included compounds originating from cereal plant phytochemicals such as benzoxazinoids and alkylresorcinol metabolites, and compounds produced by gut microbiota (such as enterolactones, hydroxybenzoic and dihydroferulic acid metabolites). Pyrraline, riboflavin, 3-indolecarboxylic acid glucuronide, 2,8-dihydroxyquinoline glucuronide and N-α-acetylcitrulline were also tentatively identified. In order to combine multiple metabolites in a model to predict bread consumption, a stepwise logistic regression analysis was used. Receiver operating curves were constructed to evaluate the global performance of individual metabolites and their combination. The area under the curve values [AUC (95 % CI)] of combined models ranged from 77.8 % (69.1 86.4 %) to 93.7 % (89.4 98.1 %), whereas the AUC for the metabolites included in the models had weak values when they were evaluated individually: from 58.1 % (46.6 69.7 %) to 78.4 % (69.8 87.1 %). Our study showed that a daily bread intake significantly impacted on the urinary metabolome, despite being examined under uncontrolled free-living conditions. We further concluded that a combination of several biomarkers of exposure is better than a single biomarker for the predictive ability of discriminative analysis.