Automated solid-phase extraction for concentration and clean-up of female steroid hormones prior to liquid chromatography-electrospray ionization-tandem mass spectrometry: an approach to lipidomics.

A method for determination of free and glucuronide-conjugated female steroid hormones in urine at the pgmL(-1) level is here presented. For this purpose, a dual approach with or without beta-glucuronidase hydrolysis has been developed to succeed in this analysis. The target analytes were two progestogens - progesterone and pregnenolone - and three endogenous estrogens - estradiol, estriol and estrone. Separation and detection were carried out by liquid chromatography electrospray ionization and tandem mass spectrometry (LC-ESI-MS-MS) with a triple quadrupole (qQq) mass detector. The determination step was optimized by multiple reaction monitoring for highly selective identification and sensitive quantification of female hormones in a complex sample such as human urine. As these compounds are present in urine at very low concentration (ngmL(-1) level), a preconcentration and clean-up step by solid-phase extraction was automatically carried out prior to the chromatographic step in order to improve the sensitivity of the method. This sample pretreatment was performed using a lab-on-valve (LOV) manifold which provided preconcentration factors ranging from 59.1 to 72.3 for 10mL urine. The detection and quantification limits were in the ranges 1.8-18pg and 6-61pg on-column, respectively, with precision values from 1.93 to 10.99%, expressed as relative standard deviation. These results enable to conclude the suitability of the LOV-LC-qQq approach for determination of the lipidomic profiling of the main female steroid hormones in a difficult matrix as human urine. The method can be potentially applied to the clinical and other metabolomic areas.

Sequential (step-by-step) detection, identification and quantitation of extra virgin olive oil adulteration by chemometric treatment of chromatographic profiles.

An analytical method for the sequential detection, identification and quantitation of extra virgin olive oil adulteration with four edible vegetable oils--sunflower, corn, peanut and coconut oils--is proposed. The only data required for this method are the results obtained from an analysis of the lipid fraction by gas chromatography-mass spectrometry. A total number of 566 samples (pure oils and samples of adulterated olive oil) were used to develop the chemometric models, which were designed to accomplish, step-by-step, the three aims of the method: to detect whether an olive oil sample is adulterated, to identify the type of adulterant used in the fraud, and to determine how much aldulterant is in the sample. Qualitative analysis was carried out via two chemometric approaches--soft independent modelling of class analogy (SIMCA) and K nearest neighbours (KNN)--both approaches exhibited prediction abilities that were always higher than 91% for adulterant detection and 88% for type of adulterant identification. Quantitative analysis was based on partial least squares regression (PLSR), which yielded R2 values of >0.90 for calibration and validation sets and thus made it possible to determine adulteration with excellent precision according to the Shenk criteria.