M.A. Goicolea

Coupling solid-phase microextraction and high-performance liquid chromatography for direct and sensitive determination of halogenated fungicides in wine

A solid-phase microextraction (SPME) method coupled to high-performance liquid chromatography with diode array detection (HPLC-DAD) for the analysis of six organochlorine fungicides (nuarimol, triadimenol, triadimefon, folpet, vinclozolin and penconazole) in wine was developed. For this purpose, polydimethylsiloxane-divinylbenzene-coated fibers were utilized and all factors affecting throughput, precision, and accuracy of the SPME method were investigated and optimized. These factors include: matrix influence, extraction and desorption time, percentage of ethanol, pH, salt effect and desorption mode. The performed analytical procedure showed detectability ranging from 4 to 27 microg l(-1) and precision from 2.4 to 14.2% (as intra-day relative standard deviation, RSD) and 4.7-25.7% (as inter-day RSD) depending on the fungicide. The results demonstrate the suitability of the SPME-HPLC-DAD method to analyze these organochlorine fungicides in red wine.

Sensitive determination of triazines in underground waters using stir bar sorptive extraction directly coupled to automated thermal desorption and gas chromatography-mass spectrometry.

An automated thermal desorption-gas chromatography-mass spectrometry method for the determination of triazin herbicides in aqueous solution with excellent sensitivity was developed. The method is based on the use of stir bar sorptive extraction. The main parameters such as extraction time, sample volume, the addition of salt and organic modifiers, desorption temperature, desorption flow and desorption time which affect the efficiency of the proposed methodology are fully discussed. The proposed method is sensitive and shows a good linearity within a range of 10-10000 ng L(-1) with correlation coefficients higher than 0.998. Quantitation limits are, in all cases, below the limits accepted by European legislation for human waters consumption and ranging between 11.3 ng L(-1) and 0.7 ng L(-1). The repetitivity, expressed as a relative standard deviation, has values of lower than 8% for all analytes. Using this method, determination of 10 triazines in underground water samples was successfully performed. The average concentrations obtained in the analysis of the spiked samples at two different levels of concentration correspond to mean recoveries ranging from 94.4+/-5.1% to 106.0+/-6.3% for a significance level of 0.05.

Development of a stir bar sorptive extraction based HPLC-FLD method for the quantification of serotonin reuptake inhibitors in plasma, urine and brain tissue samples.

The aim of this article is to present an analytical application of stir bar sorptive extraction (SBSE) coupled to HPLC-fluorescence detection (FLD) for the quantification of fluoxetine (FLX), citalopram (CIT) and venlafaxine (VLF) and their active metabolites - norfluoxetine (NFLX), desmethyl- (DCIT) and didesmethylcitalopram (DDCIT) and o-desmethylvenlafaxine (ODV) - in plasma, urine and brain tissue samples. All the parameters influencing adsorption (pH, ion strength, organic modifier addition, volume, extraction time and temperature) and desorption (desorption solvent composition, time, temperature and desorption mode) of the analytes on the stir bar have been optimized. For each matrix, the analytical method has been assessed by studying the linearity and the intra- and interday accuracy (89-113%) and precision (RSD<13%). The improvement of the quantification limits (0.2-2 microg l(-1) for plasma, 2-20 ng g(-1) for brain tissue and 1-10 microg l(-1) for urine, depending on the respective response for analytes) and the development of a procedure for all the matrices make this method useful in clinical and forensic analysis.

Untargeted metabolomic analysis using liquid chromatography quadrupole time-of-flight mass spectrometry for non-volatile profiling of wines.

The current study presents a method for comprehensive untargeted metabolomic fingerprinting of the non-volatile profile of the Graciano Vitis vinifera wine variety, using liquid chromatography/electrospray ionization time of flight mass spectrometry (LC-ESI-QTOF). Pre-treatment of samples, chromatographic columns, mobile phases, elution gradients and ionization sources, were evaluated for the extraction of the maximum number of metabolites in red wine. Putative compounds were extracted from the raw data using the extraction algorithm, molecular feature extractor (MFE). For the metabolite identification the WinMet database was designed based on electronic databases and literature research and includes only the putative metabolites reported to be present in oenological matrices. The results from WinMet were compared with those in the METLIN database to evaluate how much the databases overlap for performing identifications. The reproducibility of the analysis was assessed using manual processing following replicate injections of Vitis vinifera cv. Graciano wine spiked with external standards. In the present work, 411 different metabolites in Graciano Vitis vinifera red wine were identified, including primary wine metabolites such as sugars (4%), amino acids (23%), biogenic amines (4%), fatty acids (2%), and organic acids (32%) and secondary metabolites such as phenols (27%) and esters (8%). Significant differences between varieties Tempranillo and Graciano were related to the presence of fifteen specific compounds.

Plasma biomarker discovery for early chronic kidney disease diagnosis based on chemometric approaches using LC-QTOF targeted metabolomics data

Graphical abstract Figure. No caption available. HighlightsMultivariate data analysis of 16 metabolites from three arginine‐related metabolic pathways was carried out.CIT, SAM, SDMA and CNN found to be potential biomarkers for pediatric CKD.CIT, SAM, SDMA and CNN add 18% accuracy for early CKD diagnosis in comparison with CNN‐based classification.4 new biomarkers for preliminary CKD stage establishment prior to nephrologic assessment. ABSTRACT Chronic kidney disease (CKD) is a progressive pathological condition in which renal function deteriorates in time. The first diagnosis of CKD is often carried out in general care attention by general practitioners by means of serum creatinine (CNN) levels. However, it lacks sensitivity and thus, there is a need for new robust biomarkers to allow the detection of kidney damage particularly in early stages. Multivariate data analysis of plasma concentrations obtained from LC‐QTOF targeted metabolomics method may reveal metabolites suspicious of being either up‐regulated or down‐regulated from urea cycle, arginine methylation and arginine‐creatine metabolic pathways in CKD pediatrics and controls. The results show that citrulline (CIT), symmetric dimethylarginine (SDMA) and S‐adenosylmethionine (SAM) are interesting biomarkers to support diagnosis by CNN: early CKD samples and controls were classified with an increase in classification accuracy of 18% when using these 4 metabolites compared to CNN alone. These metabolites together allow classification of the samples into a definite stage of the disease with an accuracy of 74%, being the 90% of the misclassifications one level above or below the CKD stage set by the nephrologists. Finally, sex‐related, age‐related and treatment‐related effects were studied, to evaluate whether changes in metabolite concentration could be attributable to these factors, and to correct them in case a new equation is developed with these potential biomarkers for the diagnosis and monitoring of pediatric CKD.