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With the increase of the demand of low flavouring smoked seafood products, there is a need of methodologies able to distinguish between different seafood treatments, as not all of them are allowed in all markers. Following this objective, in the present work an untargeted volatolomics approach was applied to identify volatile markers that demonstrate that Cold smoked products can be distinguished from Tasteless smoke neither Carbon monoxide treated seafood, which are prohibited in the European Union. The use of dynamic headspace for the volatile extraction followed by thermal desorption in combination with Gas Chromatography (GC) coupled to single quadrupole Mass Spectrometry (MS) has been employed for the determination of volatile composition of smoked fish. Data processing consisted on the use of PARADISe software, applied for GC/MS data treatment, followed by the multivariate analysis with PLS_Toolbox (MATLAB), and finally the creation and validation of statistical classification model. All 107 variables obtained allowed the construction of a model reaching the correct classification of 97% of the blind samples, while a simplified model with only 11 variables correctly classified up to 93% of the blind samples. These 11 compounds were elucidated to develop subsequent target volatolomics approaches, if needed. Ordered according to the importance in the classification model, the elucidated compounds were: 3-methyl-cyclopentanone, ethylbenzene, 2-methyl-2-cyclopenten-1-one, 2-methyl-benzofuran, furfuryl alcohol, 2-acetylfuran, acetophenone, guaiacol, 1-hydroxy-2-butanone, 4-vinylguaicol and acetoin. The results demonstrated the great potential of untargeted volatolomics for smoked seafood treatments classification., (Copyright © 2020. Published by Elsevier Ltd.)

The capabilities of dynamic headspace entrainment followed by thermal desorption in combination with gas chromatography (GC) coupled to single quadrupole mass spectrometry (MS) have been tested for the determination of volatile components of olive oil. This technique has shown a great potential for olive oil quality classification by using an untargeted approach. The data processing strategy consisted of three different steps: component detection from GC-MS data using novel data treatment software PARADISe, a multivariate analysis using EZ-Info, and the creation of the statistical models. The great number of compounds determined enabled not only the development of a quality classification method as a complementary tool to the official established method "PANEL TEST" but also a correlation between these compounds and different types of defect. Classification method was finally validated using blind samples. An accuracy of 85% in oil classification was obtained, with 100% of extra virgin samples correctly classified., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The novel atmospheric pressure chemical ionization (APCI) source has been used in combination with gas chromatography (GC) coupled to hybrid quadrupole time-of-flight (QTOF) mass spectrometry (MS) for determination of volatile components of olive oil, enhancing its potential for classification of olive oil samples according to their quality using a metabolomics-based approach. The full-spectrum acquisition has allowed the detection of volatile organic compounds (VOCs) in olive oil samples, including Extra Virgin, Virgin and Lampante qualities. A dynamic headspace extraction with cartridge solvent elution was applied. The metabolomics strategy consisted of three different steps: a full mass spectral alignment of GC-MS data using MzMine 2.0, a multivariate analysis using Ez-Info and the creation of the statistical model with combinations of responses for molecular fragments. The model was finally validated using blind samples, obtaining an accuracy in oil classification of 70%, taking the official established method, "PANEL TEST", as reference., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

A dynamic headspace purge-and-trap (DHS-P&T) methodology for the determination and quantification of 61 volatile compounds responsible for muskmelon and watermelon aroma has been developed and validated. The methodology is based on the application of purge-and-trap extraction followed by gas chromatography coupled to (ion trap) mass spectrometry detection. For this purpose two different P&T sorbent cartridges have been evaluated. The influence of different extraction factors (sample weight, extraction time, and purge flow) on extraction efficiency has been studied and optimised using response surface methodology. Precision, expressed as repeatability, has been evaluated by analysing six replicates of real samples, showing relative standard deviations between 3% and 27%. Linearity has been studied in the range of 10-6130 ng mL(-1) depending on the compound response, showing coefficients of correlation between 0.995 and 0.999. Detection limits ranged between 0.1 and 274 ng g(-1). The methodology developed is well suited for analysis of large numbers of muskmelon and watermelon samples in plant breeding programs., (Copyright © 2015 Elsevier Ltd. All rights reserved.)