Electrochemical multi-sensors device coupled with heuristic or meta-heuristic selection algorithms for single-cultivar olive oil classification

Potentiometric electrochemical multi-sensors performance highly depends on the capability of selecting the best set of sensors. Indeed, signals are usually collinear resulting in over-fitted multivariate models with low predictive applicability. In this work, a comparative study was made to evaluate the predictive performance of classification models coupled with heuristic or meta-heuristic variable selection algorithms. In this study, eleven single cultivar extra virgin olive oils, from two crop years, were used. The results demonstrated that linear discriminant analysis with simulated annealing algorithm allowed selecting the best subset of sensors enabling 100% of correct cross validation classifications, considering samples split by crop year

Separation and Determination of Some of the Main Cholesterol-Related Compounds in Blood by Gas Chromatography-Mass Spectrometry (Selected Ion Monitoring Mode)

Oxysterols are metabolites produced in the first step of cholesterol metabolism, which is related to neurodegenerative disorder. They can be detected by testing blood, plasma, serum, or cerebrospinal fluid. In this study, some cholesterol precursors and oxysterols were determined by gas chromatography coupled to mass spectrometry. The selected cholesterol-related compounds were desmosterol, lathosterol, lanosterol, 7 -hydroxycholesterol, 7 -hydroxycholesterol, 24(S)-hydroxycholesterol, 25-hydroxycholesterol, 7-ketocholesterol, and 27-hydroxycholesterol. A powerful method was developed and validated considering various analytical parameters, such as linearity index, detection and quantification limits, selectivity and matrix effect, precision (repeatability), and trueness (recovery factor) for each cholesterol-related compound. 7 -hydroxycholesterol, 7 -hydroxycholesterol, and desmosterol exhibited the lowest detection and quantification limits, with 0.01 and 0.03 g/mL, respectively, in the three cases. 7-ketocholesterol and lathosterol showed matrix effect percentages between 95.5% and 104.8%, respectively (demonstrating a negligible matrix effect), and very satisfactory repeatability values (i.e., overall performance of the method). Next, the method was applied to the analysis of a very interesting selection of mouse plasma samples (9 plasma extracts of non-transgenic and transgenic mice that had been fed different diets). Although the number of samples was limited, the current study led to some biologically relevant conclusions regarding brain cholesterol metabolism.The authors are grateful for the interesting collaboration with the biotechnology-based company Neuron Bio. The research project was funded by CEI BioTic/University of Granad

Single-cultivar extra virgin olive oil classification using a potentiometric electronic tongue

Label authentication of monovarietal extra virgin olive oils is of great importance. A novel approach based on a potentiometric electronic tongue is proposed to classify oils obtained from single olive cultivars (Portuguese cvs. Cobrançosa, Madural, Verdeal Transmontana; Spanish cvs. Arbequina, Hojiblanca, Picual). A meta-heuristic simulated annealing algorithm was applied to select the most informative sets of sensors to establish predictive linear discriminant models. Olive oils were correctly classified according to olive cultivar (sensitivities greater than 97%) and each Spanish olive oil was satisfactorily discriminated from the Portuguese ones with the exception of cv. Arbequina (sensitivities from 61% to 98%). Also, the discriminant ability was related to the polar compounds contents of olive oils and so, indirectly, with organoleptic properties like bitterness, astringency or pungency. Therefore the proposed E-tongue can be foreseen as a useful auxiliary tool for trained sensory panels for the classification of monovarietal extra virgin olive oils.This work was co-financed by FCT and FEDER under Program COMPETE (Project PEst-C/EQB/LA0020/2013)

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Olive oil quality grading is traditionally assessed by human sensory evaluation of positive and negative attributes (olfactory, gustatory, and final olfactorygustatory sensations). However, it is not guaranteed that trained panelist can correctly classify monovarietal extra-virgin olive oils according to olive cultivar. In this work, the potential application of human (sensory panelists) and artificial (electronic tongue) sensory evaluation of olive oils was studied aiming to discriminate eight single-cultivar extra-virgin olive oils. Linear discriminant, partial least square discriminant, and sparse partial least square discriminant analyses were evaluated. The best predictive classification was obtained using linear discriminant analysis with simulated annealing selection algorithm. A low-level data fusion approach (18 electronic tongue signals and nine sensory attributes) enabled 100 % leave-one-out cross-validation correct classification, improving the discrimination capability of the individual use of sensor profiles or sensory attributes (70 and 57 % leave-one-out correct classifications, respectively). So, human sensory evaluation and electronic tongue analysis may be used as complementary tools allowing successful monovarietal olive oil discrimination.This work was co-financed by FCT/MEC and FEDER under Program PT2020 (Project UID/EQU/50020/2013); by Fundacao para a Ciencia e Tecnologia under the strategic funding of UID/BIO/04469/2013 unit; and by Project POCTEP through Project RED/AGROTEC-Experimentation network and transfer for development of agricultural and agro industrial sectors between Spain and Portugal

Autentificación de aceites vegetales mediante el empleo de cromatografía de gases y espectrometría de masas: cuantificación de aceite de oliva

Tesis Univ. Granada. Departamento de Química Analítica. Leída el 15 de mayo de 201