about
Neural network analysis of spectroscopic data of lycopene and beta-carotene content in food samples compared to HPLC-UV-vis.Exhaled Breath Markers for Non-Imaging and Non-Invasive Measures for Detection of Multiple Sclerosis.Cancer metabolism: the volatile signature of glycolysis-in vitro model in lung cancer cells.Neural networks applied to characterize blends containing refined and extra virgin olive oils.Neural networks applied to determine the thermophysical properties of amino acid based ionic liquids.Identifying and Quantifying Adulterants in Extra Virgin Olive Oil of the Picual Varietal by Absorption Spectroscopy and Nonlinear Modeling.A COSMO-RS based guide to analyze/quantify the polarity of ionic liquids and their mixtures with organic cosolvents.Prediction of gas solubilities in ionic liquids.The initial freezing temperature of foods at high pressure.Ionic liquids: determination of their aqueous content using differential scanning calorimeter equipment, chaotic parameters and a radial basis network model.Chaotic parameters and their role in quantifying noise in the output signals from UV, TGA and DSC apparatus.Solving the spectroscopy interference effects of beta-carotene and lycopene by neural networks.Self-organizing maps and learning vector quantization networks as tools to identify vegetable oils.Estimation of ternary liquid-liquid equilibria for arene/alkane/ionic liquid mixtures using neural networks.Prediction of non-ideal behavior of polarity/polarizability scales of solvent mixtures by integration of a novel COSMO-RS molecular descriptor and neural networks.Optimising an artificial neural network for predicting the melting point of ionic liquids.Artificial sensing intelligence with silicon nanowires for ultraselective detection in the gas phase.Spectroscopic determination of the photodegradation of monovarietal extra virgin olive oils and their binary mixtures through intelligent systems.Sensors: A Highly Sensitive Diketopyrrolopyrrole-Based Ambipolar Transistor for Selective Detection and Discrimination of Xylene Isomers (Adv. Mater. 21/2016).A Highly Sensitive Diketopyrrolopyrrole-Based Ambipolar Transistor for Selective Detection and Discrimination of Xylene Isomers.Quantification of phenolic compounds in olive oil mill wastewater by artificial neural network/laccase biosensor.The accurate estimation of physicochemical properties of ternary mixtures containing ionic liquids via artificial neural networks.Linking chemical parameters to sensory panel results through neural networks to distinguish olive oil quality.Estimation of the refractive indices of imidazolium-based ionic liquids using their polarisability values.Neural networks to estimate the water content of imidazolium-based ionic liquids using their refractive indices.Programmed Nanoparticles for Tailoring the Detection of Inflammatory Bowel Diseases and Irritable Bowel Syndrome Disease via Breathprint.Silicon Nanowire Sensors Enable Diagnosis of Patients via Exhaled Breath.Cancerous glucose metabolism in lung cancer-evidence from exhaled breath analysis.Application of lag-k autocorrelation coefficient and the TGA signals approach to detecting and quantifying adulterations of extra virgin olive oil with inferior edible oils.Linear and non linear chemometric models to quantify the adulteration of extra virgin olive oil.Radial basis network analysis of color parameters to estimate lycopene content on tomato fruits.Estimation with neural networks of the water content in imidazolium-based ionic liquids using their experimental density and viscosity valuesA Novel Method To Quantify the Adulteration of Extra Virgin Olive Oil with Low-Grade Olive Oils by UV−VisImprovement of fluidized-bed dryers for drying solid waste (olive pomace) in olive oil mills
P50
Q33516085-8E9B92DC-E679-457D-A7E8-FCB5013AE378Q38646509-524FDB6E-F54D-4220-A784-79531D5ACB1EQ38722328-BBF708AC-6B96-4692-B75A-78F04FBCD263Q39260963-132A6334-8E68-43C4-AAB4-7606A3D3CD33Q39980566-05DD9BCC-072C-443D-89F7-87A774069CD7Q40885523-DE7B2F35-E385-4211-B34F-B3881FE15A76Q43166562-EFA83F00-71EA-4F0A-8209-6A2EAF1A4009Q43727606-B264F652-11B2-4291-9332-22C44C691116Q44436013-6BD27FFB-9662-4CA6-A27B-B8E9F980FF99Q45036548-7A0C54A6-9BD4-409D-9340-A2C90EA8D1B1Q45216110-FA7418E7-D605-484C-8145-75AD2E259C24Q45374032-79BC56DB-7ED2-4EC6-AFE7-2AB0C53EBF27Q46097480-92311FAA-7DA0-4194-A4BA-6D1BAC0C67BEQ46218969-F2893475-66BB-4F62-B3B6-D6CCF6B803A6Q46340089-586698E7-087B-42FE-A880-85190F202536Q46345709-80ADE1B6-31DC-4D72-939E-405B59F6D2EAQ46944890-9F5894E5-08E2-4F69-BDAE-BD842844542BQ46957064-27C87417-7BE1-4DF3-8564-0B7C1FAE7A51Q50635372-3DD0FC71-6C43-4953-A159-67090EF01C75Q50688168-F7F691B3-3203-44DB-8864-9CAB3BFED951Q50940483-298F546F-E789-48A7-956B-8E3D5FA832E4Q50977702-3105AF96-8033-4BEA-AEDB-2FB732ABD31BQ51027579-0B44013C-0A29-4419-A292-BC435D953909Q51141813-58050F98-B5BB-4A3E-B329-631D8FBC6B01Q51149140-6483A1DB-5E51-4929-BEF0-4BB8C2B2DFB8Q51259486-34990618-35E5-4B53-A445-EE9372D6A881Q51264612-9E464E22-F65A-44F9-B445-933EBA4AD9A5Q51511486-3613ECAB-7374-48D5-B1D1-AAE667B14555Q51601590-762FED38-5057-440D-AD48-466E83FB60A7Q51630471-64FDD915-5E22-46DC-A58C-DA4D3879E732Q51642451-8427781F-2423-40AF-98F6-C58B8E5A5EF8Q58683610-ED0F2D1F-4C2B-43D9-B528-1725D8497C7FQ58683767-62565621-960E-409C-8B4F-A71F22575805Q58683946-9FC23EBE-1E7F-496F-A60F-11B83099D471
P50
description
hulumtues
@sq
researcher
@en
wetenschapper
@nl
հետազոտող
@hy
name
José S Torrecilla
@ast
José S Torrecilla
@en
José S Torrecilla
@es
José S Torrecilla
@nl
José S Torrecilla
@sl
type
label
José S Torrecilla
@ast
José S Torrecilla
@en
José S Torrecilla
@es
José S Torrecilla
@nl
José S Torrecilla
@sl
prefLabel
José S Torrecilla
@ast
José S Torrecilla
@en
José S Torrecilla
@es
José S Torrecilla
@nl
José S Torrecilla
@sl
P1053
L-6525-2014
P106
P21
P31
P3829
P496
0000-0003-1209-203X