Papers

2022

• Benefits, toxicity and current market of cannabidiol in edibles. Critical Reviews in Food Science and Nutrition. 2022, 2024493

Under review

• A review on artificial neural networks applications and challenges in the food industrial sector.
• Cyclodextrins Inclusion Complex for Food Industry: Preparation Methods and Analytical Techniques.
• Competitive Lotka–Volterra equation from biology to microbiology: A new fourdimensional equation to describe the nisin synthesis system in different series of batch cultures.

In progress

• Machine learning methods to model the growth and product formation by Lactococcus lactis CECT 539 in fed-batch cultures.
• Machine learning models based on Quantitative Structure−Activity Relationship to predict ready biodegradability of chemicals.

2021 and earlier

• Machine Learning Applied to the Oxygen-18 Isotopic Composition, Salinity and Temperature/Potential Temperature in the Mediterranean Sea. Mathematics 2021, 2523.
• Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models. Energies 2021, 2332.
• Metal and metalloid profile as a fingerprint for traceability of wines under any Galician protected designation of origin. Journal of Food Composition and Analysis 2021, 104043
• Synthesis of advanced biobased green materials from renewable biopolymers. Current Opinion in Green and Sustainable Chemistry. 2021, 29, 100436.
• Recent advances in the extraction of polyphenols from eggplant and their application in foods. LWT-Food Science and Technology, 2021,146, 111381.
• Essential Oils as Antimicrobials in Crop Protection. Antiobiotics, 2021, 10, 34.
• Inclusion of seaweeds as healthy approach to formulate new low-salt meat products. Current Opinion in Food Science, 2021, 40, 20-25.
• Random forest, artificial neural network, and support vector machine models for honey classification. eFood 2020, 1 (1), 69-76.
• Stability assessment of extracts obtained from Arbutus unedo L. fruits in powder and solution systems using machine-learning methodologies. Food Chemistry 2020, 127460.
• Valorization of by-products from olive oil industry and added-value applications for innovative functional foods. Food research International. 2020, 137, 109683.
• Value-Added Compound Recovery from Invasive Forest for Biofunctional Applications: Eucalyptus Species as A Case Study. Molecules. 2020, 25(18), 4227.
• Humulus lupulus L. as a Natural Source of Functional Biomolecules. Applied Sciences 2020. 10 (15), 5074.
• Pomegranate peel as suitable source of high-added value bioactives: tailored functionalized meat products. Molecules, 2020, 25(12), 2859.
• Latest developments in the application of cyclodextrin host-guest complex  in beverage technology processes. Food Hydrocolloids, 2020, 106, 105882
• Prediction models to control aging time in red wine. Molecules 2019, 24 (5), 826.
• Assessment of neural networks and time series analysis to forecast airborne Parietaria pollen presence in the Atlantic coastal regions. International Journal of Biometeorology 2019, 1-11.
• Influence of B-Cyclodextrins upon the Degradation of Carbofuran Derivatives under Alkaline Conditions. Journal of Pesticides and Biofertilizers 2018, 2 (2), 1-4.
• Geochemical signatures of the groundwaters from Ourense thermal springs, Galicia, Spain. Sustainable Water Resources Management 2018
• Modelling and optimization of biogenic synthesis of gold nanoparticles from leaf extract of Swertia chirata using artificial neural network. Journal of Cluster Science 2018, 29, 1151-1159.
• Approach of different properties of alkylammonium surfactants using artificial intelligence and response surface methodology. Tenside Surfactants Detergents 2017, 54, 132-140.
• Improved 1,3-propanediol production with maintained physical conditions and optimized media composition: Validation with statistical and neural approach. Biochemical Engineering Journal 2017, 126, 109-117
• Electrical percolation of AOT-based microemulsions of n-alcohols. Journal of Molecular Liquids, 2016, 215, 18-23.
• Airborne castanea pollen forecasting model for ecological and allergological implementation. Science of the Total Environment, 548-549, 110-121.
• Application of transit data analysis and artificial neural network in the prediction of discharge of Lor River, NW Spain. Water Science & Technology, 2016, 73 (7), 1756-1767.
• Comparison between developed models using response surface methodology (RSM) and artificial neural networks (ANNs) with the purpose to optimize Oligosaccharide Mixtures Production from Sugar Beet Pulp. Industrial Crops and Products, 2016, 92, 290–299.
• Alternative method to predict activity coefficients at infinite dilution of hydrocarbons in aqueous solutions using Artificial Neural Network. Mediterranean Journal of Chemistry 2015, 3 (6), 1073-1082.
• A model to forecast the risk periods of plantago pollen allergy by using the ann methodology. Aerobiologia, 2015, 31 (2), 201-211.
• Identification of relevant phytochemical constituents for characterization and authentication of tomatoes by Generalized Linear Model linked to Automatic Interaction Detection (GLM-AID) and Artificial Neural Network models (ANNs). PLOS ONE, 2015, 1-18.
• Influence prediction of alkylamines upon electrical percolation of AOT-based microemulsions using artificial neural networks. Tenside Surfactants Detergents 2015, 6, 473-476.
• Forecasting olea airborne pollen concentration by means of artificial intelligence. Fresenius Environmental Bulletin, 2015, 24 (12b), 4574-4580
• Cleavage of carbofuran and carbofuran-derivatives in self-assembly colloid systems. Progress in Reaction Kinetics and Mechanism, 2015, 40 (2), 105-118.
• Density, viscosity and refractive index prediction of binary and ternary mixtures systems of ionic liquid. Mediterranean Journal of Chemistry 2014, 3 (4), 972-986.
• Percolative Behavior Models Based On Artificial Neural Networks For Electrical Percolation Of Aot Microemulsions In The Presence Of Crown Ethers. Tenside Surfactants Detergents, 2014, 51 (6), 533-540.
• Prediction of the Penetration of Drugs by Artificial Neural Networks. IEEE art. no. 6615859
• Percolation threshold of AOT microemulsions with n-alkyl acids as additives prediction by means of artificial neural networks. Tenside Surfactants Detergents, 2013, 50 (5), 360-368.
• Predicting critical micelle concentration values of non-ionic surfactants by using artificial neural networks. Tenside Surfactants Detergents, 2013, 50 (2), 118-124.
• Esters flash point prediction using artificial neural networks. Journal of Computational Chemistry, 2013, 34(5), 355-359.
• Predicción de la temperatura superior de disolución crítica mediante redes neuronales artificiales. Revista Iberoamericana de polímeros, 2012, 13(6), 295-306
• Relative Permittivity of Carbon Dioxide + Ethanol Mixtures prediction by means of Artificial Neural Networks. Journal of Mediterranean Chemistry, 2012, 2(1), 388-400.
• Influence prediction of small organic molecules (ureas and thioureas) upon electrical percolation of AOT-based microemulsions using artificial neural networks. Tenside Surfactants Detergents, 2012, 49, 316-320.
• Prediction of surface tension of alcohol + water solutions using artificial neural networks. Mediterranean Journal of Chemistry, 2012, 5, 221-231.
• N-Alkylamines-Based Micelles Aggregation Number Determination by Fluorescence Techniques. Journal of Solution Chemistry, 2011, 40 (12), 2072-2081.
• Dyes and biomimetic systems. Detergency and food industry. CyTA Journal of Food, 2011, 9 (4), 252-256.
• Artificial intelligence for electrical percolation of AOT-based microemulsions prediction. Tenside, Surfactants, Detergents, 2011, 48 (6), 477-483.
• Prediction of water temperature in a small river using multilayer perceptron artificial neural networks. Electronic Journal of Environmental, Agricultural and Food Chemistry, 2011, 10 (8), 1608-2615.
• Ciclodextrinas y su aplicación en la tecnología alimentaria. Industria Alimentaria, 2011, 45-46.
• Basic degradation of 3-keto-carbofuran in the presence of non-ionic self-assembly colloids. Fresenius Environmental Bulletin, 2011, 20 (2), 354-357.
• Influence of anionic and nonionic micelles upon hydrolysis of 3-hydroxy-carbofuran. International Journal of Chemical Kinetics, 2011, 43 (8), 402-408.
• Multilayer perceptron neural network for flow prediction. Journal of Environmental Monitoring, 2011, 13 (1), 35-41.
• Alkalinefading of triarylmethyl carbocations in self-assembly microheterogeneous media. Progress in Reaction Kinetics and Mechanism, 2011, 36 (2), 139-165.
• The use of artificial neural networks to forecast biological atmospheric allergens or pathogens only as Alternaria spores.  Journal of Environmental Monitoring, 2010, 12 (11), 2145-2152.
• Artificial neural networks: A promising tool to evaluate the authenticity of wine. CyTA Journal of Food, 2010, 8 (1), 79-86.
• Prediction of ethene + oct-1-ene copolymerization ideal conditions using artificial neuron networks. Journal of Chemical and Engineering Data, 2010, 55 (9), 3542-3547.
• Factors controlling flavors binding constants to cyclodextrins and their applications in foods. Food Research International, 2010, 43 (4), 1212-1218.
• Influence of colloid suspensions of humic acids on the alkaline hydrolysis of N-methyl-N-nitroso-p-toluene sulfonamide. International Journal of Chemical Kinetics, 2010, 42 (5), 316-322.
• Evaluation of atmospheric Poaceae pollen concentration using a neural network applied to a coastal Atlantic climate region. Neural Networks, 2010, 23 (3), 419-425.
• Cyclodextrin-surfactant mixed systems as reaction media. Progress in Reaction Kinetics and Mechanism, 2010, 35 (2), 105-129.
• A review on the use of cyclodextrins in foods. Food Hydrocolloids, 2009, 23 (7), 1631-1640.
• Sorption behaviour of arsenic by iron and aluminium-oxides-coated quartz particles. Fresenius Environmental Bulletin, 2008, 17 (12 A), 2122-2125.
• Influence of colloid suspensions of humic acids upon the alkaline fading of carbocations. Journal of Physical Organic Chemistry, 2008, 21 (7-8), 555-560.
• Organic reactivity in AOT-stabilized microemulsions. Progress in Reaction Kinetics and Mechanism, 2008, 33 (1), 81-97.