• 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

2016

• 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 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, 92, 290–299.

2015

• 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

2014

• 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.

2013

• 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.

 2012

• 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.

2011

• 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.

2010

• 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.

2008

• 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.