title: Modelling drying kinetics of thyme (Thymus vulgaris L.): theoretical and empirical models, and neural networks.
abstract: The drying kinetics of thyme was analyzed by considering different conditions: air temperature of between 40°C  and 70°C , and air velocity of 1 m/s. A theoretical diffusion model and eight different empirical models were fitted to the experimental data. From the theoretical model application, the effective diffusivity per unit area of the thyme was estimated (between 3.68 × 10(-5) and 2.12 × 10 (-4) s(-1)). The temperature dependence of the effective diffusivity was described by the Arrhenius relationship with activation energy of 49.42 kJ/mol. Eight different empirical models were fitted to the experimental data. Additionally, the dependence of the parameters of each model on the drying temperature was determined, obtaining equations that allow estimating the evolution of the moisture content at any temperature in the established range. Furthermore, artificial neural networks were developed and compared with the theoretical and empirical models using the percentage of the relative errors and the explained variance. The artificial neural networks were found to be more accurate predictors of moisture evolution with VAR ≥ 99.3% and ER ≤ 8.7%.
authors.affiliation: ASPA Group, Food Technology Department, Polytechnic University of Valencia, Valencia, Spain.
authors.forename: J
authors.initials: J
authors.lastname: Rodríguez
authors.forename: G
authors.initials: G
authors.lastname: Clemente
authors.forename: N
authors.initials: N
authors.lastname: Sanjuán
authors.forename: J
authors.initials: J
authors.lastname: Bon
mesh_terms: D000388:Air; D003890:Desiccation / Q000379:methods*; D005519:Food Preservation / Q000379:methods* / Q000706:statistics & numerical data*; D006358:Hot Temperature; D007700:Kinetics; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D046930:Thymus Plant / Q000737:chemistry*; D014867:Water / Q000737:chemistry
chemical_list: D014867:Water
keywords: drying; kinetics; neural networks; thyme