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Quirijns, E.J., van Boxtel, A.J.B., van Loon, W.K.P. and van Straten, G. (2005) Sorption Isotherms, GAB Parameters and Isosteric Heat of Sorption. Journal of the Science of Food Agriculture, 85, 1805-1814.
https://doi.org/10.1002/jsfa.2140

has been cited by the following article:

  • TITLE: A Neural Based Modeling Approach for Drying Kinetics Analysis of Mint Branches and Their Fractions (Leaves and Stems)

    AUTHORS: Aline de Holanda Rosanova, Gustavo Dias Maia, Fábio Bentes Freire, Maria do Carmo Ferreira

    KEYWORDS: Aromatic Herbs, Regular Mint, Branches, Stems, Sorption Isotherms, Oven Drying, Artificial Neural Network

    JOURNAL NAME: Advances in Chemical Engineering and Science, Vol.7 No.2, March 13, 2017

    ABSTRACT: This work is aimed at investigating regular mint (Mentha × villosa) drying behavior and assessing how the heterogeneous composition of plants affects their drying kinetics. Drying kinetics and sorption isotherms were evaluated for whole branches and their fractions (leaves and stems). Stems and leaves were characterized by measurement of dimensions, apparent density and initial moisture content. The moisture sorption isotherms were obtained under temperatures of 30°C, 40°C and 50°C for branches, stems and leaves and the data were fitted to the GAB model. Mint branches and their fractions were oven dried at temperatures from 40°C to 70°Cand were obtained kinetic curves for each part. Water sorption patterns were similar for leaves and stems and the GAB model described well the sorption behavior of both materials. At a constant temperature, the drying rates were higher for leaves in comparison to stems and the differences increased as the temperature was raised. Therefore, depending on drying conditions, the moisture distribution in dried branches might be significantly different. Since the leaves constitute the major fraction in branches, the drying rates of branches were closer to those of leaves. The kinetic curves were fitted to a diffusion model based on an analytical solution of Fick’s second diffusion law and to an empirical model based on artificial neural network (ANN). The results showed that the model based on the ANN predicted the drying kinetics of the different parts better than the diffusive model. A single network was built to describe the kinetic behavior of branches and fractions in the whole range of temperatures investigated. The diffusive model based on fitting effective diffusivity did not provide good predictions of moisture content, probably because neither the dependence of effective diffusivity on the moisture content nor the heterogeneity and shrinking of static beds were considered.