CITT and inverse analyses applied to the study of the mushroom drying process

• Published in: Journal of food engineering Vol. 101; no. 2; pp. 166 - 178
• Main Authors: da Silva, Cristiane Kelly F., de Oliveira, Andréa Samara Santos, Kurozawa, Louise Emy, Murr, Fernanda Elizabeth Xidieh, da Silva, Zaqueu Ernesto
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01-11-2010; [New York, NY]: Elsevier Science Pub. Co; Elsevier
• Subjects: Biological and medical sciences; CITT; classical integral transform technique; diffusion; Diffusion coefficient; drying; drying quality; equipment performance; food analysis; food composition; Food engineering; Food industries; food processing quality; Fruit and vegetable industries; Fundamental and applied biological sciences. Psychology; General aspects; inverse analyses; Inverse problem; Levenberg–Marquardt; Mass transfer; model food systems; mushrooms; Sensitivity study; CITT; Diffusion coefficient; Levenberg–Marquardt; Sensitivity study; Mass transfer; Inverse problem; Fungi; Levenberg-Marquardt; Sensitivity; Analysis; Edible fungi; Drying
• ISSN: 0260-8774; 1873-5770
• DOI: 10.1016/j.jfoodeng.2010.06.021

A study and modeling of the mass transfer phenomenon in the drying process of agricultural products, using Fick’s second law of diffusion adapted to infinite flat plate geometry, are described. An analytical solution methodology, the classical integral transform technique (CITT), is employed. Predicted results are compared with experimental data on the drying of sliced mushrooms in different operating conditions. A phenomenological analysis is made using graphics and tables. The data obtained by CITT and experimentally show negligible differences. The mass diffusion coefficient during hot air drying of mushrooms is estimated. Experimental drying kinetics are applied to the product at different air temperatures and air flow rates aiming to find a solution for the mass transfer equation, using an inverse problem with the Levenberg–Marquardt optimization technique in successive trials. Experimental data are also modified by a normal distribution of random errors. Statistical analyses show good agreement between experimental and predicted curves.