A successful comparison between a non-invasive measurement of local profiles during drying of a highly shrinkable food material (eggplant) and the spatial reaction engineering approach. (October 2018)

• Record Type: Journal Article
• Authors: Putranto, Aditya; Chen, Xiao Dong
• Is Part Of: Journal of food engineering. Volume 235(2018)
• Page Start: 23
• Page End: 31
• Publication Date: 2018-10
• Subjects: Drying -- Internal profiles -- Model -- Heat and mass transfer -- Spatial reaction engineering approach; Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Analyse -- Périodiques; Aliments -- Recherche -- Périodiques; 664.005
• DOI: 10.1016/j.jfoodeng.2018.04.024
• Languages: English
• ISSNs: 0260-8774

Abstract

Abstract: A reliable mathematical model is useful for predicting internal profiles inside materials during drying. In this study, for the first time, the spatial reaction engineering approach (S-REA) is employed to model the local profiles of food materials during drying. The REA is applied as the local rate of phase change and combined with a set of equations of conservation of heat and mass transfer to yield the spatial profiles of temperature and concentration during drying. The S-REA predictions are benchmarked against the Magnetic Resonance Imaging (MRI) data. The study indicates that the S-REA is applicable to model the internal profiles inside food materials during drying. The S-REA predictions also show closer agreement towards the experimental data than the effective diffusion model. While the S-REA predictions are accurate, it requires minimum number of experiments to generate the drying parameters. The S-REA has contributed to better analysis of transport phenomena inside food materials during drying through generation of local profiles. The S-REA predictions can potentially be implemented to interpret the sensory and quality matters during drying. Highlights: The S-REA predicts accurately the local profiles during drying as benchmarked towards MRI measurement. The local profiles generated by the S-REA assist in explaining complex interrelationships among variables during drying. The S-REA predictions are useful to describe the quality matters during drying.