Surface Temperature Limit as Food Quality Control in Automatic Learning Model for Drying Process

Abstract

This paper presents design and implementation of an automatic learning model for a drying process. Setting surface temperature limit as an upper boundary for the drying process is very helpful key to prevent loss in physico-chemical properties such as color variation, nutrients, preferable odors, and surface textures. Based upon input-out data acquired from the designed system, the drying machine can identify system parameter adjusting by innovation sequences of the Kalman gains. The model is then used as a predictor to prescribe suggestion rules for firing automatically suitable control gains. According to the experimental results, Thai curry paste as testing materials under the proposed control process reveal desired properties, meaning that the scheme is effective and is available to be adopted for other similar dried food requirements.