Abstract
The quality and shelf-life of an underutilized frui ts are compromised by the conventional method of drying. We therefore proposed using hot-air chamber to develop the drying curves of Canarium odontophyllum (dabai) fruit. Present study provides evidence of the best mathematical model to demonstrate the drying characteristi cs of this indigenous fruit and thus, may generate or add to t he existing database. The drying experiments were performed at three different relative humidity of 10, 20 and 30% and a constant air velocity of 1 m sec -1 . Drying kinetics of C. odontophyllum fruit were investigated and obtained. A non-linear regression procedure was used to fit three different one-term exponential models of thin layer drying models. The models were compared with experimental data of C. odontophyllum fruit drying at air temperature of 55°C. The fit qu ality of the models was evaluated using the coefficient of determination (R 2 ), Mean Bias Error (MBE) and Root Mean Square Error (RMSE). The highest value of R 2 obtained was 0.9348, the lowest MBE value was 0.0018 and the value for RMSE was 0.0420. Page model is the best mathematical model to describe the drying behavior of C. odontophyllum fruit.
Highlights
Drying is one of an important post handling process of agricultural products (Fudholi et al, 2010)
The initial moisture content of dabai fruit was determined by measuring its initial and final weight using the hot-air chamber at 120°C until constant weight was obtained (Meziane, 2011)
This study demonstrated that the higher the relative humidity, the longer was the drying process of dabai fruit due to the increased moisture content of dabai fruit
Summary
Drying is one of an important post handling process of agricultural products (Fudholi et al, 2010). Most agricultural commodities and marine products require drying process in an effort to preserve the quality of the final product. Drying kinetics is generally evaluated experimentally by measuring the weight of a drying material a function of time. Mathematical modeling of thin layer drying is important for optimum management of operating parameters and prediction performance of drying process. It is essential to set out accurate models to simulate the drying curves under different drying conditions. The description and prediction of the drying kinetics of a given material are still a weakness in the modeling of drying process. There is a great need for stable and reliable model to quantify and predict drying rates and drying times with a satisfying accuracy (Saeed et al, 2008)
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