OPTIMIZATION OF OSMOTIC DEHYDRATION OF TOONA SINENSIS LEAVES USING RESPONSE SURFACE METHODOLOGY

2007 ◽  
Author(s):  
CHANGLU WANG ◽  
LU LI ◽  
ZHANYONG LI ◽  
CHANGJIN LIU
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Osmotic Dehydration ◽  
Toona Sinensis

Optimization of Osmotic Dehydration of Toona Sinensis Leaves Using Response Surface Methodology

2008 ◽  
Vol 4 (6) ◽  
Author(s):  
Changlu Wang ◽  
Lu Li ◽  
Zhanyong Li ◽  
Changjin Liu ◽  
Mianhua Chen
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Salt Concentration ◽  
Water Loss ◽  
Immersion Time ◽  
Osmotic Dehydration ◽  
Sucrose Concentration ◽  
Optimal Solution ◽  
Solid Gain ◽  
Toona Sinensis

Toona Sinensis leaves were dehydrated by osmosis. The effects of temperature, immersion time, sucrose and salt concentration on water loss and solid gain during osmotic dehydration were investigated by response surface methodology. Consequently, second order polynomial models were developed for water loss and solid gain as a function of the experimental parameters. By applying Excel’s Solver feature, an optimal solution for maximum water loss and minimum solid gain was given at temperature of 30oC, sucrose concentration of 50%, salt concentration of 10%, and immersion time of 4 hours.

scholarly journals Optimization of osmotic dehydration of chestnut (Castanea sativa Mill.) slices using Response Surface Methodology

2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Teresa Delgado ◽  
Bruna Paim ◽  
José Alberto Pereira ◽  
Susana Casal ◽  
Elsa Ramalhosa
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Water Loss ◽  
Osmotic Dehydration ◽  
Sucrose Concentration ◽  
Volume Ratio ◽  
Castanea Sativa ◽  
Color Variation ◽  
Process Conditions ◽  
Operational Conditions

Osmotic dehydration of chestnut slices in sucrose was optimized for the first time by Response Surface Methodology (RSM). Experiments were planned according to a three-factor central composite design (α=1.68), studying the influence of sucrose concentration, temperature and time, on the following parameters: volume ratio, water activity, color variation, weight reduction, solids gain, water loss and normalized moisture content, as well as total moisture, ash and fat contents. The experimental data was adequately fitted into second-order polynomial models with coefficients of determination (R2) from 0.716 to 0.976, adjusted-R2 values from 0.460 to 0.954, and non-significant lacks of fit. The optimal osmotic dehydration process conditions for maximum water loss and minimum solids gain and color variation were determined by the “Response Optimizer” option: 83% sucrose concentration, 20 °C and 9.2 hours. Thus, the best operational conditions corresponded to high sugar concentration and low temperature, improving energy saving and decreasing the process costs.

Optimization of Osmotic Dehydration of Radish in Salt Solution Using Response Surface Methodology

2009 ◽  
Vol 5 (3) ◽  
Author(s):  
Manivannan Petchi ◽  
Rajasimman Manivasagan
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Salt Solution ◽  
Water Loss ◽  
Weight Reduction ◽  
Osmotic Dehydration ◽  
Dehydration Process ◽  
Initial Sample ◽  
Solid Gain ◽  
Sample Ratio

Response surface methodology was used to determine the optimum processing conditions that yield maximum water loss and weight reduction and minimum solid gain during osmotic dehydration of radish in salt solution. The experiments were conducted according to Central Composite Design (CCD). The independent process variables for osmotic dehydration process were temperature (25 – 45°C), processing time (30 -150 minutes), salt concentrations (5 - 25% w/w) and solution to sample ratio (5:1 – 25:1). The osmotic dehydration process was optimized for water loss, solid gain, and weight reduction. The optimum conditions were found to be: temperature – 36°C, immersion time - 95 min, salt concentration – 25% and solution to sample ratio 15:1. At this optimum point, water loss, solid gain and weight reduction were found to be 34.5 (g/100 g initial sample), 2.2 (g/100 g initial sample) and 32.1 (g/100 g initial sample), respectively.

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