Optimisation of osmotic dehydration process of guavas by response surface methodology and desirability function

2011 ◽  
Vol 47 (1) ◽  
pp. 132-140 ◽  
Author(s):  
Gláucia S. Vieira ◽  
Leila M. Pereira ◽  
Miriam D. Hubinger
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Desirability Function ◽  
Osmotic Dehydration ◽  
Dehydration Process

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.

scholarly journals Efficiency of Osmotic Dehydration of Pomegranate Seeds in Polyols Solutions Using Response Surface Methodology

Horticulturae ◽  
2021 ◽  
Vol 7 (9) ◽  
pp. 268
Author(s):  
Brahim Bchir ◽  
Haifa Sebii ◽  
Sabine Danthine ◽  
Christophe Blecker ◽  
Souhail Besbes ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Water Loss ◽  
Weight Reduction ◽  
Osmotic Dehydration ◽  
Effective Diffusivity ◽  
Dehydration Process ◽  
Solid Gain ◽  
Pomegranate Seeds ◽  
Comparison Of The Results

This study investigates the influence of polyol compounds (sorbitol and erythritol) on the osmotic dehydration process of pomegranate seeds. The efficacy of the osmotic dehydration process was estimated based on the determination of water loss, weight reduction, solid gain, and effective diffusivity and also through a comparison of the results obtained between sucrose and polyol osmotic solutions. Response surface methodology was used to optimize the osmotic process. Quality attributes of pomegranate seeds were determined through the assessment of physical (texture and color) characteristics. This innovative research applies alternative solutions in the osmotic process, which until now, have not been commonly used in the osmotic dehydration of pomegranate seeds processing by researchers worldwide. Results revealed the excellent correlation of experimental values with the model. Erythritol and sorbitol exhibit stronger efficiency than sucrose. However, erythritol was not satisfactory due to the high solid gain. Therefore, the sorbitol osmotic agent seems to be the most suitable for the osmotic dehydration of pomegranate seeds. The optimal condition for maximum water loss (38.61%), weight reduction (37.77%), and effective diffusivity (4.01 × 10−8 m2/s) and minimum solid gain (−0.37%) were 13.03 min, 27.77 °Brix, and 37.7 °C, using a sorbitol solution. Results of texture and color revealed the major impact of erythritol and sorbitol osmotic agents on seed characteristics during the osmotic dehydration process.

scholarly journals Optimization of the osmotic dehydration process of plums (Prunus Salicina Lindl.) in solutions enriched with inulin, using response surface methodology

LWT ◽  
2022 ◽  
pp. 113092
Author(s):  
Irene Palacios Romero ◽  
María José Rodríguez Gómez ◽  
Francisco Manuel Sánchez Iñiguez ◽  
Patricia Calvo Magro
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Osmotic Dehydration ◽  
Dehydration Process ◽  
Prunus Salicina

scholarly journals Determination of the Optimal Neural Network Transfer Function for Response Surface Methodology and Robust Design

2021 ◽  
Vol 11 (15) ◽  
pp. 6768
Author(s):  
Tuan-Ho Le ◽  
Hyeonae Jang ◽  
Sangmun Shin
Keyword(s):  
Response Surface Methodology ◽  
Transfer Function ◽  
Response Surface ◽  
Robust Design ◽  
Transfer Functions ◽  
Desirability Function ◽  
Likelihood Estimation ◽  
Statistical Simulation ◽  
Screening Procedure ◽  
Function Estimation

Response surface methodology (RSM) has been widely recognized as an essential estimation tool in many robust design studies investigating the second-order polynomial functional relationship between the responses of interest and their associated input variables. However, there is scope for improvement in the flexibility of estimation models and the accuracy of their results. Although many NN-based estimations and optimization approaches have been reported in the literature, a closed functional form is not readily available. To address this limitation, a maximum-likelihood estimation approach for an NN-based response function estimation (NRFE) is used to obtain the functional forms of the process mean and standard deviation. While the estimation results of most existing NN-based approaches depend primarily on their transfer functions, this approach often requires a screening procedure for various transfer functions. In this study, the proposed NRFE identifies a new screening procedure to obtain the best transfer function in an NN structure using a desirability function family while determining its associated weight parameters. A statistical simulation was performed to evaluate the efficiency of the proposed NRFE method. In this particular simulation, the proposed NRFE method provided significantly better results than conventional RSM. Finally, a numerical example is used for validating the proposed method.

Modeling and Optimization of Vibration Amplitude in Turning of Ti-6Al-4V Using Response Surface Methodology

2012 ◽  
Vol 217-219 ◽  
pp. 1567-1570
Author(s):  
A.K.M. Nurul Amin ◽  
Muammer Din Arif ◽  
Syidatul Akma Sulaiman
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Vibration Amplitude ◽  
Desirability Function ◽  
Cutting Speed ◽  
Vibration Sensor ◽  
Experimental Investigations ◽  
Depth Of Cut ◽  
Work Piece ◽  
Inferior Surface

Chatter is detrimental to turning operations and leads to inferior surface topography, reduced productivity, dimensional accuracy, and shortened tool life. Avoidance of chatter has mostly been through reliance on heuristics such as: limiting material removal rates or selecting low spindle speeds and shallow depth of cuts. But, modern industries demand increased output and not steady operational limits. Various research efforts have therefore focused on developing mathematical models for chatter formation. However, as yet there is no existent model that meets all experimental verification. This research employed a novel technique based on the synergy of statistical modeling and experimental investigations in order to develop an effective empirical mathematical model for chatter amplitude and to subsequently find optimal machining conditions. Ti-6Al-4V, Titanium alloy, was used as the work-piece due to its increased popularity in applications related to aerospace, automotive, nuclear, medical, marine etc. A sequence of 15 experimental runs was conducted based on a small Central Composite Design (CCD) model in Response Surface Methodology (RSM). The primary (independent) parameters were: cutting speed, feed, and depth of cut. The tool overhang was kept constant at 70 mm. An engine lathe (Harrison M390) was employed for turning purposes. The data acquisition system comprised a vibration sensor (accelerometer) and a signal conditioning unit. The resultant vibrations were analyzed using the DASYLab 5.6 software. The best model was found to be quadratic which had a confidence level of 95% (ANOVA) and insignificant Lack of Fit (LOF) in Fit and Summary analyses. Desirability Function (DF) approach predicted minimum vibration amplitude of 0.0276 Volts and overlay plots identified two preferred machining regimes for optimal vibration amplitude.

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