Multi-objective optimization of the apple drying and rehydration processes parameters

Winiczenko, Radosław

doi:10.9755/ejfa.2018.v30.i1.1595

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…In our work and in the literature [32,52] various processes parameters (drying and rehydration) and various quality criteria were considered. Winiczenko et al [52] carried out optimization of the drying and rehydration (in distilled water) processes of apple using MOGA algorithm. They obtained the following recommended processes parameters.…”

Section: Pareto Id Wac (−) Vr (−) CD (−) T D ( • C) V (M/s) Ph (−) T mentioning

confidence: 99%

Apple Cubes Drying and Rehydration. Multiobjective Optimization of the Processes

Winiczenko¹,

Górnicki²,

Kaleta³

et al. 2018

Sustainability

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The effect of convective drying temperature (Td), air velocity (v), rehydration temperature (Tr), and kind of rehydrating medium (pH) was studied on the following apple quality parameters: water absorption capacity (WAC), volume ratio (VR) color difference (CD). To model, simulate, and optimize parameters of the drying and rehydration processes hybrid methods artificial neural network and multiobjective genetic algorithm (MOGA) were developed. MOGA was adapted to the apple tissue, where the simultaneous minimization of CD and VR and the maximization of WAC were considered. The following parameters range were applied, 50 ≤ Td ≤ 70 °C and 0.01 ≤ v ≤ 6 m/s for drying and 20 ≤ Tr ≤ 95 °C for rehydration. Distilled water (pH = 5.45), 0.5% solution of citric acid (pH = 2.12), and apple juice (pH = 3.20) were used as rehydrating media. For determining the rehydrated apple quality parameters the mathematical formulas were developed. The following best result was found. Td = 50.1 °C, v = 4.0 m/s, Tr = 20.1 °C, and pH = 2.1. The values of WAC, VR, and CD were determined as 4.93, 0.44, and 0.46, respectively. Experimental verification was done, the maximum error of modeling was lower than 5.6%.

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Section: Pareto Id Wac (−) Vr (−) CD (−) T D ( • C) V (M/s) Ph (−) T mentioning

confidence: 99%

Apple Cubes Drying and Rehydration. Multiobjective Optimization of the Processes

Winiczenko¹,

Górnicki²,

Kaleta³

et al. 2018

Sustainability

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show abstract

“…Ferrández et al 17 also successfully replaced constrained mono‐objective problems with MOO for high‐pressure thermal processes in food treatment, as the latter gave a better, adequate set of parameters with less computation time. Several studies have been dedicated to MOO through GA 16,18,19 and hybrid ANN‐MOGA 20–24 . The utilization of ANN and GA can be efficient for multivariate modeling and optimization in the case of non‐linear variables.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have been dedicated to MOO through GA 16,18,19 and hybrid ANN-MOGA. [20][21][22][23][24] The utilization of ANN and GA can be efficient for multivariate modeling and optimization in the case of non-linear variables.…”

Section: Introductionmentioning

confidence: 99%

Ascorbic acid nanoencapsulation using polyelectrolyte complex formation and optimization using hybrid artificial neural network‐genetic algorithm

Khuntia,

Mitra

2023

JSFA Reports

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BackgroundsThe polyelectrolyte complex (PEC) formation using chitosan (Cs) and alginate (Alg) has been employed to carry out Vitamin C (VC) nanoencapsulation because of its instability in adverse conditions. This method includes VC nanoencapsulation using physical cross‐linking. The effect of the mixing order as well as the different mass ratios of both the polymer and the VC content, were investigated on the size, pdi, zeta potential, encapsulation efficiency (EE%), and yield%. Hence, considering different mass ratios of both polymer (4:1–1:4) and VC content (10%–30% w/w of Cs) as independent variables, PECs were formed using 0.1% (w/v) of Cs and Alg solution.ResultThe result showed that, for equal mass ratio (1:1) of Cs and Alg, the addition of Alg solution to Cs solution led to lower particle size, while higher particle size was observed with reverse order of addition. However, no significant effect of polymer mass ratios was observed on particle size and NPs stability, while EE varied with VC concentration. Artificial neural network (ANN) was applied to train the experimental parameters Cs: Alg ratios (X1:X2) and VC content (X3) for the output variables. Moreover, process optimization was carried out using multi‐objective genetic algorithm (MOGA) with the goal of lowering particle size and increasing EE while increasing nanoparticle yield.ConclusionThe PEC method effectively encapsulated VC as obtained from higher EE. The Cs: Alg ratio of 4:2.1 and 18% VC content (w/w of Cs) was found optimum for nanoencapsulation. The final ANN‐GA model showed an acceptable agreement with experimental data.

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Multiobjective optimization of skim milk microfiltration based on expert knowledge

Belna¹,

Ndiaye²,

Taillandier³

et al. 2022

Expert Systems with Applications

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Multi-objective optimization of the apple drying and rehydration processes parameters

Cited by 5 publications

References 27 publications

Apple Cubes Drying and Rehydration. Multiobjective Optimization of the Processes

Apple Cubes Drying and Rehydration. Multiobjective Optimization of the Processes

Ascorbic acid nanoencapsulation using polyelectrolyte complex formation and optimization using hybrid artificial neural network‐genetic algorithm

Multiobjective optimization of skim milk microfiltration based on expert knowledge

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