Multiobjective Optimization Approach: Thermal Food Processing

Abakarov, A.; Sushkov, Yu. A.; Almonacid, Sergio; Simpson, Ricardo

doi:10.1111/j.1750-3841.2009.01348.x

Cited by 29 publications

(16 citation statements)

References 45 publications

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“…Optimization using Multi-objective Genetic Algorithms (MOGA). Multi-objective optimization (MOO) has not been often applied in the food industry (Abakarov et al, 2009). One of the earliest studies concerned with the application of MOO was the flow pattern in a multi-effect evaporator system (Nishitani and Kunugita, 1979).…”

Section: Multi-objective Optimization Problemmentioning

confidence: 99%

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

Winiczenko

2018

Emir J Food Agric

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The aim of the paper is to investigate the effect of drying and rehydration parameters on the quality of rehydrated apples and to optimize these parameters based on quality of the rehydrated products. Hybrid artificial neural network (ANN) and multi-objective genetic algorithm (MOGA) method were successfully developed to model, simulate, and optimize the drying and rehydration parameters. This method was applied to the apple tissue, where the simultaneous maximization of the volume ratio (VR) and water absorption capacity (WAC) and the minimization of colour difference (CD) and dry matter loss (DML) were considered. The range of drying and rehydration parameters was: 50-70°C (drying temperature, Td), 0.01-6 m×s-1(drying air velocity, V), and 20-95°C (rehydration temperature, Tr). Additionally, the mathematical formulas for determing the quality parameters of rehydrated apple were developed. The best solution has been found only for: CD, VR, WAC (50.1°C for Td, 0.03 m×s-1 for V, and 67.5°C for Tr). The values of CD, VR, and WAC were predicted as 1.20, 53.2% and 0.57 respectively. It has been observed that DML was always conflicting with other quality parameters.

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Section: Multi-objective Optimization Problemmentioning

confidence: 99%

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

Winiczenko

2018

Emir J Food Agric

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“…Further applications of MOO to simulated moving beds in food processing are shown in [53][54][55]. Abakarov et al [56] have used a multi-objective optimization technique for the thermal sterilization of packaged foods. The quality indicators of apple issues for drying and rehydrated processes using a non-sorting genetic algorithm (NSGA) were described in [57,58].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Mass Diffusion Coefficient and Mass Biot Number Using a Nondominated Sorting Genetic Algorithm

2020

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A precise determination of the mass diffusion coefficient and the mass Biot number is indispensable for deeper mass transfer analysis that can enable finding optimum conditions for conducting a considered process. The aim of the article is to estimate the mass diffusion coefficient and the mass Biot number by applying nondominated sorting genetic algorithm (NSGA) II genetic algorithms. The method is used in drying. The maximization of coefficient of correlation (R) and simultaneous minimization of mean absolute error (MAE) and root mean square error (RMSE) between the model and experimental data were taken into account. The Biot number and moisture diffusion coefficient can be determined using the following equations: Bi = 0.7647141 + 10.1689977s − 0.003400086T + 948.715758s 2 + 0.000024316T 2 − 0.12478256sT, D = 1.27547936·10 −7 − 2.3808·10 −5 s − 5.08365633·10 −9 T + 0.0030005179s 2 + 4.266495·10 −11 T 2 + 8.33633·10 −7 sT or Bi = 0.764714 + 10.1689091s − 0.003400089T + 948.715738s 2 + 0.000024316T 2 − 0.12478252sT, D = 1.27547948·10 −7 − 2.3806·10 −5 s − 5.08365753·10 −9 T + 0.0030005175s 2 + 4.266493·10 −11 T 2 + 8.336334·10 −7 sT. The results of statistical analysis for the Biot number and moisture diffusion coefficient equations were as follows: R = 0.9905672, MAE = 0.0406375, RMSE = 0.050252 and R = 0.9905611, MAE = 0.0406403 and RMSE = 0.050273, respectively.

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“…Benefits of VRT processes may include a better retention of the nutritional and sensory quality of the food, and a reduction in processing time (Chen and Ramaswamy ; Abakarov et al . ); however, previous studies have been based mainly on simulation and optimization of the temperature of retort profiles (103–140C), frequently using quality kinetic parameters obtained from the literature. As an example, Durance et al .…”

Section: Introductionmentioning

confidence: 99%

Variable Retort Temperature Profiles for Canned Papaya Puree

Avila-Gaxiola

Delgado‐Vargas

Zazueta‐Niebla

et al. 2015

J Food Process Engineering

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Constant retort temperature (CRT) processes for thermally treated foods are usually time-consuming and have drastic impact on food quality. Variable retort temperature (VRT) processes may decrease the processing time and may retain better the food nutritional quality. Five isolethal profiles (one CRT and four VRT) for preparing papaya puree (pH 3.8) were designed using pectin methyl esterase as the target enzyme for thermal processing, and their effect on the puree quality was evaluated. The objective was to compare these profiles based on processing time, vitamin C retention, consistency index and color. The VRT isolethal profiles reduced the processing time up to 33.3% compared with CRT, whereas vitamin C, consistency index and chroma were retained up to 65, 71 and 89%, respectively, compared with fresh puree. The best resulting treatment was a VRT upstairs profile (75C/19 min, 80C/8.5 min, 90C/10.7 min and 6C/20.8 min). The results show that the use of VRT profiles allows retaining the nutritional quality of canned papaya puree. PRACTICAL APPLICATIONSThis research explored the use of variable retort temperature to increase product quality and improve processing time in canning of papaya puree. In an upstairs profile, 65% of vitamin C was retained compared with fresh product. The results would be useful for canning of conduction-heated foods.

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Multiobjective Optimization Approach: Thermal Food Processing

Cited by 29 publications

References 45 publications

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

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

Evaluation of the Mass Diffusion Coefficient and Mass Biot Number Using a Nondominated Sorting Genetic Algorithm

Variable Retort Temperature Profiles for Canned Papaya Puree

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