Apple Cubes Drying and Rehydration. Multiobjective Optimization of the Processes

Winiczenko, Radosław; Górnicki, Krzysztof; Kaleta, Agnieszka; Janaszek-Mańkowska, Monika; Choińska, Aneta; Trajer, Jędrzej

doi:10.3390/su10114126

Cited by 8 publications

(10 citation statements)

References 49 publications

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“…In the previous research about multi-objective normalization of apple drying [31] and drying and rehydration [32] the critical factors-drying time and energy consumption for drying were not considered. Taking these factors into account is very important from the point of view of material acquisition costs.…”

Section: Mogamentioning

confidence: 99%

“…In literature the multi-objective optimization of apple drying was done [31,32]. The simultaneous minimization of color difference, maximization of the volume ratio, and water absorption capacity was successfully applied.…”

Section: Introductionmentioning

confidence: 99%

“…The simultaneous minimization of color difference and volume ratio and the maximization of water absorption capacity were considered. The optimal solution: drying air temperature, air velocity, rehydration temperature and kind of rehydrating medium were conducted in [32].…”

Section: Introductionmentioning

confidence: 99%

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Application of a MOGA Algorithm and ANN in the Optimization of Apple Drying and Rehydration Processes

2021

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The aim of the study was to estimate the optimal parameters of apple drying and the rehydration temperature of the obtained dried apple. Conducting both processes under such conditions is aimed at restoring the rehydrated apple to the raw material properties. The obtained drying parameters allow the drying process to be carried out in a short drying time (DT) and at low energy consumption (EC). The effect of air velocity (vd), drying temperature (Td), characteristic dimension (CD), and rehydration temperature (Tr) on rehydrated apple quality was studied. Quality parameters of the rehydrated apple as: color change (CC), mass gain ratio (MG), solid loss ratio (SL), volume gain ratio (VG) together with DT and EC were taken into consideration. The artificial neural network was used for modeling of rehydrated apple quality parameters, DT, and EC. A multi-objective genetic algorithm was developed in order to optimize parameters of the drying and rehydration processes. The simultaneous minimization of CC, SL, DT, EC, and the maximization of MG and VG were considered with the following drying and rehydration processes parameters: Td: 50–70 °C, vd: 0.01–2 m/s, Tr: 20–70 °C. The best solution has been found at drying temperature 56.1 °C, air velocity 1.3 m/s, characteristic dimension 2.0 mm, and rehydration temperature 59.2 °C. This apple drying and rehydration resulted in MG = 3.51, SL = 0.57, VG = 4.77, CC = 11.2, DT = 5.4 h, EC = 159.8 GJ/kg. The parameters of apple drying and rehydration processes can be recommended for the industry application.

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Section: Mogamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of a MOGA Algorithm and ANN in the Optimization of Apple Drying and Rehydration Processes

2021

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“…It is known that different drying methods and conditions can result in large quality differences of dried and rehydrated products [ 3 , 4 , 5 , 6 , 7 , 8 , 9 ]. As the major drying methods in industrial production, hot air drying (HA) and freeze drying (FD) have been investigated from both the academic and industrial points of view for many years.…”

Section: Introductionmentioning

confidence: 99%

“…However, both methods still have problems in producing desired products with good rehydration quality. In particular, hot air drying (HA)leads to slow rehydration and tough texture [ 8 , 9 ]. While freeze-drying (FD) shows good rehydration and volume retention, texture is considered too fragile when eaten [ 10 , 11 , 12 , 13 ], which makes freeze-dried products less attractive for consumers.…”

Section: Introductionmentioning

confidence: 99%

Study on the Rehydration Quality Improvement of shiitake Mushroom by Combined Drying Methods

Xia

Yang

et al. 2021

Foods

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The aim of study is to improve the rehydration quality of dried shiitake mushrooms for their instant food manufacturers and fast restaurants. Serial combined drying methods were investigated to achieve this objective: either instant controlled pressure drop drying (DIC) or freeze drying (FD) was used as the treatments for microstructure improvement, and they were applied either before or after an additional drying step at low (35 °C) or high (65 °C) temperatures. Dried mushrooms were assessed for quality indicators like relative volume, rehydration rate, dry matter loss and sensory scores. Microstructure properties were inferred to understand the physical mechanisms of quality changes. Principal component analysis (PCA) was used to cluster treatments and to identify combinations of drying techniques, rendering improved quality. Consequently, it was shown that DIC treatment before hot air drying at 35 °C was shown to be the most promising combined drying method to enhance the rehydration quality, leading to a high volume recovery ratio, low dry matter loss after rehydration, and high rehydration rates. This good performance could be explained by the retention of pore interconnectivity resulting from the slight expansion of porous structure during DIC and the retention of cell membrane integrity.

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Optimization of frying process for maintaining nutritional quality to satisfy consumers' sensory attributes: A novel application of multi‐criteria decision‐making approach

Sadhu

Lahiri

Roy

et al. 2022

Multi Criteria Decision Anal

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The objective of the study was to assess the optimum frying condition of fish considering the multiple perspectives (retention of nutritional quality indices [NQI], reduction of preparation time, and improvement of health benefit) to satisfy consumerpreferred sensory attributes by controlling the most impactful process variables (temperature, time, and oil amount). The multi-criteria decision-making (MCDM) approach is appropriate to handle the numerous conflicting criteria and numerous multiple objectives. First, an artificial neural network (ANN) model was developed to build a nonlinear correlation between the cooking process parameters and NQI by an automatic exhaustive search of all available algorithms and activation functions. All the NQI are conflicting in nature. Therefore, the ANN-based multi-objective genetic algorithm was implemented to obtain the Pareto optimal solutions to improve all NQI simultaneously. Five optimised conditions were selected amongst the Pareto optimal solutions, satisfying the above-mentioned multiple criteria. Finally, a well-known MCDM approach, the analytical hierarchy process (AHP), was applied for sensory analysis to evaluate the overall acceptance of the optimised conditions based on the relative importance of consumers' general sensory modalities (flavour, colour & appearance, and taste). Furthermore, the following condition (140.01 C, 7.62 min, 47.87 ml oil/kg of fish) was selected as the most accepted in terms of all quality attributes that may be implemented as the standard condition in domestic and industrial purposes.

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Apple Cubes Drying and Rehydration. Multiobjective Optimization of the Processes

Cited by 8 publications

References 49 publications

Application of a MOGA Algorithm and ANN in the Optimization of Apple Drying and Rehydration Processes

Application of a MOGA Algorithm and ANN in the Optimization of Apple Drying and Rehydration Processes

Study on the Rehydration Quality Improvement of shiitake Mushroom by Combined Drying Methods

Optimization of frying process for maintaining nutritional quality to satisfy consumers' sensory attributes: A novel application of multi‐criteria decision‐making approach

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