Rehydration modeling and characterization of dehydrated sweet corn

Nayi, Pratik; Kumar, Navneet; Kachchadiya, Sagar; Chen, Ho‐Hsien; Singh, Punit; Shrestha, Pratiksha; Pandiselvam, R.

doi:10.1002/fsn3.3303

Cited by 6 publications

(4 citation statements)

References 38 publications

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“…According to Figure4a, the maximum moisture ratio was revealed during the drying of ZD88 at 35 C, followed by 45 and 55 C. While drying RP909, a comparable pattern was observed in Figure4bindicating the moisture removal rate was faster at higher drying air temperatures than at lower ones. This finding was consistent with(Liu et al, 2021;Nayi et al, 2023), where a high moisture removal rate was observed due to a large temperature difference at the boundary of the product, leading to enhanced heat and mass transfer processes at 55 C. Conversely, a reduced drying rate was associated with low temperatures drying at 35 and 45 C. Moreover, Figure5a,b shows increasing the infrared temperature accelerates the rate of drying, enhancing evaporation, and promoting more significant mass and heat transfer rates. However, when the moisture content is low, reducing the drying temperature helps maintain a consistent evaporation rate throughout the drying process(Li et al, 2021).…”

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confidence: 86%

Far‐infrared drying influence on machine learning algorithms in improving corn drying process with graphene irradiation heating plates

Jibril,

Zhang,

Wang

et al. 2024

J Food Process Engineering

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Drying methods often suffer from appropriate drying process modeling, low heating efficiency, and high‐energy consumption. However, graphene may provide significant improvements during the drying process due to its ability to save energy and convert electro‐thermal energy effectively. In this study, random forest (RF), surface vector machine (SVM), artificial neural network (ANN), and k‐nearest neighbor (kNN) were the four machine‐learning algorithms employed to analyze the drying process of two types of corn (ZD88 and RP909) at varying temperatures of 35, 45, and 55°C in a graphene far‐infrared drying. The study found that the drying process of corn showed a decrease in moisture ratio during the falling rate period from 35 to 55°C. The moisture diffusivity increased from 35 to 55°C with the rise in the temperature, ranging from 2.74 to 4.36 × 10−8 m2/s for ZD88 and 2.05 to 3.07 × 10−8 m2/s for RP909. The ZD88 showed a heat efficiency of 81.62% at 55°C. The results of the machine learning algorithms revealed that 1–6 ANN was the best architecture. Normalized PUK was the best for SVM filters. A k = 3 and s‐fold = 5 were the best values for k‐NN and RF, respectively. Among the computed algorithms, the tested data for normalized Pearson universal kernel SVM filters proved to be the most effective in computing the process of drying corn. Overall, a graphene far‐infrared dryer showed improved heating efficiency and less energy consumption, providing a new concept for developing a drying process through machine learning algorithms strategies.Practical applicationsDrying methods often suffer from appropriate drying process modeling, low heating efficiency, and high‐energy consumption. This study provides significant improvements due to the ability of graphene irradiation heating plates to save energy and convert electro‐thermal energy effectively. The study achieved a heating efficiency of 81.62% at an infrared temperature of 55°C. Machine learning algorithms are intelligent models that minimize the limitations in describing the drying process of agricultural materials. The study found the normalized Pearson universal kernel was the most effective computation SVM filter. Therefore, using a graphene far‐infrared dryer showed a better drying alternative for industrial applications, as it has the potential to improve heating efficiency and consume less energy, providing a new concept for developing drying equipment through machine learning algorithms modeling strategies.

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supporting

confidence: 86%

Far‐infrared drying influence on machine learning algorithms in improving corn drying process with graphene irradiation heating plates

Jibril,

Zhang,

Wang

et al. 2024

J Food Process Engineering

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“…The average rehydration ratio of dried SC microwaved for 0, 30, 60, 90, and 120 s were 127.27%, 136.63%, 136.91%, 137.07%, and 136.72%, respectively. Nayi et al ( 2023 ) reported that rehydrated sweet corn obtained from samples treated by microwave‐blanching and drying at 70°C had high maintenance of total sugars, ascorbic acid, geometric mean diameter, and color. The impacts of edible coating and sonication on the rehydration ratio of SC were examined by Salehi, Ghazvineh, and Inanloodoghouz ( 2023 ).…”

Section: Resultsmentioning

confidence: 99%

Influence of microwave pretreatment on the total phenolics, antioxidant activity, moisture diffusivity, and rehydration rate of dried sweet cherry

Salehi,

Inanloodoghouz,

Ghazvineh

2023

Food Science & Nutrition

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The target of this work was to investigate the influence of microwave pretreatments (at five levels of 0, 30, 60, 90, and 120 s) on the total phenolics content, antioxidant potential, mass transfer rate, effective moisture diffusivity (Deff), and rehydration rate of sweet cherries (SC). The drying duration of microwave‐treated SC was shorter than the untreated sample. The average drying time of fresh SC microwaved for 0, 30, 60, 90, and 120 s were 220, 205, 190, 175, and 150 min, respectively. The Deff values, total phenolics, and antioxidant capacity of microwave‐treated SC were higher than the untreated sample. In this study, the SC Deff as determined by the second Fick law varied from 8.73 × 10−10 to 1.41 × 10−9 m2/s. The experimental data for the dehydration curves were fitted to different thin‐layer equations, and the Midilli equation using the experimental constants best described the drying rate of SC. As the microwave pretreatment time increased from 0 to 120 s, the total phenolics and antioxidant capacity of dried SC increased from 1491.4 μg Gallic acid equivalents (GAE)/g dry to 2272.1 μg GAE/g dry, and 54.47%–62.59% (p < .05). The microwave pretreatment enhanced the rehydration rate of dried SC. The rehydration percent of dried SC microwaved for 0, 30, 60, 90, and 120 s were 127.27%, 136.63%, 136.91%, 137.07%, and 136.72%, respectively.

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“…Constant A 2 allows us to predict the maximum capacity of water absorption. The mentioned model has been widely applied to predict the rehydration course of dried biological materials [39][40][41][42][43][44].…”

Section: Model No Model Equation Model Name Referencesmentioning

confidence: 99%

Some Aspects of the Modelling of Dried Red Beets Rehydration Process

Kaleta,

Górnicki,

Obranović

et al. 2024

Applied Sciences

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Some dehydrated products must be rehydrated before consumption or further industry processing. Optimization of the rehydration process needs mathematical models of the process. Despite the widespread use of computers and their associated software, empirical equations are still widely used in view of their simplicity and ease of computation. The mathematical description of the kinetics of mass gain, volume increase, dry matter loss, and moisture content increase and changes of rehydration indices during the rehydration of dried red beets was investigated. The effects of drying air temperature (Td), drying air velocity (vd), characteristic dimension (L), and rehydration temperature (Tr) on model constants were also examined. Red beets cubes (10 mm) and slices (5 and 10 mm) were dried in natural convection (vd = 0.01 m/s), forced convection (vd = 2 m/s), and fluidization (vd = 6 m/s) at Td = 50, 60, and 70 °C. The rehydration was conducted in distilled water at Tr = 20, 45, and 70 °C. The kinetics of rehydrating dried red beets was modelled applying five empirical models: Peleg, Lewis (Newton), Henderson–Pabis, Page, and modified Page. Equations were developed to make the model constants dependent on Td, vd, L, and Tr. Artificial neural networks (ANNs) (feedforward multilayer perceptron) were adopted to condition the rehydration indices on Td, vd, L, and Tr. The following models can be recommended as the most acceptable: (1) the modified Page model for mass gain (RMSE = 0.0236–0.0897) and for volume increase (RMSE = 0.0213–0.0972), (2) the Peleg model for dry mass loss (RMSE = 0.0161–0.610), and (3) the Henderson–Pabis model for moisture content increase (RMSE = 0.0350–0.1062). The ANNs performed the rehydration indices in an acceptable way (RMSE = 0.0528–0.2285). Both the rehydration indices and model constants depended (but to a different degree) on the investigated drying and rehydration conditions.

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Rehydration modeling and characterization of dehydrated sweet corn

Cited by 6 publications

References 38 publications

Far‐infrared drying influence on machine learning algorithms in improving corn drying process with graphene irradiation heating plates

Far‐infrared drying influence on machine learning algorithms in improving corn drying process with graphene irradiation heating plates

Influence of microwave pretreatment on the total phenolics, antioxidant activity, moisture diffusivity, and rehydration rate of dried sweet cherry

Some Aspects of the Modelling of Dried Red Beets Rehydration Process

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