Dehydration–rehydration mechanism of vegetables at the cell-wall and cell-membrane levels and future research challenges

Wang, Bixiang; Li, Yue; Lv, Ying; Xuan, Jianhua; Wang, Zhitong; He, Yang; Lian-kui, Wen

doi:10.1080/10408398.2023.2233620

Cited by 3 publications

(1 citation statement)

References 113 publications

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“…Symbiotic transport can lead to damage to the cell membranes, but it also speeds up the drying process. Examining the mechanism of dehydration and rehydration in vegetables can enhance processing techniques and stimulate innovation in their use (Wang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Kinetic and artificial neural network modeling of dried black beauty eggplant (Solanum melongenaL.) slices during rehydration

İnan‐Çınkır,

Süfer,

Pandiselvam

2024

J Food Process Engineering

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This paper targeted to investigate rehydration characteristics of dehydrated black beauty eggplant (which was dried under sun, by hot air, microwave, and infrared) at 25, 50, and 75°C, and to analyze rehydration ratio (RR) by artificial neural network (ANN) approach. RRs of both microwave‐processed specimens (between 5.66 and 7.91) and sun‐dried ones (between 5.38 and 6.37) were always greater than the others, at all rehydration temperatures. Rehydration temperature had an enhancing effect on RRs, and the image that closely resembled the fresh sample was captured in the microwave‐dried rehydrated samples. Nearly 180–240 min was adequate for rehydration of all slices. Zeroth‐ and first‐order kinetic models, Peleg, Peppas, and two‐term exponential decay models, as well as a new rational model were tested to describe rehydration kinetics. Two‐term equation was almost superior with high R2 (between 0.9734 and 0.9994) and the lowest root mean square error (RMSE) and χ2. Novel recommended mathematical expression was also successful (R2: 0.9679–0.9989, χ2: 0.0051–0.0975, RMSE: 0.0191–0.0866). Regarding relationship between actual and predicted RRs and performance indices of ANN equation, overall R and R2 were recorded as follows: 0.9975–0.9950 (sun‐dried) > 0.99642–0.9929 (hot air‐dried) > 0.9955–0.9911 (infrared‐dried) > 0.9907–0.9815 (microwave‐dried), respectively. The proposed ANN model and novel mathematical formula not only offer a considerable potential in predicting rehydration patterns and developing rehydration protocols in food production sector, but also contribute to save energy by completely understanding the process and optimizing conditions.Practical applicationsThe eggplant is a cherished vegetable often dried, providing a velvety texture and rich taste in Anatolian recipes. In Anatolia, colorful vegetables are strung together on strings, adorning traditional houses, and serving as a testament to the region's deep‐rooted cultural heritage. The Turkic people in Central Asia demonstrated remarkable skill in preserving plants by carefully drying leaves, stems, or roots. The process of rehydration entails the addition of water to dehydrated eggplant slices in order to restore their initial dimensions and attributes for being consumed. This investigation integrates the utilization of ANNs to simulate the rehydration procedures of eggplant. This simulation can be employed for the purpose of quality control and standardization within the food sector. Hence, food manufacturers can anticipate the rehydration characteristics of their products across various production batches and conditions, thereby guaranteeing adherence to quality benchmarks.

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Section: Introductionmentioning

confidence: 99%

Kinetic and artificial neural network modeling of dried black beauty eggplant (Solanum melongenaL.) slices during rehydration

İnan‐Çınkır,

Süfer,

Pandiselvam

2024

J Food Process Engineering

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Dehydration–rehydration vegetables: Evaluation and future challenges

Wang,

Jia,

et al. 2023

Food Chemistry: X

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Suitable Model for Rehydration of Dried Red Beets: Effect of Solid-to-Liquid Ratio on Rehydration Characteristics

Kaleta,

Górnicki,

Obranović

et al. 2024

Applied Sciences

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Rehydration of food products was carried out in various amounts of liquid. The effects of the solid-to-liquid ratio on rehydration characteristics of dried red beets were investigated. Six values of the solid-to-liquid ratio (1:20, 1:15, 1:10, 1:5, 1:3, and 1:1) were taken into consideration. Red beets cubes (10 mm) were dried in a forced convection (2 m/s; 60 °C) and then rehydrated in distilled water at a temperature of 20 °C. The kinetics of mass gain, dry matter loss, water gain, moisture content increase, and volume increase were modeled using the empirical Peleg model which parameters have physical meanings. Equations were formulated to make the Peleg model parameters dependent on the solid-to-liquid ratio. The obtained results indicated that the amount of rehydrating water influenced the values of the model parameters and the course of dried red beets rehydration and helped explain the rehydration kinetics. The Peleg model can be recommended for describing the kinetics of mass gain (R2 = 0.9662–0.9895), dry matter loss (R2 = 0.7042–0.9881), water gain (R2 = 0.9732–0.9913), moisture content increase (R2 = 0.9770–0.9929), and volume increase (R2 = 0.9374–0.9847) during the rehydration of dried red beet cubes at all considered values of the solid-to-liquid ratio.

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Dehydration–rehydration mechanism of vegetables at the cell-wall and cell-membrane levels and future research challenges

Cited by 3 publications

References 113 publications

Kinetic and artificial neural network modeling of dried black beauty eggplant (Solanum melongenaL.) slices during rehydration

Kinetic and artificial neural network modeling of dried black beauty eggplant (Solanum melongenaL.) slices during rehydration

Dehydration–rehydration vegetables: Evaluation and future challenges

Suitable Model for Rehydration of Dried Red Beets: Effect of Solid-to-Liquid Ratio on Rehydration Characteristics

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