Effects of solar tunnel drying zones and slice thickness on the drying characteristics of taro (<i>Colocasia esculenta</i> (L.) Schott) slice

Molla, Ehtenesh Tilahun; Teka, Tilahun A.; Taye, Addisalem Hailu

doi:10.1002/fsn3.3175

Cited by 4 publications

(5 citation statements)

References 27 publications

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“…(2) Moisture Ratio . Concerning drying data analysis, the moisture ratio (MR) is essential for describing the various models of thin-layer drying and determined using [ 14 ]

where MR is the dimensionless moisture ratio, M is the moisture content at any time (%), M e is the equilibrium moisture content (%), and M 0 is the initial moisture content (%) wet basis.…”

Section: Methodsmentioning

confidence: 99%

“…( 4 )); the R 2 value was computed using the RSQ function of Microsoft Excel. For the best fit, R 2 value should be higher and RMSE and χ 2 values should be lower [ 14 ].

…”

Section: Methodsmentioning

confidence: 99%

“…(2) Moisture Ratio. Concerning drying data analysis, the moisture ratio (MR) is essential for describing the various models of thin-layer drying and determined using [14]…”

Section: Physical Property and Drying Characteristicsmentioning

confidence: 99%

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Harmonizing Drying Time, Layer Thickness, and Drier Zones for Drying Kinetics: Quality and Safety of Solar Tunnel-Dried Wet-Processed Parchment Coffee (Coffea arabica L.)

Abdissa,

Tola,

Taye

et al. 2023

International Journal of Food Science

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Tunnel solar dryer is the recently used drying method for better quality and safety of parchment coffee. However, the higher variation of drying temperature and RH along the long tunnel solar dryer results in a heterogeneous environment in the tunnel, which could make parchment coffee dried at different times or with different moisture contents. This study is aimed at investigating the effect of solar tunnel dryer zones at different zones of the dryer, divided into three zones from the inlet to the exit side of the drier and drying layer thicknesses on the drying time, drying kinetics, physicochemical, sensory, and fungal growth loads of parchment coffee. Furthermore, seven mathematical models were evaluated to select the best-fitting model for a specific zone to predict drying time. Results showed that dryer zones significantly ( p < 0.05 ) interacted with layer thickness for most of the measured parameters except titratable acidity and sensory properties. The dryer zone, coupled with the reduction in drying layer thickness, caused an increase in effective diffusivity and moisture removal rate and reduced drying time. The drying time to reach constant moisture content varied from 14 to 17 hours. Overall raw bean, cup, and total quality varied from 36.3 to 37, 48 to 51, and 84.3 to 87.3%, respectively. Values for physicochemical parameters ranged from 5.3 to 6.9 (pH), 2.1 to 2.6% (titratable acidity), 2.3 to 4.3°Brix TSS, 10.9 to 15.2% (ether extract), 39.2 to 53.5GAE/g (total phenolic content), and 38.5 to 59.2 (DPPH scavenging capacity). The fungal infection percentage at the end of drying varied from 4 to 93.3%, which could be associated with potential mycotoxin formation if recommended conditions were not maintained. In general, for better quality, similar drying times, and a lesser fungal load, it is recommended to use 4, 5, and 6 cm layer thickness in zones one, two, and three, respectively. The drying kinetics of parchment coffee in different dryer zones with different drying layer thicknesses showed variation. Zone one at 2 and 4 cm layer thicknesses is best described by the Verma model. Four- and six-centimetre layer thicknesses in zones 2 and 3 are best described by the modified Midilli model.

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“…(2) Moisture Ratio . Concerning drying data analysis, the moisture ratio (MR) is essential for describing the various models of thin-layer drying and determined using [ 14 ]

where MR is the dimensionless moisture ratio, M is the moisture content at any time (%), M e is the equilibrium moisture content (%), and M 0 is the initial moisture content (%) wet basis.…”

Section: Methodsmentioning

confidence: 99%

“…( 4 )); the R 2 value was computed using the RSQ function of Microsoft Excel. For the best fit, R 2 value should be higher and RMSE and χ 2 values should be lower [ 14 ].

…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Harmonizing Drying Time, Layer Thickness, and Drier Zones for Drying Kinetics: Quality and Safety of Solar Tunnel-Dried Wet-Processed Parchment Coffee (Coffea arabica L.)

Abdissa,

Tola,

Taye

et al. 2023

International Journal of Food Science

Self Cite

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show abstract

“…In addition, a smaller cutting size results in less resistance to moisture transfer and a faster evaporation rate [22]. Conversely, a larger cutting size increases the pathway for moisture to diffuse out of the taro, reducing the evaporation rate [23]. Due to the increase in heat transfer resistance, the drying rate decreased with the increase in cutting size, and therefore the drying time was prolonged.…”

Section: Drying Characteristicsmentioning

confidence: 99%

Efficient Production of High-Quality Infrared-Assisted Spouted Bed-Dried Areca taro Based on the Drying Temperature and Cutting Size Control

Zhao,

2024

Foods

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The purpose of this study was to apply infrared-assisted spouted bed drying (IRSBD) technology for Areca taro drying and to investigate the effects of different parameters on its drying quality. Specifically, in order to determine the suitable conditions for IRSBD, the effects of different drying temperatures (45 °C, 50 °C, 55 °C, and 60 °C) and cutting sizes (6 × 6 × 6 mm, 8 × 8 × 8 mm, 10 × 10 × 10 mm, and 12 × 12 × 12 mm) on the drying characteristics, temperature uniformity, and quality properties (including colour, rehydration ratio, total phenol content, total flavonoid content, and antioxidant activity) of Areca taro were studied. The results showed that the optimal drying condition was the sample with a cutting size of 10 × 10 × 10 mm and drying at 50 °C, which yielded the dried sample with the best colour, highest total phenol and flavonoid contents, maximum antioxidant capacity, and rehydration ratio.

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“…Generally, convective dryers are preferred for industrial scale drying of various fruits and vegetables due to its simple constructional design and least expensive compared to other reported dryers including vacuum dryer, freeze dryer, solar dryer, etc. (Bhagya Raj & Dash, 2020;Molla et al, 2023;Salehi, 2023). The dehydration operation is a complex phenomenon that comprises simultaneous heat and mass transfer (HMT) between the sample domain and the adjacent drying medium (air).…”

Section: Introductionmentioning

confidence: 99%

Finite element modeling in heat and mass transfer of potato slice dehydration, nonisotropic shrinkage kinetics using arbitrary Lagrangian–Eulerian algorithm and artificial neural network

Das,

Prasad

2024

J Food Process Engineering

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The present study optimized the drying temperature (50–80°C) for potato slices based on color, texture, and visual observations. At an optimized temperature (60°C), a 2D axisymmetric finite element method (FEM) was developed in COMSOL Multiphysics to predict the heat and mass transfer (HMT) in a disk‐shaped potato slice. The nonisotropic shrinkage was predicted for the potato slice by the arbitrary Lagrangian–Eulerian approach. The experimental dehydration results revealed that axial shrinkage (27.44%) was 2.5 times higher than radial shrinkage (67.39%). The simulated outcomes based on FEM revealed the realistic visualization of spatial heat transfer, moisture migration, and nonisotropic slice deformation. The predicted moisture content, surface temperature, and shrinkage properties were in good agreement with the experimental results. The shrinkage behavior was further validated using artificial neural network (ANN) to simulate the slice shrinkage. Results showed that both the COMSOL and ANN approaches can precisely predict the shrinkage‐dependent HMT model. The ANN model outperformed the COMSOL determined by mean absolute error, mean square error (MSE), root MSE, and Chi‐square (χ2) values. The successful application of the presented approach for determining dehydration characteristics may have potential for quality assessment and management of different fruits and vegetables.Practical applicationsThis journal article explores the practical industrial applications of combining finite element method (FEM)‐based heat and mass transfer model and artificial neural networks (ANNs) to improve the efficiency and quality of food drying processes. FEM is employed to simulate and predict the realistic visualization of heat and mass transfer phenomena along with non‐isotropic shrinkage, while ANN serves as a data‐driven modeling tool for process control and prediction. The integration of these two technologies offers significant advantages in the food industry, including quantification of precise temperature and moisture content, as well as to monitor the drying process of various food products, reduced energy consumption, and time.

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Effects of solar tunnel drying zones and slice thickness on the drying characteristics of taro (Colocasia esculenta (L.) Schott) slice

Cited by 4 publications

References 27 publications

Harmonizing Drying Time, Layer Thickness, and Drier Zones for Drying Kinetics: Quality and Safety of Solar Tunnel-Dried Wet-Processed Parchment Coffee (Coffea arabica L.)

Harmonizing Drying Time, Layer Thickness, and Drier Zones for Drying Kinetics: Quality and Safety of Solar Tunnel-Dried Wet-Processed Parchment Coffee (Coffea arabica L.)

Efficient Production of High-Quality Infrared-Assisted Spouted Bed-Dried Areca taro Based on the Drying Temperature and Cutting Size Control

Finite element modeling in heat and mass transfer of potato slice dehydration, nonisotropic shrinkage kinetics using arbitrary Lagrangian–Eulerian algorithm and artificial neural network

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