Thin-layer catalytic far-infrared radiation drying and flavour of tomato slices

Abano, Ernest Ekow; Le, Hai; Qu, Wenjuan

doi:10.4081/jae.2014.226

Cited by 15 publications

(20 citation statements)

References 27 publications

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“…It was further revealed that as sample thickness increased, the conductive resistance of the sample proliferated, thus leading to reduction of final product temperature (Figure a). The result is in agreement with previous studies such as infrared drying of tomato samples (Abano et al, ), and apple samples (Nowak & Lewicki, ).…”

Section: Resultssupporting

confidence: 93%

“…The drying rates displayed the significant difference with different irradiation distances (Figure ), which was similar to result reported by Abano et al () and Nowak and Lewicki () and was contrary to result reported Cao et al (). The different results might be owing to the difference in distances and materials used.…”

Section: Resultssupporting

confidence: 70%

“…The effect of the sample thickness on the drying time can be interpreted as that increasing the thickness resulting in increased diffusion path of moisture within the samples and following that the increase the conductive resistance and the moisture gradient of the sample due to it led to an extension in drying time. It would seem that with an increase in the sample thickness, the rate of moisture transfer reduces owing to an enhancement of mass transfer resistance (Figures and a), thereby leading to the higher moisture content of infrared‐dried pumpkin samples, as found in previous studies for fruits and vegetables (Abano et al, ; Doymaz, ; Nowak & Lewicki, ; Sadin et al, ; Sharma et al, ).…”

Section: Resultsmentioning

confidence: 63%

“…Moisture diffusivity is representing of the moisture flow within foodstuff during drying. Especially, in the falling rate period of drying, moisture transfer happens largely via molecular diffusion (Abano et al, ). The

D_{eff}

values are designated by plotting empirical drying data of each run in terms of

\ln M R

against drying time (Equation 4) and following that, the

E_{a}

value can be calculated by performing linear regression analysis and plotting

\ln D_{eff}

against

1 / T

(Equation 5) and or plotting

\ln D_{eff}

against

m / P_{m}

at a given thickness of samples (Equation 6) depending on the available data.…”

Section: Effective Moisture Diffusivitymentioning

confidence: 99%

“…In spite of ignoring shrinkage during analytical solving, diffusion models able to explain drying kinetics satisfactorily to the experimental data (da Silva et al, ). Equation (2) can be reduced for sufficiently long drying times (

M R < 0.6

) as follows (Abano et al, ; Celma et al, ; Corrêa et al, ; Doymaz, , , ):

M R = \frac{8}{π^{2}} \exp (- \frac{π^{2} D_{eff} t}{L^{2}})

…”

Section: Introductionmentioning

confidence: 99%

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Mathematical modeling of infrared radiation thin‐layer drying of pumpkin samples under natural and forced convection

Sadeghi

Movagharnejad

Asl

2019

J Food Process Preserv

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A comparative study was carried out between a large number of mathematical models and artificial neural networks to estimate the drying curves. Diamente et al. model and modified two‐term exponential‐V model were determined as the best ones describing drying curves for natural and forced drying air systems, respectively. The ANNs with 4‐9‐9‐1 and 3‐9‐1 topologies, log‐sigmoid, and hyperbolic tangent sigmoid transfer functions, and Levenberg–Marquardt training algorithm presented the best results for the former and latter systems, respectively. Furthermore, it was found that ANN modeling had much better performance in prediction of drying curves with respect to statistical analyses. Moisture diffusivity values were obtained in a range of 0.932×10-10-11.976×10-10normalm2/s and 2.209×10-10-9.848×10-10normalm2/s for the systems, respectively. Activation energies were determined as 88,509, 60,344, and 44,806 W/kg for the former system and 36.88, 29.66, and 18.59 kJ/mol for the latter system with an increase in the thickness of samples. Practical applications The main aim of drying food products is the reduction of moisture content up to a certain level to achieve the smallest possible amount of the microbiological spoilage. This process has a considerable effect on the drying kinetics and quality of the dried product as well. Infrared (IR) heating, as an alternative drying method for agricultural products, is efficient to preserve the main characteristics and to shorten drying time. It is important to investigate the effect operating parameters of IR dryer on the mentioned cases. Our studies have clearly displayed that IR heating of pumpkin samples can result in high heating rate and fast drying. Therefore, this alternative approach could be employed as an energy saving drying method along with improved drying efficiency and better product quality in comparison with the common drying methods. Study results are useful for producers of not only dried pumpkins but also other agricultural products.

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Section: Resultssupporting

confidence: 93%

Section: Resultssupporting

confidence: 70%

Section: Resultsmentioning

confidence: 63%

D_{eff}

values are designated by plotting empirical drying data of each run in terms of

\ln M R

against drying time (Equation 4) and following that, the

E_{a}

value can be calculated by performing linear regression analysis and plotting

\ln D_{eff}

against

1 / T

(Equation 5) and or plotting

\ln D_{eff}

against

m / P_{m}

at a given thickness of samples (Equation 6) depending on the available data.…”

Section: Effective Moisture Diffusivitymentioning

confidence: 99%

M R < 0.6

) as follows (Abano et al, ; Celma et al, ; Corrêa et al, ; Doymaz, , , ):

M R = \frac{8}{π^{2}} \exp (- \frac{π^{2} D_{eff} t}{L^{2}})

…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Mathematical modeling of infrared radiation thin‐layer drying of pumpkin samples under natural and forced convection

Sadeghi

Movagharnejad

Asl

2019

J Food Process Preserv

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Mathematical modelling of infrared‐dried kiwifruit slices under natural and forced convection

Sadeghi

Asl

Movagharnejad

2019

Food Science & Nutrition

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In this work, the effect of the radiation intensity, slice thickness, and the distance between slices and infrared lamps under natural drying air and the effect of slice thickness and air velocity under forced drying air on the moisture diffusion characteristics and the drying rate of kiwifruit slices during infrared drying were investigated. The drying of kiwifruit happened in the falling rate period, and no constant‐rate period was observed in the drying curves. One hundred models were fitted to the drying data. Among the models, the exponential dsecay function model and modified two‐term exponential‐V model and the artificial neural networks with 4‐5‐7‐1 and 3‐5‐5‐1 topologies, hyperbolic tangent sigmoid transfer function, and Levenberg‐Marquardt training algorithm presented the best results and showed the goodness of fit with the experimental data for the former and latter systems, respectively. The diffusivities varied between 1.216 × 10−10–8.997 × 10−10 m2⁄s and 2.567 × 10−10–10.335 × 10−10 m2⁄s for natural and forced drying air systems, respectively.

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Effect of infrared drying on chemical and microbial properties of Cold‐Hardy grape pomace (Edelweiss and Marquette)

Shad

Venkitasamy

Kuelbs

et al. 2023

Food Science & Nutrition

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This study aims to add value to a common wine industry waste by preserving bioactive compounds in cold‐hardy grape pomace (GP) and preventing any microbial growth under the proper drying conditions. Effects of infrared (IR) and hot air (HA) drying on the microbial and physicochemical properties such as color, phenolic compounds, and antioxidant activity of white (Edelweiss) and red (Marquette) GP were studied. The IR heating rapidly reduced the moisture content of GP from 55% wet basis (w.b.) to less than 10%, which resulted in a drying time reduction of 71.9% to 80.2% compared to HA drying. There were no significant differences in color parameters among the IR‐ and HA‐dried pomaces (p > .05). The phenolic content of ‘Edelweiss’ pomace was not significantly affected by both IR and HA drying, whereas the phenolic content of ‘Marquette’ pomace was substantially reduced from 274 mg/g dry extract in raw pomace to 127 mg/g dry extract after HA drying and to 141.9 mg/g dry extract after IR drying. Overall, the microbial load on the fresh pomace samples was dramatically reduced by IR heating, with a reduction of more than 99.9% when the pomaces were dried using IR at a temperature higher than 130°C. However, this high temperature of IR led to a significant reduction of DPPH antiradical scavenge activity for ‘Edelweiss’ pomace (p < .05). This study shows that using the IR approach, cold‐hardy ‘Edelweiss’ and ‘Marquette’ grape pomaces can be efficiently dried with the antioxidant activity maintained, which could be used in a variety of food products as a functional ingredient.

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Thin-layer catalytic far-infrared radiation drying and flavour of tomato slices

Cited by 15 publications

References 27 publications

Mathematical modeling of infrared radiation thin‐layer drying of pumpkin samples under natural and forced convection

Mathematical modeling of infrared radiation thin‐layer drying of pumpkin samples under natural and forced convection

Mathematical modelling of infrared‐dried kiwifruit slices under natural and forced convection

Effect of infrared drying on chemical and microbial properties of Cold‐Hardy grape pomace (Edelweiss and Marquette)

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