Optimization of the microwave drying process for potato chips based on the measurement of dielectric properties

Luo, Guiyi; Song, Cuihua; Hongjie, Pu; Li, Zhenfeng; Xu, Wanxiu; Raghavan, G.S.V.; Chen, Haiying

doi:10.1080/07373937.2018.1500482

Cited by 40 publications

(20 citation statements)

References 40 publications

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“…Also, the results of Monteiro et al (2020) demonstrated even when applying very low fixed power (incremental power density) during the microwave drying process led to burn the product surface but this study revealed that applying high power with the decremental power density and moderated power with fixed power density led to prevent the burning of products. Similar results also were reported by Luo et al (2019) that decreasing microwave power with moisture content necessitated for preventing the burning and controlling the product temperature. Our results also indicated that the burning of banana slices was happened in the center of the slices.…”

Section: Resultssupporting

confidence: 89%

“…After that, microwave power is lowered and moderated to an appropriate level to avoid the overheating effect which can deteriorate the product quality (Mousa & Farid, 2002). In recent decades, many researchers applied this method for avoiding the risk of overheating, burning and scorching of products (Botha, Oliveira, & Ahrne, 2012; Luo et al, 2019; Pereira, Marsaioli Jr., & Arhne, 2007; Wang & Xi, 2005; Wray & Ramaswamy, 2015). Also, assessment of the variable microwave power in comparison to the constant microwave power during the drying process demonstrated that variable microwave power was more suitable for drying and charring of the product could be reduced with this technique (Ahrne, Pereira, Staack, & Floberg, 2007; Li et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Real‐time evaluation of artificial neural network‐developed model of banana slice kinetics in microwave‐hot air dryer

Sabzevari

Behroozi‐Khazaei

Darvishi

2021

J Food Process Engineering

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The required microwave power input and moisture content prediction in microwave‐hot air dryer are challenging. In this study, two artificial neural networks (ANN) were investigated for modeling of moisture content of banana slices and required microwave power input. The experiments were done in five levels of microwave power density (MPD) (4, 5, 6, 7, and 8 W/g) with fixed mode and two levels (6 and 8 W/g) with variable mode at 40°C hot air temperature. The initial MPD, mode, and time were used as input variables in both ANN models. The ANN with 3–5–5–1 structure had best performance for MC modeling with 0.0148 of RMSE and 0.9998 of R for testing data. Also, the ANN with 3–3–1 structure could predict the microwave power with 14.013 of RMSE and 0.9929 of R for testing data. Real‐time verification of developed models showed that developed ANN models could predict microwave power and MC with fixed MPD with good reliability and confidence for online drying control but did not reasonably satisfy for variable MPD. Also, the appearance quality of the banana slices was monitored by machine vision. The results showed that the percentage of banana charring at the fixed MPDs of 6, 7, and 8 is equal to 12%, 24%, and 29%, respectively, whereas no burns were observed at the other power density levels. Practical applications The main benefit of microwave drying is volumetric and high‐efficiency heating. But the temperature of the product at the end of drying process increased and brunt the product. Using the variable microwave power program is one of the famous methods for temperature control to prevent scrunching and burning the product. But powerful models for predicting the moisture content and microwave power during the drying process are not available. In this study, the drying kinetic of banana slices and input microwave power in the hybrid microwave‐hot air dryer under constant and variable microwave power was investigated and real‐time evaluating of the developed models for dryer control was examined. The results expressed the real‐time evaluation of the developed model had great accuracy and reliability and can be used for microwave dryer control.

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Section: Resultssupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Real‐time evaluation of artificial neural network‐developed model of banana slice kinetics in microwave‐hot air dryer

Sabzevari

Behroozi‐Khazaei

Darvishi

2021

J Food Process Engineering

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“…When the hot air temperature increased, the antioxidant substances in the cabbages, including anthocyanin, polyphenols, and flavonoids, caused severe heat damages and intensified the Maillard reaction (Xia, 2013 ). The similar behaviors were also reported in apple slices, Asparagus officinalis , white button mushroom slices, and potato chips (Baltacioglu, 2017 ; Doymaz, 2014 ; Horuz et al., 2018 ; Luo et al., 2019 ).…”

Section: Resultssupporting

confidence: 75%

Optimization of combined microwave and hot air drying technology for purple cabbage by Response Surface Methodology (RSM)

Jing

Yang

et al. 2021

Food Science & Nutrition

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“…Similar to our study, dielectric properties were reduced with decreasing water content. Luo et al. (2019) dried potato slices using microwave and examined dielectric properties during drying.…”

Section: Resultsmentioning

confidence: 99%

Optimization of ohmic heating-assisted osmotic dehydration as a pretreatment for microwave drying of quince

Kutlu

2021

Food sci. technol. int.

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In this study, the ohmic heating system was used as a novel application for osmotic dehydration of quince. After osmotic dehydration, samples were dried by microwave. In this regard, the effects of process variables such as electrical field strengths (20, 30 and 40 V/cm), holding time (10, 20 and 30 minutes), microwave power (90, 180 and 270 W) and sucrose concentration (0%, 25% and 50%) on dielectric constant, dielectric loss factor, rehydration ratios, total phenolic compounds and color values were investigated by response surface methodology (RSM). Optimum conditions were found as 40 V/cm electrical field strength, 30 min holding time, 16.67% sucrose concentration and 270 W microwave power. Total phenolic content, rehydration ratio, color differences and dielectric properties of the novel method were found to be higher than that of control. Moreover, Midilli and Wang & Sing models gave the superior fit to the moisture ratio data obtained during drying.

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Optimization of the microwave drying process for potato chips based on the measurement of dielectric properties

Cited by 40 publications

References 40 publications

Real‐time evaluation of artificial neural network‐developed model of banana slice kinetics in microwave‐hot air dryer

Real‐time evaluation of artificial neural network‐developed model of banana slice kinetics in microwave‐hot air dryer

Optimization of combined microwave and hot air drying technology for purple cabbage by Response Surface Methodology (RSM)

Optimization of ohmic heating-assisted osmotic dehydration as a pretreatment for microwave drying of quince

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