Effects of power input and food aspect ratio on microwave thawing process of frozen food in commercial oven

Klinbun, Waraporn; Rattanadecho, Phadungsak

doi:10.1080/08327823.2019.1677430

Cited by 14 publications

(10 citation statements)

References 32 publications

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“…La ley de Fourier de la conducción del calor permite determinar la difusividad térmica del alimento entre otras propiedades y pueden ayudar a los procesadores de alimentos con calor para obtener mejores condiciones de proceso (Dash et al, 2022). La ley de Fourier fue utilizada para el modelamiento del calentamiento por microondas de arroz congelado (Klinbun & Rattanadecho, 2019) y secado solar de papas en forma de cilindro (Dhalsamant et al, 2018). Los modelos matemáticos empíricos como el modelo de Peleg, Page, Weibull y Midilli, fueron utilizados de forma simple en diversas investigaciones como en el secado de mango (Garcia Paternina et al, 2015) y tostado de cacao (Ariana et al, 2019).…”

Section: Uso De Sensores Para Modelamiento Matemático Durante El Tost...unclassified

Use of sensors for mathematical modeling during the roasting of cocoa beans (Theobroma cacao) of the Chuncho variety

Ramos¹

2022

Rev. Investig. Altoandin.

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Simple innovations in temperature control during cocoa roasting can help improve the quality of chocolate made by small entrepreneurs in the Andean-Amazonian valleys of Peru. The objective of this research was to evaluate the effect of oven temperature on the temperature of cocoa beans (Theobroma cacao) during roasting, using six mathematical models. Cocoa beans in the amounts of 100 and 200 g were placed in a tray in a conventional electric oven set at 250 °C. A K-type thermocouple with its respective reader was introduced in the geometric center of a cocoa bean and another 5 cm from the surface. The readings were recorded every 5 min and exported via Bluetooth to a computer. The temperature data were fitted to the mathematical models of the Concentrated System, Fourier’s Law, Peleg, Page, Weibull and Midilli. The convective heat transfer coefficient (h), the thermal diffusivity (α), and the constants of the empirical models were determined. The h was 7.04 and 7.74 W/m2 °C for the toast of 100 and 200 g, respectively. The α was 3.09 x 10-8 and 3.28 x 10-8 m2/s for the 100 and 200 g toast, respectively. The rate constants of the empirical models showed a difference in the roasting of 100 and 200 g. All models represented the experimental data very well, since the values of R2, MRSE and MA%E were close to 1, close to 0 and less than 10%, respectively. The best mathematical model was that of Peleg.

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Section: Uso De Sensores Para Modelamiento Matemático Durante El Tost...unclassified

Use of sensors for mathematical modeling during the roasting of cocoa beans (Theobroma cacao) of the Chuncho variety

Ramos¹

2022

Rev. Investig. Altoandin.

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“…To minimize dripping during thawing and deterioration in quality, new technologies have been developed in recent years. These include microwave thawing [ 102 , 114 , 115 , 116 ], ohmic thawing [ 113 ], high-pressure thawing [ 61 , 101 ], ultrasonic thawing [ 107 , 117 ], radiofrequency thawing [ 33 , 108 , 118 ], high-voltage electric field thawing [ 104 , 111 ], and far-infrared thawing [ 112 ]. Among these methods, microwave thawing presents advantages such as a short thawing time, easy control, fast heat efficiency, and energy saving.…”

Section: Effect Of Thawing On Food Qualitymentioning

confidence: 99%

“…These advantages have resulted in the extensive use of microwave thawing, both in industry and at home [ 112 , 119 , 120 , 121 ]. Nonetheless, its major problem is non-uniform heating via the uneven distribution of the microwave field inside the food product [ 114 , 122 ].…”

Section: Effect Of Thawing On Food Qualitymentioning

confidence: 99%

“…Assumptions made during simulations are often considered to reduce complexity and the computational time of the model, which could adversely affect the accuracy of the model. Some of the assumptions made during the simulation of freezing and thawing of food products include uniform initial temperature distribution throughout the product, material homogeneity [ 114 ], uniform distribution of convective heat transfer coefficient around the product [ 10 , 134 ] and ignoring mass and momentum transfer in the product [ 114 , 133 ]. Reasonably limiting assumptions could improve the accuracy of the simulation, although the running calculation could be longer.…”

Section: Current Limitations and Future Trendsmentioning

confidence: 99%

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Finite Element Method for Freezing and Thawing Industrial Food Processes

Fadiji

Ashtiani

Onwude

et al. 2021

Foods

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Freezing is a well-established preservation method used to maintain the freshness of perishable food products during storage, transportation and retail distribution; however, food freezing is a complex process involving simultaneous heat and mass transfer and a progression of physical and chemical changes. This could affect the quality of the frozen product and increase the percentage of drip loss (loss in flavor and sensory properties) during thawing. Numerical modeling can be used to monitor and control quality changes during the freezing and thawing processes. This technique provides accurate predictions and visual information that could greatly improve quality control and be used to develop advanced cold storage and transport technologies. Finite element modeling (FEM) has become a widely applied numerical tool in industrial food applications, particularly in freezing and thawing processes. We review the recent studies on applying FEM in the food industry, emphasizing the freezing and thawing processes. Challenges and problems in these two main parts of the food industry are also discussed. To control ice crystallization and avoid cellular structure damage during freezing, including physicochemical and microbiological changes occurring during thawing, both traditional and novel technologies applied to freezing and thawing need to be optimized. Mere experimental designs cannot elucidate the optimum freezing, frozen storage, and thawing conditions. Moreover, these experimental procedures can be expensive and time-consuming. This review demonstrates that the FEM technique helps solve mass and heat transfer equations for any geometry and boundary conditions. This study offers promising insight into the use of FEM for the accurate prediction of key information pertaining to food processes.

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“…Thawing in a microwave oven is different, as heat generation occurs within the internal part of the food. The heat needed for transforming the water from solid to liquid phase is large (Klinbun and Rattanadecho, 2019), and this is the reason that foods with a high‐water content thaw more slowly than foods with a low water content (Kumar et al., 2020). The typical temperature profiles during thawing display different phases, with different durations and rates depending on the location within the food (e.g.…”

Section: Assessmentmentioning

confidence: 99%

Guidance on date marking and related food information: part 2 (food information)

Koutsoumanis¹,

Allende²,

Álvarez‐Ordóñez³

et al. 2021

EFS2

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A risk‐based approach was used to develop guidance to be followed by food business operators ( FBO s) when deciding on food information relating to storage conditions and/or time limits for consumption after opening a food package and thawing of frozen foods. After opening the package, contamination may occur, introducing new pathogens into the food and the intrinsic (e.g. pH and a w ), extrinsic (e.g. temperature and gas atmosphere) and implicit (e.g. interactions with competing background microbiota) factors may change, affecting microbiological food safety. Setting a time limit for consumption after opening the package (secondary shelf‐life) is complex in view of the many influencing factors and information gaps. A decision tree ( DT ) was developed to assist FBO s in deciding whether the time limit for consumption after opening, due to safety reasons, is potentially shorter than the initial ‘best before’ or ‘use by’ date of the product in its unopened package. For products where opening the package leads to a change of the type of pathogenic microorganisms present in the food and/or factors increasing their growth compared to the unopened product, a shorter time limit for consumption after opening would be appropriate. Freezing prevents the growth of pathogens, however, most pathogenic microorganisms may survive frozen storage, recover during thawing and then grow and/or produce toxins in the food, if conditions are favourable. Moreover, additional contamination may occur from hands, contact surfaces or contamination from other foods and utensils. Good practices for thawing should, from a food safety point of view, minimise growth of and contamination by pathogens between the food being thawed and other foods and/or contact surfaces, especially when removing the food from the package during thawing. Best practices for thawing foods are presented to support FBO s.

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Effects of power input and food aspect ratio on microwave thawing process of frozen food in commercial oven

Cited by 14 publications

References 32 publications

Use of sensors for mathematical modeling during the roasting of cocoa beans (Theobroma cacao) of the Chuncho variety

Use of sensors for mathematical modeling during the roasting of cocoa beans (Theobroma cacao) of the Chuncho variety

Finite Element Method for Freezing and Thawing Industrial Food Processes

Guidance on date marking and related food information: part 2 (food information)

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