Prediction of convective heat transfer coefficient during deep-fat frying of pantoa using neurocomputing approaches

Neethu, K C; Sharma, A. K.; Pushpadass, Heartwin A.; Emerald, F. Magdaline Eljeeva; Manjunatha, M.

doi:10.1016/j.ifset.2016.02.012

Cited by 20 publications

(11 citation statements)

References 22 publications

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“…These results had a better fit than a conventional multi-variable linear regression. In a study conducted by Neethu et al, [29] the prediction of the convective heat transfer coefficient during pantoa frying by means of RNA was studied, obtaining a correlation coefficient for the results derived from the network of 0.9984, using a multilayer network with feedback and a learning algorithm of backpropagation, one of the most common neural network architectures. Arjona-Román et al [30] developed an RNA model that allowed the determination of the calorific capacity (Cp) of the defrosting of pork with variables not associated with said measurement.…”

Section: Application Of Rna In Thermal Treatmentsmentioning

confidence: 99%

“…Application of artificial neural network method to exergy and energy analyses of fluidized bed dryer for potato cubes [33]. Prediction of convective heat transfer coefficient during deep-fat frying of pantoa using neurocomputing approaches [29]. Prediction by Artificial Neural Networks (ANN) of the diffusivity, mass, moisture, volume and solids on osmotically dehydrated yacon (Smallantus sonchifolius) [26].…”

Section: Exploitation Of Artificial Neural Networkmentioning

confidence: 99%

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Neural networks in food industry

Correa¹,

Castillo²,

Martelo³

2018

ces

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In the present review we study the application of artificial neural networks in food industry, classifying the information obtained in different categories and the most used programs for its development. The prediction of parameter on operation such as drying, heat treatment, food classification and quality analysis are the main objective for RNA application. Multilayer with feedback propagation is the model most used being Matlab, the software par excellence for its development. Application of artificial neural network in food industry proved to be a useful tool to determine their quality through non-invasive, low-cost and real time processes.

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Section: Application Of Rna In Thermal Treatmentsmentioning

confidence: 99%

Section: Exploitation Of Artificial Neural Networkmentioning

confidence: 99%

Neural networks in food industry

Correa¹,

Castillo²,

Martelo³

2018

ces

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“…Some studies have reported the use of mathematical models to describe, predict, and optimize the kinetic behavior of foods during the frying process. Fick's law has been used to describe the process of moisture diffusion and as a basis for optimizing processing conditions for different fried foods [18,19]. Authors such as [20][21][22][23][24] applied this diffusive model during frying of pea chips, potato strips, yucca slices, peas and banana slices, and sweet potato slices, respectively.…”

Section: Introductionmentioning

confidence: 99%

Mass Transfer during Atmospheric and Vacuum Frying of Chorizo

Castillo

Díaz

et al. 2021

International Journal of Food Science

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The main objective of this study was to evaluate the kinetics of moisture and oil uptake during chorizo deep-fat frying as compared to atmospheric and vacuum conditions. The conditions in the process were 90, 120, and 150°C for vacuum frying and 160, 170, and 180°C for atmospheric frying. The kinetics of moisture loss during atmospheric and vacuum frying was studied from the analytical solution of Fick’s second law for cylinder geometry. Oil absorption was also determined using a first-order kinetic model. The moisture content decreased by 33.72% at the maximum process temperature and time during vacuum frying (150°C, 360 s), as compared to the atmospheric frying, which was 28.61% (180°C). The oil content at the end of the process (360 s) was 27.79% (90°C), 27.31% (120°C), and 24.82% (150°C) for vacuum-fried chorizos, and higher values were obtained in the atmospheric frying, obtaining values of 34.45% (160°C), 31.36% (170°C), and 28.47% (180°C) ( p < 0.05 ). In summary, the vacuum frying yielded sausages with a lower final oil percentage and higher moisture content; these results are promising because they may influence consumer preference for sensory parameters.

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“…Puede definirse como un tipo especial de cocción por inmersión en aceite o grasa comestible a una temperatura superior al punto de ebullición del agua (Bouchon, 2009;Tirado et al, 2013;2015b). Básicamente, la fritura es un proceso de deshidratación como otros (Tirado et al, 2015a;2016a;2016b) que origina en los alimentos reacciones de gelatinización de almidones, desnaturalización de proteínas y cambios en las propiedades físicas y organolépticas (Neethu et al, 2016). Además de los cambios muy apreciados por los consumidores, un efecto adicional es la preservación del alimento como resultado de la inactivación de enzimas por calor y la reducción de la actividad de agua (Dueik y Bouchon, 2011).…”

Section: Introductionunclassified

Efectos de la Fritura al Vacío en los Atributos de Calidad de Arepa con Huevo

2017

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ResumenLa fritura al vacío permite mejorar la calidad de los alimentos debido a las menores temperaturas y baja exposición del aceite al oxígeno. El objetivo de este estudio fue analizar el efecto del freído al vacío sobre la calidad de arepas con huevo. Se realizaron tres formulaciones con incorporación de pasta de ajonjolí. El freído fue a 120 °C, 130 °C y 140 °C, 30 kPa y 5 min. Se utilizó un diseño experimental factorial 3 2 . Los contenidos de humedad y luminosidad disminuyeron con el aumento de la temperatura. El aceite aumentó con la adición de pasta. La deseabilidad máxima fue 0.74 a 133.5 °C y 63.4 % de pasta. Las respuestas fueron: humedad (30.78 %), aceite (26.65 %), ∆E (22.74), luminosidad (52.41), color (4.24), olor (4.33), sabor (4.64) y dureza (4.45). La fritura al vacío es una tecnología alternativa para obtener arepas con huevo de buena calidad y aceptabilidad. Palabras clave: arepa con huevo, atributos sensoriales, optimización, pasta de ajonjolí Effects of Vacuum Frying on the Attributes of Quality of Arepa with Egg AbstractVacuum frying improves food quality due to the lower temperature and low oxygen exposure of the frying oil. The aim of this study was to analyze the effect of vacuum frying on the quality of arepas containing egg. Three formulations were made with addition of sesame paste. Frying was at 120 °C, 130 °C and 140 °C, 30 kPa and 5 min. A factorial experimental design 3 2 was used. Moisture content and brightness decreased with increase in temperature. Oil content increased with the addition of pasta. The maximum desirability was 0.74 at 133.5 °C and 63.4 % paste. The values of the responses were: moisture (30.78 %), oil (26.65 %), ∆E (22,74), light (52,41), color (4,24), smell (4,33), flavor (4.64) and hardness (4,45). Vacuum frying is an alternative technology for obtaining arepas with egg with good quality and acceptability.

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Prediction of convective heat transfer coefficient during deep-fat frying of pantoa using neurocomputing approaches

Cited by 20 publications

References 22 publications

Neural networks in food industry

Neural networks in food industry

Mass Transfer during Atmospheric and Vacuum Frying of Chorizo

Efectos de la Fritura al Vacío en los Atributos de Calidad de Arepa con Huevo

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