Neural networks for predicting thermal conductivity of bakery products

Sablani, Shyam S.; Baik, Oon‐Doo; Marcotte, M.

doi:10.1016/s0260-8774(01)00119-4

Cited by 100 publications

(53 citation statements)

References 16 publications

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“…In addition, the MRE (Equation 9) was the criterion used to evaluate the accuracy of the estimations. A model with a value for MRE below 15 % is considered to have good accuracy Baik;Marcotte, 2002).…”

Section: Experimental Drying Kineticsmentioning

confidence: 99%

Mathematical Modeling of Orange Seed Drying Kinetics

et al. 2015

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Drying of orange seeds representing waste products from juice processing was studied in the temperatures of 40, 50, 60 and 70 °C and drying velocities of 0.6, 1.0 and 1.4 m/s. Experimental drying kinetics of orange seeds were obtained using a convective air forced dryer. Three thin-layer models: Page model, Lewis model, and the Henderson-Pabis model and the diffusive model were used to predict the drying curves. The Henderson-Pabis and the diffusive models show the best fitting performance and statistical evaluations. Moreover, the temperature dependence on the effective diffusivity followed an Arrhenius relationship, and the activation energies ranging from 16.174 to 16.842 kJ/mol. Index terms: Dehydration; diffusive model; Henderson-Pabis model; moisture content. RESUMOA secagem de sementes de laranja que representam produtos residuais do processamento de suco foi estudada nas temperaturas de 40, 50, 60 e 70 °C e velocidades de secagem de 0,6, 1,0 e 1,4 m/s. As cinéticas de secagem experimental de sementes de laranja foram obtidas utilizando um secador de ar forçado por convecção. Três modelos de camada fina: modelo de Page, modelo Lewis, e o modelo de Henderson-Pabis e o modelo difusivo foram utilizados para prever as curvas de secagem. Os modelos de HendersonPabis e difusivo mostraram o melhor desempenho adequado e avaliações estatísticas. Além disso, a dependência da temperatura na difusividade efetiva seguida a relação de Arrhenius, e as energias de ativação variam entre 16,174-16,842 kJ / mol. Termos de indexação:Desidratação; modelo difuso; modelo Henderson-Pabis; teor de umidade.

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Section: Experimental Drying Kineticsmentioning

confidence: 99%

Mathematical Modeling of Orange Seed Drying Kinetics

et al. 2015

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“…The moisture content inside the dough, needed as an input for thermal conductivity computation, was taken to be 38.5%. This represented an average value of the moisture content of the sample during the baking process with the wet dough (moisture content, 43.5-46.1%) gradually transforming to a baked bread (moisture content, 28-36%) and is within the range reported by Sablani et al (2002). The thermal conductivity calculated from this moisture content was used as an average k value of the sample during the baking process.…”

Section: Resultsmentioning

confidence: 99%

“…To relate the heat transfer with the properties of the dough at any given time, a functional dependence of thermal conductivity k on density, temperature and moisture content has been taken. For this, a neural network model developed by Sablani et al (2002) is used. The thermal diffusivity can be found from the following definition:…”

Section: Computation Schemementioning

confidence: 99%

Evolution of bubble size distribution in baked foods

Deshlahra

Mehra

Ghosal

2009

Journal of Food Engineering

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“…The development of a neural network model involves: the generation of data required for training/testing, the training/testing of the BP artificial neural network model, the evaluation of the BP artificial neural network configuration leading to the selection of an optimal configuration, and validation of the optimal BP artificial neural network model with a data set not used in training before [9].…”

Section: Methodsmentioning

confidence: 99%

Study on Thermal Conductivities Prediction for Apple Fruit Juice by Using Neural Network

Zhang

Che

et al. 2011

Computer and Computing Technologies in Agriculture IV

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Abstract. Based on experimentally measured values by thermal probe method, the prediction model of thermal conductivities of apple fruit juice as a function of concentration and temperature was studied by neural network method. The optimal neural network was made of two hidden layers and every hidden layer had six neurons. The prediction result shows that the optimal model could predict thermal conductivity with a mean relative error of 0.11%, a mean absolute error of 0.00054W/mK, a mean standard error of 0.00039 W/mK, the linear relationship of 0.9993. The calculated precision was higher for BP neural network model than that for dual regression model. The presented results were proved that this model can be used with satisfactory accuracy for the prediction of thermal conductivity of apple fruit juice.

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Neural networks for predicting thermal conductivity of bakery products

Cited by 100 publications

References 16 publications

Mathematical Modeling of Orange Seed Drying Kinetics

Mathematical Modeling of Orange Seed Drying Kinetics

Evolution of bubble size distribution in baked foods

Study on Thermal Conductivities Prediction for Apple Fruit Juice by Using Neural Network

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