Isothermal batch crystallization of alpha-lactose: A kinetic model combining mutarotation, nucleation and growth steps

Mimouni, Arnaud; Schuck, Pierre; Bouhallab, Saı̈d

doi:10.1016/j.idairyj.2008.09.006

Cited by 34 publications

(36 citation statements)

References 20 publications

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“…These include kinetic-based models and models based on response surface methodology (RSM). [13][14][15][16] Based on the function fitting technique, a response surface in highdimensional space was fitted to describe the relation between experiment inputs and output (with minimum process knowledge) so that it could be used as an interesting alternative for conventional models such as numerical simulation during optimization with a reduced computational cost. However, RSM-based models are only accurate for predicting the relationship between a limited number of input and output parameters.…”

Section: Comparison Of Artificial Neural Network (Ann) and Response Smentioning

confidence: 99%

Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in the Prediction of Quality Parameters of Spray-Dried Pomegranate Juice

2009

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Response surface methodology (RSM) is a frequently used method for empirical modeling and prediction in the processing of biological media. The artificial neural network (ANN) has recently grown to be one of the most efficient methods for empirical modeling and prediction, especially for nonlinear systems. This article presents comparative studies between an ANN and RSM in the modeling and prediction of quality parameters of spray-dried pomegranate juice. In this study, the effects of the carrier type, carrier concentration, and concentration of crystalline cellulose in a pomegranate juice spray-drying process were investigated on five quality parameters-drying yield, solubility, color change, total anthocyanin content, and antioxidant activity-using RSM and ANN methods. A central composite rotatable experimental design (CCRD) and a feed-forward multilayered perceptron (MLP) ANN trained using back-propagation algorithms for three independent variables were developed to predict the five outputs. The final selected ANN model (3-10-8-5) was compared to the RSM model for its modeling and predictive abilities. The predictive abilities of both the ANN and RSM were compared using a separate dataset of 18 unseen experiments based on RMSE (root mean square error), MAE (mean absolute error), and R 2 (correlation coefficient) for each output parameter. The results indicate the superiority of a properly trained ANN in capturing the nonlinear behavior of the system and the simultaneous prediction of five outputs.

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Section: Comparison Of Artificial Neural Network (Ann) and Response Smentioning

confidence: 99%

Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in the Prediction of Quality Parameters of Spray-Dried Pomegranate Juice

2009

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“…Mimouni et al . () developed a simple improved model to estimate the kinetics of isothermal batch lactose crystallisation. This model simultaneously accounts for the three main steps of the lactose crystallisation process: mutarotation, nucleation and crystal growth.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the presence of whey protein isolate (WPI) or gum arabic has an inhibitory effect on the solid-phase crystallisation kinetics of lactose (Das et al 2013). Mimouni et al (2009) developed a simple improved model to estimate the kinetics of isothermal batch lactose crystallisation. This model simultaneously accounts for the three main steps of the lactose crystallisation process: mutarotation, nucleation and crystal growth.…”

Section: Introductionmentioning

confidence: 99%

Lactose crystallisation in concentrated whey: the influence of vat type

Simeão

Silva

Stephani

et al. 2017

Int J of Dairy Tech

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This work aimed to describe the rate of lactose crystallisation in concentrated whey carried out using two different crystallisers, utilising central or lateral stirring. The lactose crystallisation rate differed between treatments, showing high values with use of the central stirrer. Under the experimental conditions, after 4 h of crystallisation, in neither of the vats was 70% lactose crystallisation achieved. With the standardised conditions applied in this study, with soluble solids, time, and stirring and rate cooling being constant, it was possible to verify the influence of the vat type on the rate of lactose crystallisation.

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“…In the food processing industry there has been growing interest in the crystallization of lactose in (Mcleod, 2007;Mimouni, 2007;Mimouni A. et al, 2009). For a number of reasons, α-lactose monohydrate is the most commonly used form of lactose in making medications.…”

Section: Introductionmentioning

confidence: 99%