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Application of Kinetic Models and Neural Networks to Predict the Embedding Rate During Storage of Fingered Citron Essential Oil Microcapsules

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Proceedings of the 2012 International Conference on Applied Biotechnology (ICAB 2012)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 249))

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Abstract

The aim of this work was to develop kinetic models of the embedding rate during storage of fingered citron essential oil microcapsules and evaluate the performance of artificial neural network to predict the kinetic parameters. Release kinetics of microencapsulated (by β-cyclodextrin) fingered citron essential oil was investigated under a series of temperature levels (10, 25, 40, 55, 70, 85, and 100 °C). In addition, the model was trained using a back-propagation algorithm and one hidden layer artificial neural network (ANN) was employed. The results indicated that the optimal ANN model was developed when the optimal number of neurons in the hidden layer was 19. Additionally, the correlation coefficients between predicted k, t 1/2, or D-value and experimental values were greater than 0.9971 in all case. Thus, the overall results showed that ANN could be a potential tool for quick and accurate prediction of the kinetic parameters.

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Acknowledgments

The project is supported by the National Natural Science Foundation (21006098).

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Authors and Affiliations

  1. College of Chemistry and Life Science, Zhejiang Normal University, Jinhua, 321004, People’s Republic of China

    Hanglin Xu, Jiang Wu, Keren Du, Hongjing Zhou & Gang Deng

Authors
  1. Hanglin Xu
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  2. Jiang Wu
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  3. Keren Du
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  4. Hongjing Zhou
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  5. Gang Deng
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Corresponding author

Correspondence to Gang Deng .

Editor information

Editors and Affiliations

  1. College of Bioengineering, Tianjin University of Science and Technology, Tianjin, People's Republic of China

    Tong-Cun Zhang

  2. Nanjing University of Technology, Nanjing, People's Republic of China

    Pingkai Ouyang

  3. Department of Microbiology & Molecular Genetics, University of Texas Houston Medical School, Texas, Texas, USA

    Samuel Kaplan

  4. Wellcome Trust Sanger Institute, Cambridge, United Kingdom

    Bill Skarnes

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Xu, H., Wu, J., Du, K., Zhou, H., Deng, G. (2014). Application of Kinetic Models and Neural Networks to Predict the Embedding Rate During Storage of Fingered Citron Essential Oil Microcapsules. In: Zhang, TC., Ouyang, P., Kaplan, S., Skarnes, B. (eds) Proceedings of the 2012 International Conference on Applied Biotechnology (ICAB 2012). Lecture Notes in Electrical Engineering, vol 249. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37916-1_1

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  • DOI: https://doi.org/10.1007/978-3-642-37916-1_1

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37915-4

  • Online ISBN: 978-3-642-37916-1

  • eBook Packages: EngineeringEngineering (R0)

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