International Journal of Computational Intelligence Systems

Volume 6, Issue 6, December 2013, Pages 1052 - 1058

A New Model for Prediction of the Performance of a Cappuccino Pod

Authors
Linhua Jiang, Hua Pan, Haibin Cai
Corresponding Author
Linhua Jiang
Available Online 9 January 2017.
DOI
https://doi.org/10.1080/18756891.2013.816028How to use a DOI?
Keywords
Cappuccino pod, creamer, shrinking core model, dynamic mass balance, dissolution process
Abstract
In recent years a lot of attention and efforts have been dedicated on optimizing the design and quality of coffee pods. These would be applicable on professional as well as on household machines such as Senseo or Nestle. By providing a new method to describe the mechanisms of the brewing process within the pod, the research presented in this paper gives deeper insights into the design related aspects to improve the pods. A model incorporating the function of dynamic mass balance and the shrinking core model is employed to describe the dissolution process of the creamer in the pod. This dissolution step is the most important in determining the milky taste wanted in the case of cappuccino. The model was tested by comparing its results with those of a series of experiments. With the model presented in this paper designing better pods should be easier and more efficient.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
6 - 6
Pages
1052 - 1058
Publication Date
2017/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.1080/18756891.2013.816028How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Linhua Jiang
AU  - Hua Pan
AU  - Haibin Cai
PY  - 2017
DA  - 2017/01
TI  - A New Model for Prediction of the Performance of a Cappuccino Pod
JO  - International Journal of Computational Intelligence Systems
SP  - 1052
EP  - 1058
VL  - 6
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.816028
DO  - https://doi.org/10.1080/18756891.2013.816028
ID  - Jiang2017
ER  -