| dc.contributor.author |
Amina, M |
en |
| dc.contributor.author |
Panagou, EZ |
en |
| dc.contributor.author |
Kodogiannis, VS |
en |
| dc.contributor.author |
Nychas, G-JE |
en |
| dc.date.accessioned |
2014-06-06T06:50:46Z |
|
| dc.date.available |
2014-06-06T06:50:46Z |
|
| dc.date.issued |
2010 |
en |
| dc.identifier.issn |
01697439 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1016/j.chemolab.2010.07.004 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/5154 |
|
| dc.subject |
High pressure treatment |
en |
| dc.subject |
Listeria monocytogenes |
en |
| dc.subject |
Milk |
en |
| dc.subject |
Neural networks |
en |
| dc.subject |
Partial least squares regression |
en |
| dc.subject |
Predictive modelling |
en |
| dc.subject |
Wavelet networks |
en |
| dc.subject.other |
article |
en |
| dc.subject.other |
artificial neural network |
en |
| dc.subject.other |
bacterial count |
en |
| dc.subject.other |
bacterial kinetics |
en |
| dc.subject.other |
bacterial survival |
en |
| dc.subject.other |
bactericidal activity |
en |
| dc.subject.other |
bacterium contamination |
en |
| dc.subject.other |
controlled study |
en |
| dc.subject.other |
environmental temperature |
en |
| dc.subject.other |
food analysis |
en |
| dc.subject.other |
food contamination |
en |
| dc.subject.other |
food microbiotechnology |
en |
| dc.subject.other |
high temperature |
en |
| dc.subject.other |
hydrostatic pressure |
en |
| dc.subject.other |
Listeria monocytogenes |
en |
| dc.subject.other |
milk |
en |
| dc.subject.other |
nonhuman |
en |
| dc.subject.other |
nonlinear system |
en |
| dc.subject.other |
partial least squares regression |
en |
| dc.subject.other |
prediction |
en |
| dc.subject.other |
pressure measurement |
en |
| dc.subject.other |
priority journal |
en |
| dc.subject.other |
process development |
en |
| dc.subject.other |
process model |
en |
| dc.subject.other |
process optimization |
en |
| dc.subject.other |
statistical model |
en |
| dc.subject.other |
survival rate |
en |
| dc.subject.other |
validation process |
en |
| dc.title |
Wavelet neural networks for modelling high pressure inactivation kinetics of Listeria monocytogenes in UHT whole milk |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.1016/j.chemolab.2010.07.004 |
en |
| heal.publicationDate |
2010 |
en |
| heal.abstract |
The aim of the present work was to investigate the applicability of a Wavelet Neural Network to describe the inactivation pattern of Listeria monocytogenes by high hydrostatic pressure in ultra high temperature (UHT) whole milk, and evaluate its performance against models used in predictive microbiology such as the re-parameterized Gompertz and modified Weibull equations. A comparative study with linear partial least squares regression (PLS-R) as well as neural network (NN) models demonstrated on the same dataset has been also considered. Milk was artificially inoculated with an initial population of the pathogen of ca. 107CFU/ml and exposed to a range of high pressures (350, 450, 550, 600MPa) for up to 40min at ambient temperature (ca. 25°C). Typical survival curves were obtained including a shoulder, a log-linear and a tailing phase. Increasing the magnitude of the applied pressure resulted in increasing levels of inactivation. Modelling approaches provided good fit to experimental training data as inferred by the low values of the root mean squared error (RMSE) and the high values of regression coefficient (R2). Models were validated at 400 and 500MPa with independent experimental data. First or second order polynomial models were employed to relate the inactivation parameters to pressure, whereas the wavelet network as well as the PLS and NN models were utilised as a one-step modelling approach. The prediction performance of the proposed learning-based network was better at both validation pressures. The development of accurate models to describe the survival curves of micro-organisms in high pressure treatment would be very important to the food industry for process optimisation, food safety and would eventually expand the applicability of this non-thermal process. © 2010 Elsevier B.V. |
en |
| heal.journalName |
Chemometrics and Intelligent Laboratory Systems |
en |
| dc.identifier.issue |
2 |
en |
| dc.identifier.volume |
103 |
en |
| dc.identifier.doi |
10.1016/j.chemolab.2010.07.004 |
en |
| dc.identifier.spage |
170 |
en |
| dc.identifier.epage |
183 |
en |