| dc.contributor.author |
Argyri, AA |
en |
| dc.contributor.author |
Panagou, EZ |
en |
| dc.contributor.author |
Tarantilis, PA |
en |
| dc.contributor.author |
Polysiou, M |
en |
| dc.contributor.author |
Nychas, G-JE |
en |
| dc.date.accessioned |
2014-06-06T06:50:40Z |
|
| dc.date.available |
2014-06-06T06:50:40Z |
|
| dc.date.issued |
2010 |
en |
| dc.identifier.issn |
09254005 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1016/j.snb.2009.11.052 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/5111 |
|
| dc.subject |
Aerobic storage |
en |
| dc.subject |
Artificial neural networks |
en |
| dc.subject |
Beef fillets |
en |
| dc.subject |
FTIR |
en |
| dc.subject |
Machine learning |
en |
| dc.subject |
Meat spoilage |
en |
| dc.subject.other |
Aerobic condition |
en |
| dc.subject.other |
Artificial Neural Network |
en |
| dc.subject.other |
Fourier transform infrared |
en |
| dc.subject.other |
FT-IR spectrum |
en |
| dc.subject.other |
FTIR |
en |
| dc.subject.other |
FTIR spectroscopy |
en |
| dc.subject.other |
Good correlations |
en |
| dc.subject.other |
Graphical plots |
en |
| dc.subject.other |
Machine-learning |
en |
| dc.subject.other |
Meat samples |
en |
| dc.subject.other |
Meat spoilage |
en |
| dc.subject.other |
Microbial count |
en |
| dc.subject.other |
Microbial loads |
en |
| dc.subject.other |
Multilayer perceptron neural networks |
en |
| dc.subject.other |
Quantitative detection |
en |
| dc.subject.other |
Rapid assessment |
en |
| dc.subject.other |
Sensory evaluation |
en |
| dc.subject.other |
Spectral data |
en |
| dc.subject.other |
Standard error of prediction |
en |
| dc.subject.other |
Statistical indices |
en |
| dc.subject.other |
Total viable counts |
en |
| dc.subject.other |
Fourier transform infrared spectroscopy |
en |
| dc.subject.other |
Fourier transforms |
en |
| dc.subject.other |
Learning systems |
en |
| dc.subject.other |
Meats |
en |
| dc.subject.other |
Spoilage |
en |
| dc.subject.other |
Neural networks |
en |
| dc.title |
Rapid qualitative and quantitative detection of beef fillets spoilage based on Fourier transform infrared spectroscopy data and artificial neural networks |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.1016/j.snb.2009.11.052 |
en |
| heal.publicationDate |
2010 |
en |
| heal.abstract |
A machine learning strategy in the form of a multilayer perceptron (MLP) neural network was employed to correlate Fourier transform infrared (FTIR) spectral data with beef spoilage during aerobic storage at chill and abuse temperatures. Fresh beef fillets were packaged under aerobic conditions and left to spoil at 0, 5, 10, 15, and 20 °C for up to 350 h. FTIR spectra were collected directly from the surface of meat samples, whereas total viable counts of bacteria were obtained with standard plating methods. Sensory evaluation was performed during storage and samples were attributed into three quality classes namely fresh, semi-fresh, and spoiled. A neural network was designed to classify beef samples to one of the three quality classes based on the biochemical profile provided by the FTIR spectra, and in parallel to predict the microbial load (as total viable counts) on meat surface. The results obtained demonstrated that the developed neural network was able to classify with high accuracy the beef samples in the corresponding quality class using their FTIR spectra. The network was able to classify correctly 22 out of 24 fresh samples (91.7%), 32 out of 34 spoiled samples (94.1%), and 13 out of 16 semi-fresh samples (81.2%). No fresh sample was misclassified as spoiled and vice versa. The performance of the network in the prediction of microbial counts was based on graphical plots and statistical indices (bias and accuracy factors, standard error of prediction, mean relative and mean absolute percentage residuals). Results demonstrated good correlation of microbial load on beef surface with spectral data. The results of this work indicated that the biochemical fingerprints during beef spoilage obtained by FTIR spectroscopy in combination with the appropriate machine learning strategy have significant potential for rapid assessment of meat spoilage. © 2009 Elsevier B.V. All rights reserved. |
en |
| heal.journalName |
Sensors and Actuators, B: Chemical |
en |
| dc.identifier.issue |
1 |
en |
| dc.identifier.volume |
145 |
en |
| dc.identifier.doi |
10.1016/j.snb.2009.11.052 |
en |
| dc.identifier.spage |
146 |
en |
| dc.identifier.epage |
154 |
en |