| dc.contributor.author |
Dissing, BS |
en |
| dc.contributor.author |
Papadopoulou, OS |
en |
| dc.contributor.author |
Tassou, C |
en |
| dc.contributor.author |
Ersboll, BK |
en |
| dc.contributor.author |
Carstensen, JM |
en |
| dc.contributor.author |
Panagou, EZ |
en |
| dc.contributor.author |
Nychas, G-J |
en |
| dc.date.accessioned |
2014-06-06T06:52:55Z |
|
| dc.date.available |
2014-06-06T06:52:55Z |
|
| dc.date.issued |
2013 |
en |
| dc.identifier.issn |
19355130 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1007/s11947-012-0886-6 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/6250 |
|
| dc.subject |
Chemometrics |
en |
| dc.subject |
Computational biology |
en |
| dc.subject |
Converging technologies |
en |
| dc.subject |
Meat quality |
en |
| dc.subject |
Meat spoilage |
en |
| dc.subject |
Multispectral imaging |
en |
| dc.subject |
Non-invasive methods |
en |
| dc.subject |
Predictive modelling |
en |
| dc.subject.other |
Chemometrics |
en |
| dc.subject.other |
Computational biology |
en |
| dc.subject.other |
Converging technologies |
en |
| dc.subject.other |
Meat quality |
en |
| dc.subject.other |
Meat spoilage |
en |
| dc.subject.other |
Multispectral imaging |
en |
| dc.subject.other |
Noninvasive methods |
en |
| dc.subject.other |
Predictive modelling |
en |
| dc.subject.other |
Bioinformatics |
en |
| dc.subject.other |
Imaging techniques |
en |
| dc.subject.other |
Meats |
en |
| dc.subject.other |
Noninvasive medical procedures |
en |
| dc.subject.other |
Spoilage |
en |
| dc.subject.other |
Bacteria (microorganisms) |
en |
| dc.title |
Using Multispectral Imaging for Spoilage Detection of Pork Meat |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.1007/s11947-012-0886-6 |
en |
| heal.publicationDate |
2013 |
en |
| heal.abstract |
The quality of stored minced pork meat was monitored using a rapid multispectral imaging device to quantify the degree of spoilage. Bacterial counts of a total of 155 meat samples stored for up to 580 h have been measured using conventional laboratory methods. Meat samples were maintained under two different storage conditions: aerobic and modified atmosphere packages as well as under different temperatures. Besides bacterial counts, a sensory panel has judged the spoilage degree of all meat samples into one of three classes. Results showed that the multispectral imaging device was able to classify 76.13 % of the meat samples correctly according to the defined sensory scale. Furthermore, the multispectral camera device was able to predict total viable counts with a standard error of prediction of 7.47 %. It is concluded that there is a good possibility that a setup like the one investigated will be successful for the detection of spoilage degree in minced pork meat. © 2012 Springer Science+Business Media, LLC. |
en |
| heal.journalName |
Food and Bioprocess Technology |
en |
| dc.identifier.issue |
9 |
en |
| dc.identifier.volume |
6 |
en |
| dc.identifier.doi |
10.1007/s11947-012-0886-6 |
en |
| dc.identifier.spage |
2268 |
en |
| dc.identifier.epage |
2279 |
en |