dc.creator | Arvanitoyannis, I. S. | en |
dc.creator | van Houwelingen-Koukaliaroglou, M. | en |
dc.date.accessioned | 2015-11-23T10:23:07Z | |
dc.date.available | 2015-11-23T10:23:07Z | |
dc.date.issued | 2003 | |
dc.identifier | 10.1080/10408690390826482 | |
dc.identifier.issn | 1040-8398 | |
dc.identifier.uri | http://hdl.handle.net/11615/25937 | |
dc.description.abstract | Multivariate analysis has been established as a very powerful and effective tool in classifying and grouping individual products. Principal Component Analysis, Canonical analysis, Cluster and Partial Least Squares were found to be indispensable for classifying food products according to variety and/or geographical origin. Meat and meat products were correctly classified for authentication purposes to various groups following instrumental and/or sensory analyses. | en |
dc.source | Critical Reviews in Food Science and Nutrition | en |
dc.source.uri | <Go to ISI>://WOS:000182265800002 | |
dc.subject | chemometrics | en |
dc.subject | multivariate analysis | en |
dc.subject | principal component analysis | en |
dc.subject | canonical analysis | en |
dc.subject | cluster analysis | en |
dc.subject | meat | en |
dc.subject | authentication | en |
dc.subject | quality | en |
dc.subject | control | en |
dc.subject | INFRARED REFLECTANCE SPECTROSCOPY | en |
dc.subject | BEEF CARCASS COMPOSITION | en |
dc.subject | SUBCUTANEOUS FAT THICKNESS | en |
dc.subject | MULTIVARIATE DATA-ANALYSIS | en |
dc.subject | NEURAL-NETWORK | en |
dc.subject | PREDICTION | en |
dc.subject | M-LONGISSIMUS-DORSI | en |
dc.subject | DRY-CURED SAUSAGE | en |
dc.subject | HEAT-TREATED BEEF | en |
dc.subject | THEN-THAWED BEEF | en |
dc.subject | FERMENTED SAUSAGES | en |
dc.subject | Food Science & Technology | en |
dc.subject | Nutrition & Dietetics | en |
dc.title | Implementation of chemometrics for quality control and authentication of meat and meat products | en |
dc.type | journalArticle | en |