Journal
MEAT SCIENCE
Volume 135, Issue -, Pages 142-147Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.meatsci.2017.09.016
Keywords
Minced beef; Frozen-thawed; Multispectral imaging; FTIR spectroscopy; Fraud detection; Data analysis
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Funding
- project Intelligent multi sensor system for meat analysis - iMeatSense_550 - European Union (European Social Fund-ESF)
- Greek national funds through the Operational Program Education and Lifelong Learning of the National Strategic Reference Framework (NSRF) Research Funding Program: ARISTEIA-I
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In recent years, fraud detection has become a major priority for food authorities, as fraudulent practices can have various economic and safety consequences. This work explores ways of identifying frozen-then-thawed minced beef labeled as fresh in a rapid, large-scale and cost-effective way. For this reason, freshly-ground beef was purchased from seven separate shops at different times, divided in fifteen portions and placed in Petri dishes. Multi-spectral images and FTIR spectra of the first five were immediately acquired while the remaining were frozen (-20 degrees C) and stored for 7 and 32 days (5 samples for each time interval). Samples were thawed and subsequently subjected to similar data acquisition. In total, 105 multispectral images and FTIR spectra were collected which were further analyzed using partial least-squares discriminant analysis and support vector machines. Two meat batches (30 samples) were reserved for independent validation and the remaining five batches were divided in training and test set (75 samples). Results showed 100% overall correct classification for test and external validation MSI data, while FTIR data yielded 93.3 and 96.7% overall correct classification for FTIR test set and external validation set respectively.
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