An On-Line Near-Infrared (NIR) Transmission Method for Determining Depth Profiles of Fatty Acid Composition and Iodine Value in Porcine Adipose Fat Tissue

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Abstract:

The present work describes a measurement method using spatially resolved near-infrared (NIR) spectroscopy to determine porcine carcass fat quality as a function of the distance to the skin by estimating its iodine value (IV). The new method is capable of performing on-line carcass grading at full production speed (approximately 1000 carcasses per hour). The method is demonstrated in an experiment where 35 carcasses were sampled at an abattoir, selected from three feeding groups. The NIR transmission instrument was applied on the loin of each carcass, and a parallel reference sample was removed and processed into 1.8 mm thick disks, representing a depth-of-fat profile from the loin. The disks were analyzed for fatty acid composition using gas chromatography (GC) and for IV. A principal component analysis (PCA) of the obtained GC reference values clearly showed that the feeding regimes can be differentiated. Using interval partial least squares (iPLS) regression, a model was produced that can predict the IV of the fat at a given measured depth with a root mean square error of cross-validation (RMSECV) of 1.44. The results show how the IV varies as a function of feeding regime and as a function of fat depth. The maximum variation found within a single depth profile was 10.1 IV from the skin to the innermost part of the fat layers. In the sample material investigated the average span in IV between the average values of the two porcine backfat layers was 6.4 IV (the maximum difference was 8.6 IV). The new method can provide the abattoir with new chemical information about fat quality and production quality that will open new possibilities of meat/carcass grading and product development.

Keywords: Chemometrics; Fat layers; Fatty acid composition; Interval partial least squares; Iodine Value (IV); Near-infrared transmission spectroscopy; PCA; Porcine fat; Principal component analysis; iPLS

Document Type: Research Article

DOI: http://dx.doi.org/10.1366/11-06396

Affiliations: 1: Quality and Technology, Department of Food Science, Faculty of Life Sciences, University of Copenhagen, Rolighedsvej 30, DK-1958 Frederiksberg C, Denmark; Carometec A/S, Transformervej 9, DK-2730 Herlev, Denmark 2: Carometec A/S, Transformervej 9, DK-2730 Herlev, Denmark 3: Quality and Technology, Department of Food Science, Faculty of Life Sciences, University of Copenhagen, Rolighedsvej 30, DK-1958 Frederiksberg C, Denmark

Publication date: February 1, 2012

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