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30 April 2019 Multivariate predictive models for the prediction of fatty acids in the EU high added-value “acorn Iberian pig ham” using a miniature near-infrared spectroscopy instrument
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Abstract
Acorn Iberian ham (Jamón Ibérico de Bellota) is one of the most expensive luxury foodstuffs produced in Europe, with a highly appreciated smell and flavour. Its recognized high-sensorial quality and health properties are mainly due to the traditional outdoor feeding system (Montanera) of Iberian pigs (IP), which provides high standards of animal welfare. Nowadays, one of the frauds affecting this product is the use of “special compound feeds” to simulate the fat composition of the acorns through the inclusion of sources of oleic acid like the ones found in pigs fed outdoors. The high prices paid for a cured leg of Iberian ham –ranging from hundreds to thousands of euros- leads to many opportunities for mislabelling and fraud. Fatty acid content of the adipose tissue could provide evidence of the feeding system. Gas chromatography (GC) is used at industry level for production control purposes. However, it is costly and time-consuming, and it is only applied to batches of animals rather than individual pigs. The main goal of this study was to use spectra belonging to a portable NIRS instrument (MicroNIR Onsite Lite, Viavi Solutions Inc.) for on–site quantitative (fatty acid content) analysis of individual Iberian pork carcasses at the slaughterhouse. Performance of this portable instrument was compared with an at-line NIRS monochromator. PLS models were built and optimized resulting in standard errors of cross validation ranging from 0.83 to 0.84 for palmitic acid, 0.94 to 0.99 for stearic acid, 1.47 to 1.56 for oleic acid and 0.53 to 0.58 for linoleic acid.
Conference Presentation
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Ana Garrido-Varo, Cecilia Riccioli, Tom Fearn, E. De Pedro, and Dolores C. Pérez-Marín "Multivariate predictive models for the prediction of fatty acids in the EU high added-value “acorn Iberian pig ham” using a miniature near-infrared spectroscopy instrument", Proc. SPIE 11016, Sensing for Agriculture and Food Quality and Safety XI, 110160C (30 April 2019); https://doi.org/10.1117/12.2521977
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