This work aimed to identify a combination of isotopic and molecular biomarkers in bovine muscle and adipose tissue for authentication of the diet of beef cattle. Muscle and adipose tissue samples were collected from animals one of four dietary treatments fed over a 1 year period : pasture (P), barley-based concentrate (C), silage followed by pasture (SiP) or silage followed by pasture with concentrate (SiPC). In total, 83 variables were studied including volatile compounds, colour and reflectance measurements, stable isotope ratios, fatty acids, β-carotene, lutein and αa-tocopherol. Chemometric models were created for each dietary treatment using the entire and an attenuated variable set. Meat from each dietary treatment was identified with a high level of accuracy (correct classification between 90.8% and 100%) using a discriminant approach. After elimination of insignificant variables, accuracy was maintained or marginally improved. SIMCA class-modelling performed moderately well, especially with the reduced variable set (76.1-100% correct classification). © 2013 Elsevier Ltd. All rights reserved.

Beef authentication using dietary markers: Chemometric selection and modelling of significant beef biomarkers using concatenated data from multiple analytical methods

Luciano G.;
2013-01-01

Abstract

This work aimed to identify a combination of isotopic and molecular biomarkers in bovine muscle and adipose tissue for authentication of the diet of beef cattle. Muscle and adipose tissue samples were collected from animals one of four dietary treatments fed over a 1 year period : pasture (P), barley-based concentrate (C), silage followed by pasture (SiP) or silage followed by pasture with concentrate (SiPC). In total, 83 variables were studied including volatile compounds, colour and reflectance measurements, stable isotope ratios, fatty acids, β-carotene, lutein and αa-tocopherol. Chemometric models were created for each dietary treatment using the entire and an attenuated variable set. Meat from each dietary treatment was identified with a high level of accuracy (correct classification between 90.8% and 100%) using a discriminant approach. After elimination of insignificant variables, accuracy was maintained or marginally improved. SIMCA class-modelling performed moderately well, especially with the reduced variable set (76.1-100% correct classification). © 2013 Elsevier Ltd. All rights reserved.
2013
Authentication; Beef; Biomarkers; Dietary treatment; PLS-DA; SIMCA; Adipose Tissue; Animal Feed; Animals; Biomarkers; Cattle; Diet; Discriminant Analysis; Food Contamination; Meat; Models, Theoretical; Muscle, Skeletal
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/388017
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