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 α-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).
Beef authentication using dietary markers: chemometric selection and modelling of significant beef biomarkers using concatenated data from multiple analytical methods
Luciano, Giuseppe;
2013
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 α-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).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
