Discriminant analysis as a tool to identify bovine and ovine meat produced from pasture or stall-fed animals. Issue 1 (14th December 2020) - Acciaro, Marco; Decandia, Mauro; Sitzia, Maria; Manca, Carla; Giovanetti, Valeria; Cabiddu, Andrea; Addis, Margherita; Rassu, S. Piegiacomo G.; Molle, Giovanni; Dimauro, Corrado
Abstract: This work evaluated the reliability of the multivariate statistical analysis to discriminate the feeding system and the species of ruminants using their intramuscular fatty acids (FA) profile. FA composition of 53 meat samples (longissimus dorsi muscle) from animals of different species (sheep and cattle) raised with different feeding systems (pasture and stall-fed) (4 groups overall) was determined and expressed as % fatty acid methyl ester (FAME). A stepwise discriminant analysis (SDA) was applied to the full set of FA to select the variables that best discriminated between feeding systems and animal species. The selected variables were then submitted to a canonical discriminant analysis (CDA) to test the ability of those variables in discriminating against the four groups. Discriminant analysis (DA) was then exploited to classify meat samples. From the 62 initial variables detected in the FA profile, 24 were retained in the SDA. The subsequent CDA developed by using the selected variables, significantly discriminated the four groups (Hotelling's test p < 0.0001) by extracting three canonical functions. Heptadecenoic acid C17:1 c10, seemed to play a pivotal role both in discriminating species and feeding system while some 18:1 isomers (C18:1 c12, C18:1 c13 C18:1 t13/t14) together with CLA c9, t11 and ω-3 were important in discriminating feeding systems. Multivariate statistical analysis of FA was able to track both the species and the feeding system of source animals with good accuracy. Highlights: The increasing interest in the 'green image' of meat obtained from grass-based systems guides the search for methods to trace the animal feeding system. Extracting more information from the large amounts of meat data provided by laboratory equipment is of utmost importance. Multivariate statistical analysis is able to trace with good accuracy meat samples back to their animal species and feeding system origin.