• Effect of finishing diet and duration on the sensory quality and volatile profile of lamb meat

      Gkarane, Vasiliki; Brunton, Nigel; Allen, Paul; Gravador, Rufielyn S.; Claffey, Noel A.; Diskin, Michael G.; Fahey, Alan G.; Farmer, Linda J.; Moloney, Aidan P; Alcalde, Maria J.; et al. (Elsevier, 2018-08-02)
      Animal production factors can affect the sensory quality of lamb meat. The study investigated the effect of diet composition and duration of consumption on the proximate analysis, volatile profile and sensory quality of lamb meat. Ninety-nine male Texel × Scottish Blackface lambs were raised at pasture for 10 months before being assigned in groups of 11 to one of the following treatments: 100% Silage (S) for 36 (S36), 54 (S54) or 72 (S72) days; 50% Silage - 50% Concentrate (SC) for 36 (SC36), 54 (SC54) or 72 (SC72) days; 100% Concentrate (C) for 36 (C36) or 54 (C54) or 72 (C72) days. A trained sensory panel found Intensity of Lamb Aroma, Dry Aftertaste and Astringent Aftertaste to be higher in meat from lambs on the concentrate diet. Discriminant analysis showed that the volatile profile enabled discrimination of lamb based on dietary treatment but the volatile differences were insufficient to impact highly on sensory quality. Muscle from animals in the S54 group had higher Manure/Faecal Aroma and Woolly Aroma than the SC54 and C54 groups, possibly related to higher levels of indole and skatole. Further research is required to establish if these small differences would influence consumer acceptability.
    • Semi-supervised linear discriminant analysis

      Toher, Deirdre; Downey, Gerard; Murphy, Thomas Brendan; Science Foundation Ireland; Teagasc (Wiley, 02/07/2012)
      Fisher's linear discriminant analysis is one of the most commonly used and studied classification methods in chemometrics. The method finds a projection of multivariate data into a lower dimensional space so that the groups in the data are well separated. The resulting projected values are subsequently used to classify unlabeled observations into the groups. A semi-supervised version of Fisher's linear discriminant analysis is developed, so that the unlabeled observations are also used in the model fitting procedure. This approach is advantageous when few labeled and many unlabeled observations are available. The semi-supervised linear discriminant analysis method is demonstrated on a number of data sets where it is shown to yield better separation of the groups and improved classification over Fisher's linear discriminant analysis.