ABSTRACT
Red meat is long established as an important dietary source of protein and essential nutrients including iron, zinc and vitamin B12, yet recent reports that its consumption may increase the risk of cardiovascular disease (CVD) and colon cancer have led to a negative perception of the role of red meat in health. The aim of this paper is to review existing literature for both the risks and benefits of red meat consumption, focusing on case-control and prospective studies. Despite many studies reporting an association between red meat and the risk of CVD and colon cancer, several methodological limitations and inconsistencies were identified which may impact on the validity of their findings. Overall, there is no strong evidence to support the recent conclusion from the World Cancer Research Fund (WCRF) report that red meat has a convincing role to play in colon cancer. A substantial amount of evidence supports the role of lean red meat as a positive moderator of lipid profiles with recent studies identifying it as a dietary source of the anti-inflammatory long chain (LC) n-3 PUFAs and conjugated linoleic acid (CLA). In conclusion, moderate consumption of lean red meat as part of a balanced diet is unlikely to increase risk for CVD or colon cancer, but may positively influence nutrient intakes and fatty acid profiles, thereby impacting positively on long-term health.
Subject(s)
Diet , Meat , Animals , Cardiovascular Diseases/epidemiology , Cattle , Colonic Neoplasms/epidemiology , Diet/adverse effects , Fatty Acids/blood , Fatty Acids, Omega-3/administration & dosage , Female , Food Handling , Humans , Ireland , Linoleic Acids, Conjugated/administration & dosage , Male , Meat/adverse effects , Meat/analysis , Meat/classification , Meat Products/adverse effects , Nutritive Value , Risk Assessment , Sex Characteristics , United KingdomABSTRACT
In this study multivariate analysis of Raman spectra has been used to classify adipose tissue from four different species (chicken, beef, lamb and pork). The adipose samples were dissected from the carcass and their spectra recorded without further preparation. 102 samples were used to create and compare a range of statistical models, which were then tested on 153 independent samples. Of the classical multivariate methods employed, Partial Least Squares Discriminant Analysis (PLSDA) performed best with 99.6% correct classification of species in the test set compared with 96.7% for Principal Component Linear Discrimination Analysis (PCLDA). Kohenen and Feed-forward artificial neural networks compared well with the PLSDA, giving 98.4 and 99.2% correct classification, respectively.
