Fatty acid composition in pork fat: De-novo synthesis, fatty acid sources and influencing factors – a review

ArticleFats are among the basic nutrients the human organism needs as a source of energy, as well as to grow and regenerate cells, tissues, and organs. Particularly animal fats, with their higher proportion of saturated fatty acids and low content of n-3 fatty acids, are often seen by the public as relatively undesirable food components. Fatty acid (FA) composition of pork is affected by many factors: genotype, breeding, gender and feeding methods. Numerous research teams, therefore, have searched for means of effectively manipulating the chemical composition of animal fats. This paper reviews existing knowledge and means of effectively influencing the fatty acid composition in pig fat, which is a significant component of human food in European countries due to their high consumption of pork. The findings of various authors demonstrate that not only altering of fatty acids sources in animal diets but a range of other factors as well can significantly influence the composition of fatty acids in pig fat and consequently pork quality

Economic Aspect of Growing Pigs with Respect to Sex and Immunocastration

The minimization of boar taint according to welfare appears to be immunocastration. For this purpose a test was carried out for a total of 80 piglets in the growing stage. Animals were housed by sex, respectively 20 boars, 20 immunocasatrates, 20 barrows and 20 gilts. Animals were fed ad libitum, and the production variables were observed with an emphasis on the overall economy of their production. Intergroup differences were tested by variance analyse and the test groups of growing pigs were evaluated using a profit formula. It was shown that animals in the growing period had excellent parameters of the fattening (in the 65 days, the total gain was 19 kg, with daily feed intake below 0.7 kg, feed conversion ratio 1.2 kg and average daily gain 540-560 g). The effect of sex, or the castration of young pigs on the economy of the pig production in growing period is proved to be insignificant. This hypothesis was confirmed

Muscle Fibre Types and Their Relation to Meat Quality Traits in Pigs

The authors have been studying various characteristics of muscle fibres and their relationship to the meat quality parameters for many years. However, the conclusions drawn by researchers often differ. A higher proportion of glycolytic IIB fibres in pig muscles is usually related to paler meat with lower water holding capacity. On the other hand the relationship between muscle fibres and meat texture parameters is not clear. Studies using immunohistochemistry methods that allow a more detailed classification of individual muscle fibre types could bring new findings in this area. It would thus be possible to influence muscle fibre type composition in the muscle to achieve the desired meat quality using various extrinsic and intrinsic factors. The main aim of this review is to summarise current knowledge on the description of muscle fibres typology and the effect of their morphological traits on pork meat quality

The prediction of lean meat and subcutaneous fat with skin content in pork cuts on the carcass meatness and weight

Early post-mortem, objective and non-destructive prediction of tissue distribution in the major pork cuts is a challenge for the meat industry. Mathematical models to predict pig carcass composition using total lean meat percentage and carcass weight were evaluated in this study. The data were obtained from 455 cold pig carcasses which were dissected according to the EU reference method; total lean meat percentage and carcass weight ranged from 42.45 to 69.21% and from 23.26 to 55.22 kg, respectively. Developed empirical models gave a reasonable fit to the experimental data and successfully predicted the carcass composition and tissue distribution in primal cuts. The second order polynomial models showed high coefficients of determination for prediction of experimental results (between 0.612 and 0.929), while the artificial neural network (ANN) model, based on the Broyden-Fletcher-Goldfarb-Shanno iterative algorithm, showed better prediction capabilities (overall r(2) was 0.889). The newly developed software, based on ANN model is easy, fast, cheap and with sufficient precision for application in the meat industry