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drip Drip can be referred to by a number of different terms including purge loss, press loss, and thaw loss, depending on the method of measurement and when it is measured The protein concentration of drip is about 140 mg ml 1 (ie, about 70% of that of meat itself) The proteins in drip are the intracellular, soluble proteins of the muscle cells The red color is due to the protein myoglobin, the main pigment of meat Drip loss occurs throughout the cold chain and represents a considerable economic loss to the red meat industry The potential for drip loss is inherent in fresh meat and related to the development of rigor mortis in the muscle after slaughter and its effect on pH It is in uenced by many factors Some of these, including breed, diet, and physiological history, are inherent in the live animal.

Refer to Appendix B for a summary of the LTA mathematical model and its associated parameters. It is worth noting that the grouping variable can be treated as a discrete latent variable measured by several indicators. Here our grouping variable is sex, which is measured with a single indicator. With a single indicator there is no basis on which to estimate measurement error, so the relevant parameters are xed to 0 and 1. In general, there can be any number of manifest items and response categories, and the number of response categories can vary across items. As in LCA, LTA employs the EM algorithm. Issues of model selection and t are identical to those in the latent class framework. The use of parameter restrictions serves the same purpose as in a latent class framework. However, there are several reasons that these restrictions often play a larger role in LTA models. First, because complex models are often estimated in LTA, the number of parameters involved can be quite large. Imposing restrictions on the parameters can greatly aid in model identi cation. Second, as in all longitudinal models, it is important to consider the issue of measurement invariance over time. Measurement invariance over time can be explored by imposing constraints across the sets of parameters, or conditional response probabilities, estimated at each time. Constraining these parameters to be equal across time ensures that the meaning of the latent statuses remains consistent across time. If the measurement is structurally identical at both times, then changes in the class membership over time can be attributed solely to development rather than development mixed with changes in the relations between the

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Others, such as the rate of chilling, storage temperatures, freezing, and thawing, occur during processing When meat is frozen quickly, the water, both that released by the brils as the meat has gone into rigor and that which is still held, is frozen simultaneously Consequently, there is no change in the water s relative positions or amounts At slower freezing rates, however, the water balance is altered, the extracellular water freezing rst As freezing, continues, the existing ice crystals grow at the expense of water from the intra brillar space When meat is thawed, the reverse of freezing process occurs Water that has been frozen is released and has to reestablish equilibrium with the muscle proteins and salts Obviously, if the muscle proteins have been denatured, they will reabsorb less water.

To address the variety of groups previously described, the Group Manager enables the creation, disposal and update of existing groups and their related group pro les simultaneously Group Pro le Manager and Individual Pro le Manager The Group Pro le Manager links and stores pro le information with the related group models Three types of information that is stored into a pro le are distinguished: group characteristics, group preferences and group policies The Group Pro le Manager is associated to the Individual Pro le Managers of all group members Group preferences are aggregated from the personal preferences of the group members, being gathered from their Individual Pro le Managers Group Allocator The Group Allocator provides support for dynamic group discovery using context information; ie making sure the user nds the group which is the best t to her context.