Residue Violation Information

In addition, FSIS has implemented a nationwide interagency computerized information database known as the Residue Violation Information System (RVIS), to review, sort, cross-reference, and manage all residue data obtained by FSIS, FDA, or other agencies from residue violation cases. This includes names and addresses of sellers and producers, dealers, and the results of investigations. 

Table 13.7 provides information on the antibiotic and sulfonamide violations detected during 1992-1994. Sample sizes differed in the various animal slaughter classes in different years. Thus, it would not be appropriate to attribute undue significance to the number of violations detected of an individual compound. Table 13.7 does provide relevant information about the specific compounds that produced violative residue concentrations in food animals during that period. Residue violations for more than one compound might occasionally occur in the same animal. 

As discussed below, various residue detection programs are in use. All are designed to minimize the incidence of non-compliant residues. In the matter of prevention, the role of the Food Animal Residue Avoidance Databank (FARAD) should be noted. FARAD is a USDA-supported computerized databank, established in 1982, with the objective of minimizing residue violations, through the collection, collation, and dissemination of information relevant... 

Step 8 Measuring Results and Monitoring Performance The evaluation of MPC system performance is not easy, and widely accepted metrics and monitoring strategies are not available, ffow-ever, useful diagnostic information is provided by basic statistics such as the means and standard deviations for both measured variables and calculated quantities, such as control errors and model residuals. Another useful statistic is the relative amount of time that an input is saturated or a constraint is violated, expressed as a percentage of the total time the MPC system is in service. 

The system identification step in the core-box modeling framework has two major sub-steps parameter estimation and model quality analysis. The parameter estimation step is usually solved as an optimization problem that minimizes a cost function that depends on the model s parameters. One choice of cost function is the sum of squares of the residuals, Si(t p) = yi(t) — yl(t p). However, one usually needs to put different weights, up (t), on the different samples, and additional information that is not part of the time-series is often added as extra terms k(p). These extra terms are large if the extra information is violated by the model, and small otherwise. A general least-squares cost function, Vp(p), is thus of the form... 

---