Residue Detection Programs

Federal and national agencies have adopted residue detection (control) programs for both domestically produced and imported products. The control programs vary, as they are structured according to the needs of the particular country, but overall it might be noted that published results 

PHARMACOKINETICS, DISTRIBUTION, BIOAVAILABILITY, AND RELATIONSHIP TO ANTIBIOTIC RESIDUES 

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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... 

The FDA pesticide residue monitoring program analyzes selected baby foods for endosulfan under its Total Diet Study. In the period 1991-1995, 29 incidences of detectable amounts of endosulfan were reported from analyses of 276 items purchased in 12 separate collections (FDA 1995). 

It is correct to note that the specific tolerance levels requested by the manufacturer are determined solely on the basis of agricultural practices and not upon potential human health considerations. As such, tolerances represent enforcement tools to determine whether pesticide applications were made in accordance with the law but should not be considered as safety standards. In the case where a pesticide is used properly, the resulting residue level should be below the tolerance level. Residues detected in excess of the established tolerance are likely encountered only in cases where applications are not made in accordance with the legal directions. Results obtained from federal and state monitoring programs demonstrate that the incidence of residues detected in excess of tolerances is very low and suggest that most pesticide applications are made legally. 

It should be noted that a comparable survey and assessment task in the gunshot residue detection area recently has been completed by Aerospace. The long-range objectives of this research program are to develop rapid, reliable and inexpensive techniques and equipment for use by crime laboratories in the detection of gunshot residues on the hands of suspects. 

A study of estuarine fish in 21 coastal states conducted from 1972 to 1976 as part of the National Pesticide Monitoring Program detected a mean concentration of 47 ppb in 3.9% of the fish tissue samples collected (Butler and Schutzmann 1978). In another study (Cooper 1991), fish collected in a watershed area of Mississippi were analyzed for residues of methyl parathion. Methyl parathion was detected in seven species of fish, with white bass having the greatest mean concentration, at 15.96 ppm. Methyl parathion was found in 3 of the 32 fish samples collected before spraying of methyl parathion and in 12 of the 25 samples of fish collected after methyl parathion spraying. 

Special attention was paid to the detection of residual Cu-fl quantities accompanying the metallic Cu. The relative amounts of Cu+1 and Cu were determined by curve-fitting the Cu (LMM) spectra using the Physical Electronics Version 6 curve-fitting program. The catalyst showed reduction of Cu+2 Into a mixture of Cu+1 and Cu after reduction In H2 at 250 C for one hour (Figure 6) as evidenced by the two resolved peaks In the Cu (LMM) spectrum at 568.0 and 570.3 eV which are characteristic of Cu and Cu+1, respectively, and by the disappearance of the Cu+2 2p satellite structure. It could be shown that less than 2%, If any, of the total Cu could be present In the +1 oxidation state during methanol formation. However, when the catalyst was briefly exposed to air (1 minute), a few percent of Cu+1 readily formed (7). Thus, any kind of oxidation environment has to be avoided between methanol synthesis and catalyst analysis. Otherwise, appreciable amounts of Cu+1 will be detected. 

US EPA, Assigning Values to Non-detected/Non-quantified Pesticide Residues in Human Health Food Exposure Assessments, Guidance Document Office of Pesticide Programs, US Environmental Protection Agency, Washington, DC (March 23,2000). Also available on the World Wide Web http //www.epa.gov/pesticides/trac/science/trac3b012.pdf. 

Figure 5.19 Formation of amino acids on ice surfaces irradiated in the laboratory (Nature Nature 416, 403-406 (28 March 2002) doi 10.1038/416403a-permission granted). Data were obtained from analysis of the room temperature residue of photoprocessed interstellar medium ice analogue taken after 6 M HCl hydrolysis and derivatization (ECEE derivatives, Varian-Chrompack Chirasil-L-Val capillary column 12 m x 0.25 mm inner diameter, layer thickness 0.12 pirn splitless injection, 1.5 ml min-1 constant flow of He carrier gas oven temperature programmed for 3 min at 70°C, 5°C min-1, and 17.5 min at 180°C detection of total ion current with GC-MSD system Agilent 6890/5973). The inset shows the determination of alanine enantiomers in the above sample (Chirasil-L-Val 25 m, single ion monitoring for Ala-ECEE base peak at 116 a.m.u.). DAP, diaminopentanoic acid DAH, diaminohexanoic acid a.m.u., atomic mass units.

Another computational approach for detecting /1-solenoid sequences is implemented in a program called BETAWRAP (Bradley et al., 2001). This approach aims to identify /1-solenoid sequences by using hydrophobic-residue sequence patterns of strand-turn-strand regions that were learned from non-/l-solenoid structures. This method also takes into consideration the repetitive character of these patterns in /1-solenoids. Unlike the sequence profile approaches, BETAWRAP can make ab initio predictions of /1-solenoid domains. However, it is less sensitive than the profile search and, sometimes, cannot distinguish /1-solenoids from other solenoids (A. V. K, unpublished observation) such as, for example, LRR proteins (Kobe and Deisenhofer, 1994 Kobe and Kajava, 2001). The latest modification of BETAWRAP algorithm, which is called BETAWRAPPRO (McDonnell et al., 2006), employs additional data provided by sequence profiles and this improves the results of /1-solenoid predictions. 

Mirex was not detected in 27,065 samples of food collected in 10 state food laboratories from 1988 and 1989 (Minyard and Roberts 1991). Mirex was also not detected in domestically produced or imported foods sampled as part of the FDA Pesticide Residue Monitoring Study during 1988-1989 (FDA 1990), was detected (at less than 1 % occurrence) in foods sampled in 1989-1990 (FDA 1991), and was not detected in foods sampled in 1990-1991 and 1992-1992 (FDA 1992, 1993). Mirex residues were detected in one sample of 806 composited milk samples collected through the Pasteurized Milk Program by the EPA in 1990-1991 (Trotter and Dickerson 1993). The milk was sampled at 63 stations that provide an estimated 80% of the milk delivered to U.S. population centers. At each station, milk from selected sources was composited to represent milk routinely consumed in the station s metropolitan area. The detection of mirex occurred in milk samples from Cristobal, Panama. 

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