Method for the determination of dialkyl phosphates in urine by strong anion exchange disk extraction and in-vial derivatization

A method for the determination of four dialkylphosphate metabolites in urine by strong anion exchange disk (SAX) was investigated. Calcium hydroxide was added to a 1-mL urine sample to reduce interference. The aliquot was passed through the SAX disk to accumulate dialkylphosphate metabolites on the disk. The retained dialkylphosphate metabolites were derivatized with methyl iodide in acetonitrile online, and the resulting methyl esters of dialkylphosphate metabolites were directly analyzed by capillary column gas chromatography with flame photometric detection. The recoveries of these dialkylphosphate metabolites were found to be stable. When the intact sample was diluted with deionized water at a 1:1 ratio, the recoveries were both increased and stabilized. The urine samples collected from eight fruit farmers showed that levels of dialkylphosphate metabolites in urine were significantly different before and after pesticide application, indicating the method established in this study is applicable for real sample analysis. Compared with previous studies, this method not only can greatly simplify sample preparation, but it can also significantly reduce the consumption of toxic solvents in sample preparation.

Genotype and treatment biases in estimation of carcass lean of swine

Carcasses of 181 barrows, representing five genotypes, 1) H x HD, 2) SYN, 3) HD x L[YD], 4) L x YD, and 5) Y x L (H = Hampshire, D = Duroc, SYN = synthetic terminal sire line, L = Landrace, and Y = Yorkshire), and two levels of ractopamine (RAC) treatment (0 and 20 ppm) were completely dissected and the data were used to examine genotype and treatment (RAC) biases in estimation of fat-standardized lean weight and to evaluate accuracies and precisions realized by use of equations based on variables derived from different technologies. Independent variables used to establish regression equations represented technologies of direct carcass measurements, optical probe data, TOBEC (total body electrical conductivity) readings, and dissected (DHMLN) and fat-standardized (FSHMLN) ham lean. Genotype bias existed when any equation from a single technology was used and was minimized by combining FSHMLN with one TOBEC reading, carcass length, and the probe measurement of 10th rib fat depth. Large RAC biases appeared when equations from direct carcass measurements or optical probe data were used and were minimized by an equation using either DHMLN or FSHMLN. A practical equation with relatively high R2 value and small genotype and RAC biases were developed by combining TOBEC readings with direct carcass measurements of 10th rib fat depth and warm carcass weight.

Evaluation of alternative techniques to determine pork carcass value

Three techniques for estimating the value of pork carcasses were evaluated: an optical probe, a real-time ultrasound scanner, and an electromagnetic scanner (EMSCAN). The ability of these techniques to predict carcass value was compared to the predictive ability of actual measures of backfat depth and longissimus muscle area taken with a ruler and a dot grid. Results indicated the EMSCAN model was the best predictor of carcass value. However, the optical probe, ultrasound, and the ruler/dot grid all provided information not contained in the EMSCAN model. The choice among ultrasound, the optical probe, and the ruler/dot grid depends on how the carcass will be used. There is no significant difference between ultrasound and the ruler/dot grid or the optical probe and the ruler/dot grid if the carcass is to be marketed in wholesale primal form, but the ruler/dot grid is superior if the ham and loin are to be sold as lean, boneless products. A model combining the EMSCAN and optical probe readings provided more accurate value predictions than either technique alone. A carcass value matrix for use in pricing pork carcasses was developed using readings from the optical probe. Carcass use has a substantial impact on value differences between fat and lean pigs.

Effects of ractopamine, genotype, and growth phase on finishing performance and carcass value in swine: I. Growth performance and carcass merit

A replicated factorial experiment using 183 individually fed crossbred barrows was conducted. Pigs were fed an 18.5% CP (.95% lysine) diet with 3,594 kcal of ME/kg. The effects of five genotypes (GT): 1) Hampshire (H) x (H x Duroc [D]), 2) synthetic terminal sire line, 3) (H x D) x (Landrace [L] x [Yorkshire (Y) x D]), 4) L x (Y x D), and 5) Y x L; two levels of ractopamine (RAC) treatment: 0 and 20 ppm; and three treatment weight periods (WT): 1) 59 to 100, 2) 73 to 114, and 3) 86 to 127 kg live weight on growth performance and carcass traits were evaluated. Ractopamine increased (P less than .0001) amount (FSL) and percentage (PFSL) of carcass lean standardized to 10% fat content, loin eye area (LEA), and dressing percentage (PDRES) and reduced (P less than .01) amount (DF) and percentage (PDF) of dissected fat. Magnitudes of RAC effects were smaller than those reported by other researchers. Effects of GT and WT on all growth and carcass traits were highly significant (P less than .001) except for those of WT on ADG (P less than .05) and GT on average feed intake (AFI) and backfat thickness at the last rib (BFLR; P less than .05). Genotype 3 performed better for most economically important traits than did GT 1 and 4, suggesting that heterosis existed in GT 3, which essentially was obtained by crossing GT 1 and 4. Among the three treatment periods, WT 2 had the highest ADG. As BW increased from WT 1 to 3, AFI and AFI/ADG ratio (FCR) increased and lean percentage decreased.(ABSTRACT TRUNCATED AT 250 WORDS)

Effects of ractopamine, genotype, and growth phase on finishing performance and carcass value in swine: II. Estimation of lean growth rate and lean feed efficiency

A replicated factorial experiment using 183 individually fed crossbred barrows was conducted. The pigs were fed an 18.5% CP (.95% lysine) diet with 3.594 kcal of ME/kg. The effects of five genotypes (GT): 1) Hampshire (H) X (H X Duroc [D]), 2) synthetic terminal sire line, 3) (H X D) X (Landrace [L] X [Yorkshire (Y) X D]), 4) L X (Y X D), and 5) Y X L; two levels of ractopamine (RAC) treatment: 0 and 20 ppm; and three treatment weight periods (WT): 1) 59 to 100, 2) 73 to 114, and 3) 86 to 127 kg live weight on ADG of dissected lean (ADLG) and fat standardized lean adjusted to 10% fat content (ADSLG) and feed efficiency of ADLG (LFE) and ADSLG (SLFE) were evaluated. Initial carcass lean quantity of each individual animal was determined by a regression equation (R2 = .95) generated from 30 additional barrows (six per GT) slaughtered at 59 kg and 30 (six per GT) untreated pigs slaughtered at 100 kg average live weight. Logarithmic and reciprocal transformations of dependent variables were used to stabilize heterogeneous variances and to improve normality of the residuals. Ractopamine increased (P less than .0001) ADLG, ADSLG, LFE, and SLFE, respectively, by 19.5, 25.0, 19.6, and 25.5%. Differences (P less than .001) were observed among genotypes for all traits, showing that considerable variation existed in the data and indicating that genetic improvement can be realized through the identification and selection of superior genotypes.(ABSTRACT TRUNCATED AT 250 WORDS)

Practical means for estimating pork carcass composition

Three hundred sixty-one market-weight barrow and gilt carcasses were physically dissected into bone, skin, fat and muscle. A three-variable multiple linear regression equation containing the same independent variables (warm carcass weight, 10th rib loin muscle area and 10th rib fat depth) used (U.S.) to determine pork carcass lean weight was found to be the most practical means for predicting weight of muscle standardized to 10% fat. Multiple linear regression equations containing more than three independent variables produced only slight improvements in R2 values; however, the standard deviation about the regression line was not greatly improved by the addition of more independent variables to this three-independent-variable regression model. A single multiple linear regression equation using the three independent variables above may not be adequate to describe variation over the entire live-weight range for all hogs marketed in the U.S. For most accurate muscle weight prediction, different equations should be used for weight subclasses with one equation for carcasses under 100 kg and another for those heavier than 100 kg. A single prediction equation for muscle weight was adequate for carcasses of both barrows and gilts.