Detection of monensin residues in poultry liver using an enzyme immunoassay

Monensin, a carboxylic acid ionophore, is commonly fed to poultry to control coccidiosis. A method for the detection and quantification of monensin residues in liver has been developed, Samples (3 g) were extracted with acetonitrile-water and applied to a competitive enzyme immunoassay using a polyclonal antiserum raised against a monensin-transferrin conjugate, The limit of detection (mean + 3s) calculated from the analysis of 12 known negative samples was 2.91 ng g(-1). Intra- and inter-assay RSD were determined as 8.5 and 10.6%, respectively, using a liver sample fortified with 20 ng g(-1) monensin, A pharmacokinetic study in which 70 six week old broilers were fed monensin at a rate of 120 mg kg(-1) in their feed for 14 d resulted in mean monensin liver residues of 102 ng g(-1). However these had fallen below the limit of detection of the assay within the 3 d withdrawal period recommended by the manufacturer.

The use of handheld near-infrared reflectance spectroscopy (NIRS) for the proximate analysis of poultry feed and to detect melamine adulteration of soya bean meal

The use of handheld near infrared (NIR) instrumentation, as a tool for rapid analysis, has the potential to be used widely in the animal feed sector. A comparison was made between handheld NIR and benchtop instruments in terms of proximate analysis of poultry feed using off-the-shelf calibration models and including statistical analysis. Additionally, melamine adulterated soya bean products were used to develop qualitative and quantitative calibration models from the NIRS spectral data with excellent calibration models and prediction statistics obtained. With regards to the quantitative approach, the coefficients of determination (R2) were found to be 0.94-0.99 with the corresponding values for the root mean square error of calibration and prediction were found to be 0.081-0.215 % and 0.095-0.288 % respectively. In addition, cross validation was used to further validate the models with the root mean square error of cross validation found to be 0.101-0.212 %. Furthermore, by adopting a qualitative approach with the spectral data and applying Principal Component Analysis, it was possible to discriminate between adulterated and pure samples.