Detecting and quantifying meat meal or meat and bone meal contamination in fishmeal by visible and near infrared reflectance spectra

The use of animal protein feeds such as meat meal or meat and bone meal (MMBM) play an important role in the feed manufacturing industry, but their safe and healthy use in animal feeds is of public concern in order to prevent the spread of bovine spongiform encephalopathy (BSE). The objective of the present work was to develop a technique using near infrared reflectance spectroscopy (NIRS) that would be suitable for detecting and quantifying contaminating levels of MMBM in fishmeal. To this end, a partial least squares (PLS) discriminant analysis and a modified partial least squares (MPLS) quantitative analysis, using visible and NIRS, were developed using a calibration set of 186 samples including 90 samples of pure fishmeal and 96 samples adulterated with MMBM at levels ranging from 10 to 320 g/kg. An external validation set, comprised of 39 pure samples and 54 adulterated samples, was used to validate the calibration model. A PLS discriminant analysis model developed with mathematic pretreatment 1,4,4,1, successfully detected fishmeal adulterated with MMBM. External validation indicated that all samples were discriminated correctly. A MPLS quantitative model, developed with mathematic pretreatment 1,4,4,1, also successfully predicted the MMBM in fishmeal with standard error of cross-validation (SECV) of 27.89 g/kg and ratio of the standard deviation of the validation set to the standard error of prediction (RPD) of 3.37. The calibration and validation results confirm that NIRS could provide the feed industry and inspection bodies with a rapid, non-destructive and non-invasive technique for the detection and quantification of MMBM in fishmeal.