Discrimination of Fish Bones from other Animal Bones in the Sedimented Fraction of Compound Feeds by near Infrared Microscopy

Since the bovine spongiform encephalopathy (BSE) crisis, the use of animal proteins in animal feed has been prohibited. From October 2003, the European Union (EU) adopted Regulation (EC) no. 1774/2002 governing animal by-products (ABPs), which seeks to address the possible risks inherent in recycling potential infectivity due to the absence of barriers within species and to exclude the cannibalism which may be induced by intra-species recycling. There is an urgent need to develop fast and reliable methods for identification of low-level ABP origins. In this study, near infrared (NIR) microscopy was used to identify different classes of ABPs. Samples of fish meals (n = 10) and meals of land-animal origin (n = 50) were ground, sedimented and analysed using an Auto Image Microscope connected to a Fourier transform near infrared spectrometer (FT-NIR). Sediment fraction particles were spread on a Spectralon plate, presented to the NIR microscope and scanned in the 1112–2500 nm region. The support vector machine (SVM) algorithm was used to construct models to identify class origin. Models correctly classified 100% of the samples in the calibration set and between 95 and 95.5% in the validation set. The results demonstrated the potential of FT-NIR microscopy as a rapid method for distinguishing between fish and land-animal particles.

[1]  S. Engelsen,et al.  Can spectroscopy in combination with chemometrics replace minks in digestibility tests , 2000 .

[2]  Robert P. Cogdill,et al.  Least-Squares Support Vector Machines for Chemometrics: An Introduction and Evaluation , 2004 .

[3]  Daniel Cozzolino,et al.  The assessment of the chemical composition of fishmeal by near infrared reflectance spectroscopy , 2002 .

[4]  C von Holst,et al.  An overview of tests for animal tissues in feeds applied in response to public health concerns regarding bovine spongiform encephalopathy. , 2003, Revue scientifique et technique.

[5]  P Dardenne,et al.  Classification of modified starches by fourier transform infrared spectroscopy using support vector machines. , 2005, Journal of agricultural and food chemistry.

[6]  Vincent Baeten,et al.  Combination of support vector machines (SVM) and near‐infrared (NIR) imaging spectroscopy for the detection of meat and bone meal (MBM) in compound feeds , 2004 .

[7]  Wang Hong-mei.  An introduction and evaluation of Canada employment insurance act , 2003 .

[8]  Vincent Baeten,et al.  Determination of processed animal proteins, including meat and bone meal, in animal feed. , 2004, Journal of AOAC International.

[9]  D. Taylor,et al.  Rendering practices and inactivation of transmissible spongiform encephalopathy agents. , 2003, Revue scientifique et technique.

[10]  Vincent Baeten,et al.  Spectroscopy: Developments in instrumentation and analysis , 2002 .

[11]  J. E. Guerrero,et al.  Implementation of LOCAL Algorithm with Near-Infrared Spectroscopy for Compliance Assurance in Compound Feedingstuffs , 2005, Applied spectroscopy.

[12]  Vincent Baeten,et al.  The Contribution of near Infrared Spectroscopy to the Fight against the Mad Cow Epidemic , 2001 .

[13]  Taylor Dm,et al.  Rendering practices and inactivation of transmissible spongiform encephalopathy agents. , 2003 .

[14]  P. Williams,et al.  Near-Infrared Technology in the Agricultural and Food Industries , 1987 .

[15]  A. Trocino,et al.  Prediction of chemical composition, nutritive value and ingredient composition of European compound feeds for rabbits by near infrared reflectance spectroscopy (NIRS) , 2003 .

[16]  Ronald D. Snee,et al.  Validation of Regression Models: Methods and Examples , 1977 .

[17]  Daniel Cozzolino,et al.  Near infrared reflectance spectroscopy in the prediction of chemical characteristics of minced raw fish , 2002 .

[18]  M. Meurens,et al.  Nutritive evaluation and ingredient prediction of compound feeds for rabbits by near-infrared reflectance spectroscopy (NIRS) , 1999 .

[19]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[20]  Ian Murray,et al.  Use of Discriminant Analysis on Visible and near Infrared Reflectance Spectra to Detect Adulteration of Fishmeal with Meat and Bone Meal , 2001 .