EXTERNAL VALIDATION AND TRANSFERABILITY OF NIRS MODELS DEVELOPED FOR DETECTING AND QUANTIFYING MBM IN INTACT COMPOUND FEEDING STUFFS

Two-dimensional correlation spectroscopy (2D-COS) variable selection for near-infrared microscopy discrimination of meat and bone meal in compound feed

This article presents a novel method for combining auto-peak and cross-peak information for sensitive variable selection in synchronous two-dimensional correlation spectroscopy (2D-COS). This variable selection method is then applied to the case of near-infrared (NIR) microscopy discrimination of meat and bone meal (MBM). This is of important practical value because MBM is currently banned in ruminate animal compound feed. For the 2D-COS analysis, a set of NIR spectroscopy data of compound feed samples (adulterated with varying concentrations of MBM) was pretreated using standard normal variate and detrending (SNVD) and then mapped to the 2D-COS synchronous matrix. For the auto-peak analysis, 12 main sensitive variables were identified at 6852, 6388, 6320, 5788, 5600, 5244, 4900, 4768, 4572, 4336, 4256, and 4192 cm(-1). All these variables were assigned their specific spectral structure and chemical component. For the cross-peak analysis, these variables were divided into two groups, each group containing the six sensitive variables. This grouping resulted in a correlation between the spectral variables that was in accordance with the chemical-component content of the MBM and compound feed. These sensitive variables were then used to build a NIR microscopy discrimination model, which yielded a 97% correct classification. Moreover, this method detected the presence of MBM when its concentration was less than 1% in an adulterated compound feed sample. The concentration-dependent 2D-COS-based variable selection method developed in this study has the unique advantages of (1) introducing an interpretive aspect into variable selection, (2) substantially reducing the complexity of the computations, (3) enabling the transferability of the results to discriminant analysis, and (4) enabling the efficient compression of spectral data.