Abstract
This study investigated the detection of contamination of animal feed by melamine and its derivatives by rapid analytical methods. The main goal was to propose an effective tool to detect contaminantion by using multivariate calibration equations built on a large database of non-contaminated feed ingredients. Soybean meal, maize gluten and wheat gluten samples were contaminated by different percentages of melamine and cyanuric acid. The influence of these additives on near infrared (NIR) predicted values of crude protein was studied. The predicted values of protein, in terms of the adulteration percentage, were compared with those obtained by conventional methods (Kjeldahl and Dumas). The addition of the contaminant led to an increase in the protein value when measured by classical methods and to a decrease in the value when predicted by the NIR calibration models. Among the modifications in the spectral profile of affected feed was the intensity of the spectrum at about 2170 nm, characteristic of the absorption of proteins which might explain the reduction in NIR predicted protein values when contaminants were added. An important advantage of the approach is the simultaneous detection of several analytes, making it possible to detect melamine and cyanuric acid at the same time. Contaminated feed was analysed using the near infrared (NIR) general feed ingredient database. Calibration equations were developed and applied to the samples in this study to visualise their distribution with regard to the existing data set that does not contain contaminants. Contaminated samples presented global H ( GH) (Mahalanobis distance) values greater than three and were easily distinguished from the rest. Both the full spectrum and a selected spectral region between 2130 nm and 2230 nm, including wavelengths relevant for discrimination, were used to develop mathematical equations to predict the protein content and to detect contaminated samples.
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