Performances and limitations of the HRMS method for dioxins, furans and dioxin-like PCBs analysis in animal feedingstuffs: Part I: Results of an inter-laboratory study

The European strategy for dioxin monitoring of the food chain has defined high-resolution gas chromatography coupled to high-resolution mass spectrometry (HRGC/HRMS) method as the confirmatory method that can provide reliable and comparable results at sub-parts per trillion (ppt) level. This paper describes the first inter-laboratory study on dioxins, furans and dioxin-like PCBs by HRGC/HRMS method in animal feedingstuffs. Two different statistical approaches (ISO 5725 and Cofino’s statistics) were used for the statistical evaluation. For this particular study, the performances of the HRGC/HRMS method seem to be congener-independent in repeatability and reproducibility conditions over a concentration range covering more than four orders of magnitude. Results clearly show the effect of precision loss below 0.1 ppt level per congener in repeatability conditions and below 0.2 ppt level per congener in reproducibility conditions. LODs reported by the laboratories give median values of 0.02 ng/kg for most of the toxic congeners. Relative standard deviation between the laboratories’ mean values using upper-bound approach for TEQ calculation is 6.2%, more than twice the maximum level set at 0.75 ng TEQ/kg of product.     

Spectral library validation to identify ingredients of compound feedingstuffs by near infrared reflectance microscopy

To guarantee feed quality and safety the development and improvement of analytical methods for feed authentication and detection of contaminants is fundamental. Near infrared reflectance microscopy (NIRM) has been investigated as an alternative method to contribute to control systems for feed materials. The major task is the need to build NIRM reference spectral libraries that must represent the variability in feed ingredients. The aim of the present work was to evaluate the performance of a NIRM reference spectral library on animal feed, with external samples of animal feed ingredients and possible contaminants such as processed animal proteins, and in particular to assess its ability to identify ingredients in mixtures. Three external sample sets were used: (A) artificial mixtures, (B) synthetic mixtures and (C) synthetic binary mixtures. The prediction and repeatability results for set A, in which the spectra are from pure ingredients, were very good for both animal and vegetable ingredients and confirm that the spectral library is very good at identifying spectra from pure ingredients. For sets B and C, in which the spectra were measured on mixtures, the prediction results were very disappointing compared with the artificial samples. This means that a strategy that tries to match the spectra taken from a mixture with those of pure ingredients is unlikely to meet with much success. It is possible that an interpolation between pure ingredients for suitably chosen spectral ranges may provide a way to extend this system to mixtures, including mixtures of several ingredients.