Use of volatile compound metabolic signatures in poultry liver to back-trace dietary exposure to rapidly metabolized xenobiotics.

The study investigated the feasibility of using volatile compound signatures of liver tissues in poultry to detect previous dietary exposure to different types of xenobiotic. Six groups of broiler chickens were fed a similar diet either noncontaminated or contaminated with polychlorinated dibenzo-p-dioxins/-furans (PCDD/Fs; 3.14 pg WHO-TEQ/g feed, 12% moisture), polychlorinated biphenyls (PCBs; 0.08 pg WHO-TEQ/g feed, 12% moisture), polybrominated diphenyl ethers (PBDEs; 1.63 ng/g feed, 12% moisture), polycyclic aromatic hydrocarbons (PAHs; 0.72 µg/g fresh matter), or coccidiostats (0.5 mg/g feed, fresh matter). Each chicken liver was analyzed by solid-phase microextraction - mass spectrometry (SPME-MS) for volatile compound metabolic signature and by gas chromatography - high resolution mass spectrometry (GC-HRMS), gas chromatography - tandem mass spectrometry (GC-MS/MS), and liquid chromatography - tandem mass spectrometry (LC-MS/MS) to quantify xenobiotic residues. Volatile compound signature evidenced a liver metabolic response to PAH although these rapidly metabolized xenobiotics are undetectable in this organ by the reference methods. Similarly, the volatile compound metabolic signature enabled to differentiate the noncontaminated chickens from those contaminated with PBDEs or coccidiostats. In contrast, no clear signature was pointed out for slowly metabolized compounds such as PCDD/Fs and PCBs although their residues were found in liver at 50.93 (±6.71) and 0.67 (±0.1) pg WHO-TEQ/g fat, respectively.

Mass spectrometry based sensor strategies for the authentication of oysters according to geographical origin.

This study was undertaken to investigate the relevance of using the pyrolysis-MS (Py-MS) technique to discriminate the production area of oysters harvested over two years and to assess from the data of the second year of harvest the potential of an alternative MS-based technique, the solid phase microextraction-MS (SPME-MS), to perform this discrimination. Oysters were harvested in various areas of France, and models of discrimination according to harvest season were built from Py-MS fingerprints and from virtual SPME-MS fingerprints obtained by summing the mass spectra generated by the SPME-GC-MS system. The treatment of the Py-MS data by a 21-12-3 artificial neural networks led to a correct classification of only 89.2% of the oyster samples according to shoreline. The misclassifications thus did not allow use of the Py-MS technique as a relevant tool for authentication of oyster origin. The assessment of the potential of the virtual SPME-MS fingerprints to discriminate the production area of oysters was undertaken on a part of the sample set. The virtual SPME-MS data were pretreated according to two methods, filtering of raw data (FRD) and comprehensive combinatory standard correction (CCSC), a recently developed chemometric method used for the correction of instrumental signal drifts in MS systems. The results obtained with the virtual SPME-MS fingerprints are promising because this technique, when the data were pretreated by the CCSC method, led to a successful discrimination of the oyster samples not only according to shoreline but also according to production region. This study confirms that an efficient correction method (CCSC) of instrumental drifts can considerably increase the discriminative information contained in the volatile fraction of food products.

Influence of the spatial organization of the perimysium on beef tenderness.

The spatial distribution of the intramuscular connective tissue (IMCT) in four types of beef muscle (Biceps femoris, Infraspinatus, Longissimus thoracis, and Pectoralis profundus) was examined using histology and magnetic resonance imaging (MRI). The surface and the length of the IMCT and the surface of the myofiber bundles were evaluated by image analysis. The texture of the cooked meat from these muscles was measured both instrumentally by a compression test and by sensory analysis. The relationship between muscle structure and meat texture was studied by general discriminant analysis. The models obtained could assign correctly up to 87% of the samples to two tenderness classes. Histology and MRI provided complementary information about the microscopic and macroscopic IMCT structures, respectively. Both were necessary to predict sensory tenderness whereas only the MRI measurements were necessary to predict instrumental toughness. Tough muscles had smaller MRI myofiber bundles (0.7-1 mm radius) than tender muscles.