Identification by volatolomics of hydrocarbon, oxygenated, sulfur and aromatic markers of livestock exposure to α-hexabromocyclododecane.

Volatile organic compounds (VOC)-based metabolomics, or volatolomics, was investigated for revealing livestock exposure to chemical contamination. Three farm animals, namely laying hens, broilers, and pigs, were experimentally exposed to 5 or 50 ng α-HBCDD g-1 feed. Liver and egg yolk for hens were analysed by headspace-SPME-GC-MS to reveal candidate markers of the livestock exposure to α-HBCDD. For hens, 2-butanol was found as marker in egg. In liver, twelve VOCs were highlighted as markers, with three aromatic VOCs - styrene, o-xylene, α-methylstyrene - highlighted for the two α-HBCDD doses. For broilers, six markers were revealed, with interestingly, styrene and phenol which were also found as markers in hen liver. For pigs, ten markers were revealed and the seven tentatively identified markers were oxygenated and sulfur VOCs. The candidate markers tentatively identified were discussed in light of previous volatolomics data, in particular from a γ-HBCDD exposure of laying hens.

Comparison of common components analysis with principal components analysis and independent components analysis: Application to SPME-GC-MS volatolomic signatures.

The aim of this work is to compare a novel exploratory chemometrics method, Common Components Analysis (CCA), with Principal Components Analysis (PCA) and Independent Components Analysis (ICA). CCA consists in adapting the multi-block statistical method known as Common Components and Specific Weights Analysis (CCSWA or ComDim) by applying it to a single data matrix, with one variable per block. As an application, the three methods were applied to SPME-GC-MS volatolomic signatures of livers in an attempt to reveal volatile organic compounds (VOCs) markers of chicken exposure to different types of micropollutants. An application of CCA to the initial SPME-GC-MS data revealed a drift in the sample Scores along CC2, as a function of injection order, probably resulting from time-related evolution in the instrument. This drift was eliminated by orthogonalization of the data set with respect to CC2, and the resulting data are used as the orthogonalized data input into each of the three methods. Since the first step in CCA is to norm-scale all the variables, preliminary data scaling has no effect on the results, so that CCA was applied only to orthogonalized SPME-GC-MS data, while, PCA and ICA were applied to the "orthogonalized", "orthogonalized and Pareto-scaled", and "orthogonalized and autoscaled" data. The comparison showed that PCA results were highly dependent on the scaling of variables, contrary to ICA where the data scaling did not have a strong influence. Nevertheless, for both PCA and ICA the clearest separations of exposed groups were obtained after autoscaling of variables. The main part of this work was to compare the CCA results using the orthogonalized data with those obtained with PCA and ICA applied to orthogonalized and autoscaled variables. The clearest separations of exposed chicken groups were obtained by CCA. CCA Loadings also clearly identified the variables contributing most to the Common Components giving separations. The PCA Loadings did not highlight the most influencing variables for each separation, whereas the ICA Loadings highlighted the same variables as did CCA. This study shows the potential of CCA for the extraction of pertinent information from a data matrix, using a procedure based on an original optimisation criterion, to produce results that are complementary, and in some cases may be superior, to those of PCA and ICA.

Solid-phase microextraction set-up for the analysis of liver volatolome to detect livestock exposure to micropollutants.

Starting from a critical analysis of a first "proof of concept" study on the utility of the liver volatolome for detecting livestock exposure to environmental micropollutants (Berge et al., 2011), the primary aim of this paper is to improve extraction conditions so as to obtain more representative extracts by using an extraction temperature closer to livestock physiological conditions while minimizing analytical variability and maximizing Volatile Organic Compound (VOC) abundancies. Levers related to extraction conditions and sample preparation were assessed in the light of both abundance and coefficient of variation of 22 candidate VOC markers identified in earlier volatolomic studies. Starting with a CAR/PDMS fiber and a 30min extraction, the reduction of SPME temperature to 40°C resulted in a significant decrease in the area of 14 candidate VOC markers (p<0.05), mainly carbonyls and alcohols but also a reduction in the coefficient of variation for 17 of them. In order to restore VOC abundances and to minimize variability, two approaches dealing with sample preparation were investigated. By increasing sample defrosting time at 4°C from 0 to 24h yielded higher abundances and lower variabilities for 15 and 13 compounds, respectively. Lastly, by using additives favouring the release of VOCs (1.2g of NaCl) the sensitivity of the analysis was improved with a significant increase in VOC abundances of more than 50% for 13 out of the 22 candidate markers. The modified SPME parameters significantly enhanced the abundances while decreasing the analytical variability for most candidate VOC markers. The second step was to validate the ability of the revised SPME protocol to discriminate intentionally contaminated broiler chickens from controls, under case/control animal testing conditions. After verification of the contamination levels of the animals by national reference laboratories, data analysis by a multivariate chemometric method (Common Components and Specific Weights Analysis - ComDim) showed that the liver volatolome could reveal dietary exposure of broilers to a group of environmental pollutants (PCBs), a veterinary treatment (monensin), and a pesticide (deltamethrin), thus confirming the usefulness of this analytical set-up.

Analysis of the volatile profile and identification of odour-active compounds in Bayonne ham.

The aim of this work was to reliably identify odour-active compounds in dry-cured ham using powerful analysis methods for the volatile fraction. For this purpose, dynamic headspace gas chromatography combined with eight-way olfactometry using a panel of eight sniffers was used. One- and two-dimensional gas chromatography coupled with mass spectrometry and (or) olfactometry were also used. More than 600 compounds from the volatile fraction of dry-cured ham were identified and their biochemical origins are discussed. They covered a wide diversity of structures and chemical functions. Only 29 of them proved odour-active. Comparison of the results of GC-O analysis with those obtained by orthonasal sniffing of the dry-cured ham helped to gain a better understanding of how these substances contributed to the overall aroma of the product. Thus, "Fruity-Floral", "Green-Vegetable" or "Plastic-Chemical" odours intensively perceived by GC-O have been poorly perceived by orthonasal sniffing. By contrast, "Animal-Meat products" or "Butter-Lactic-Cheesy" odours have been much better perceived by orthonasal sniffing. These results indicate that to understand the interactions between odour-active compounds, experimental doping with carefully selected odour-active compounds will be necessary.

Dielectric behavior of beef meat in the 1-1500kHz range: Simulation with the Fricke/Cole-Cole model.

The electrical properties of biological tissues have been researched for many years. Impedance measurements observed with increasing frequencies are mainly attributed to changes in membrane conductivity and ion and charged-molecule mobility (mainly Na(+), K(+), CL(-) ions). Equivalent circuits with passive electrical components are frequently used as a support model for presentation and analyses of the behavior of tissues submitted to electrical fields. Fricke proposed an electrical model where the elements are resistive and capacitive. The model is composed of a resistive element (Rp) representing extracellular fluids (ECF) placed in parallel with a capacitive element (Cs) representing insulating membranes in series and a resistive element (Rs) representing intracellular fluids (ICF). This model is able to describe impedance measurements: at lower frequencies, most of the current flows around the cells without being able to penetrate them, while at higher frequencies the membranes lose their insulating properties and the current flows through both the extracellular and intracellular compartments. Since meat ageing induces structural change, particularly in membrane integrity, the insulating properties of membranes decrease, and intracellular and extracellular electrolytes mix, thus driving changes in their electrical properties. We report a method combining the Fricke and Cole-Cole models that was developed to monitor and explain tissues conductivity changes in preferential directions during beef meat ageing.