A need for determination of arsenic species at low levels in cereal-based food and infant cereals. Validation of a method by LC-ICPMS.

The present study arose from the need to determine inorganic arsenic (iAs) at low levels in cereal-based food. Validated methods with a low limit of detection (LOD) are required to analyse these kinds of food. An analytical method for the determination of iAs, methylarsonic acid (MA) and dimethylarsinic acid (DMA) in cereal-based food and infant cereals is reported. The method was optimised and validated to achieve low LODs. Ion chromatography-inductively coupled plasma mass spectrometry (LC-ICPMS) was used for arsenic speciation. The main quality parameters were established. To expand the applicability of the method, different cereal products were analysed: bread, biscuits, breakfast cereals, wheat flour, corn snacks, pasta and infant cereals. The total and inorganic arsenic content of 29 cereal-based food samples ranged between 3.7-35.6 and 3.1-26.0 microg As kg-1, respectively. The present method could be considered a valuable tool for assessing inorganic arsenic contents in cereal-based foods.

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images.

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

Accuracy profile validation of a new analytical method for butane measurement using headspace-gas chromatography-mass spectrometry.

The aim of our study was to provide an innovative HS-GC/MS method applicable to the routine determination of butane concentration in forensic toxicology laboratories. The main drawback of the GC/MS methods discussed in literature concerning butane measurement was the absence of a specific butane internal standard necessary to perform quantification. Because no stable isotope of butane is commercially available, it is essential to develop a new approach by an in situ generation of standards. To avoid the manipulation of a stable isotope-labelled gas, we have chosen to generate in situ an internal labelled standard gas (C(4)H(9)D) following the basis of the stoichiometric formation of butane by the reaction of deuterated water (D(2)O) with Grignard reagent butylmagnesium chloride (C(4)H(9)MgCl). This method allows a precise measurement of butane concentration and therefore, a full validation by accuracy profile was presented.