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With measurement uncertainty estimation accounting for all relevant uncertainty contributions, the results of measurements using the same procedure on different objects or samples may no longer be considered as being independent, and correlations have to be taken into account. For this purpose, a simple approximation for the estimation of covariances is derived and applied to the estimation of uncertainty for some basic combinations of two measurement results. This covariance estimate is also applied to the estimation of uncertainty for the mean value of the results of replicate measurements on the same object or sample.
Distribution of Trichothecenes, Zearalenone, and Ergosterol in a Fractionated Wheat Harvest Lot
(2008)
To investigate possible co-occurrences of type B trichothecenes and zearalenone within a Fusarium culmorum-infected wheat harvest lot, kernels were fractionated into six groups by visual criteria. The Fusarium-damaged kernels were subdivided into white, shrunken, and red kernel groups, and the remaining kernels were sorted into healthy, black spotted, and nonspecific groups. The distribution patterns of nivalenol, deoxynivalenol, zearalenone, and ergosterol were determined for possible correlations. Significant correlations between the distribution patterns were found for the mycotoxins and ergosterol for the grouped kernels (r = 0.997-0.999, p < 0.0001). Additionally, remarkably outstanding levels of nivalenol (24-fold more than the mean at 1.16 mg/kg), deoxynivalenol (27-fold more than the mean at 0.16 mg/kg), zearalenone (25-fold more than the mean at 77 µg/kg), and ergosterol (17-fold more than the mean at 13.4 mg/kg) were found in the red kernel group. Further, detailed mycotoxin and ergosterol analyses were carried out on various segments (kernel surface, conidia, bran, and flour) of the red kernels. However, the mycotoxin and ergosterol distribution profiles revealed nonsignificant correlations for these kernel segments, with the exception of deoxynivalenol and nivalenol, which were moderately correlated (r = 0.948, p = 0.035).
Concomitant species that appear at the same or very similar times in a mass-spectral analysis can clutter a spectrum because of the coexistence of many analyte-related ions (e.g., molecular ions, adducts, fragments). One method to extract ions stemming from the same origin is to exploit the chemical information encoded in the time domain, where the individual temporal appearances inside the complex structures of chronograms or chromatograms differ with respect to analytes. By grouping ions with very similar or identical time-domain structures, single-component mass spectra can be reconstructed, which are much easier to interpret and are library-searchable. While many other approaches address similar objectives through the Pearson’s correlation coefficient, we explore an alternative method based on a modified cross-correlation algorithm to compute a metric that describes the degree of similarity between features inside any two ion chronograms. Furthermore, an automatic workflow was devised to be capable of categorizing thousands of mass-spectral peaks into different groups within a few seconds. This approach was tested with direct mass-spectrometric analyses as well as with a simple, fast, and poorly resolved LC–MS analysis. Single-component mass spectra were extracted in both cases and were identified based on accurate mass and a mass-spectral library search.