Measurement uncertainty interval in case of a known relationship between precision and mean [version 1; peer review: 2 approved, 1 approved with reservations]

Background: Measurement uncertainty is typically expressed in terms of a symmetric interval y±U, where y denotes the measurement result and U the expanded uncertainty. However, in the case of heteroscedasticity, symmetric uncertainty intervals can be misleading. In this paper, a different approach for the calculation of uncertainty intervals is introduced. Methods: This approach is applicable when a validation study has been conducted with samples with known concentrations. In a first step, test results are obtained at the different known concentration levels. Then, on the basis of precision estimates, a prediction range is calculated. The measurement uncertainty for a given test result can then be obtained by projecting the intersection of the test result with the limits of the prediction range back onto the axis of the known values, now interpreted as representing the measurand. Results: It will be shown how, under certain circumstances, asymmetric uncertainty intervals arise quite naturally and lead to more reliable uncertainty intervals. Conclusions:  This article establishes a conceptual framework in which measurement uncertainty can be derived from precision whenever the relationship between the latter and concentration has been characterized. This approach is applicable for different types of distributions. Closed expressions for the limits of the uncertainty interval are provided for the simple case of normally distributed test results and constant relative standard deviation

Methylbis(2,6-dichIorthiophenoIato)bismut(III)-Synthese, fungizide und bakterizide Aktivität

The methylbis(thiophenolato)bismuth(III) derivative CH3Bi(SCAH3Cl2-2.6)2 was prepared by reaction of CH3BiBr2 with equivalent amounts of the appropriate lithium thiolate. The new Bi organyl was characterized by elemental analysis, 1H NMR. IR . and mass spectroscopy. The biological activity against bacteria, yeasts, and moulds was investigated

Towards the Development of (Certified) Reference Materials for Effective Veterinary Drug Residue Testing in Food-producing Animals

Reliable analysis of veterinary drug residues in animal-derived foodstuffs represents an important measure to ensure consumer protection. European Legislation addresses this issue, for instance by defining maximum residue level, specifying sampling and monitoring plans, and stipulating performance and validation criteria for analytical methods. Certified reference materials (CRMs) constitute an important tool for method validation and method performance verification. This article addresses the current status of legislation, describes characteristics of analytical methods in veterinary drug residue testing and then focuses on the challenges and considerations towards the development of new (certified) reference materials in this fieldJRC.D.2-Reference material

European analytical criteria: past, present, and future

In this paper, the past, present, and (possible) future of the European analytical criteria for residues are described. The elaboration of the revision of Commission Decision 93/256/EC was a long process starting in 1996 and ending with the formation of a European Commission (EC) working group in 1998. This working group took account of developments in scientific and technical knowledge at that time and produced a draft version of the revision within 6 months. The revision, finally published in 2002 (2002/657/EC), includes new ideas on the identification of analytes and the criteria for performance assessment as well as validation procedures. Currently (2009), the evolution in analytical equipment and progress in scientific research, accompanied by recent European regulatory changes, demands an update or revision of the 2002/657/EC

Essential terminology and considerations for validation of non-targeted methods

Through their suggestive name, non-targeted methods (NTMs) do not aim at a predefined “needle in the haystack.” Instead, they exploit all the constituents of the haystack. This new type of analytical method is increasingly finding applications in food and feed testing. However, the concepts, terms, and considerations related to this burgeoning field of analytical testing need to be propagated for the benefit of those associated with academic research, commercial development, or official control. This paper addresses frequently asked questions regarding terminology in connection with NTMs. The widespread development and adoption of these methods also necessitate the need to develop innovative approaches for NTM validation, i.e., evaluating the performance characteristics of a method to determine if it is fit-for-purpose. This work aims to provide a roadmap for approaching NTM validation. In doing so, the paper deliberates on the different considerations that influence the approach to validation and provides suggestions therefor

Development of Non-Targeted Mass Spectrometry Method for Distinguishing Spelt and Wheat

Food fraud, even when not in the news, is ubiquitous and demands the development of innovative strategies to combat it. A new non-targeted method (NTM) for distinguishing spelt and wheat is described, which aids in food fraud detection and authenticity testing. A highly resolved fingerprint in the form of spectra is obtained for several cultivars of spelt and wheat using liquid chromatography coupled high-resolution mass spectrometry (LC-HRMS). Convolutional neural network (CNN) models are built using a nested cross validation (NCV) approach by appropriately training them using a calibration set comprising duplicate measurements of eleven cultivars of wheat and spelt, each. The results reveal that the CNNs automatically learn patterns and representations to best discriminate tested samples into spelt or wheat. This is further investigated using an external validation set comprising artificially mixed spectra, samples for processed goods (spelt bread and flour), eleven untypical spelt, and six old wheat cultivars. These cultivars were not part of model building. We introduce a metric called the D score to quantitatively evaluate and compare the classification decisions. Our results demonstrate that NTMs based on NCV and CNNs trained using appropriately chosen spectral data can be reliable enough to be used on a wider range of cultivars and their mixes