Interlaboratory development and proposition for a new quality control sample for chemical forensics analysis of chemical warfare agents

A new quality control (QC) test sample for gas chromatography–mass spectrometry (GC–MS) was created and analysed to test the comparability and repeatability of chemical forensics results within the Organisation for the Prohibition of Chemical Weapons (OPCW)–designated laboratories. The QC test sample was designed in collaboration between four laboratories and consists of 27 compounds which evaluate the performance of GC–MS instruments. This solution was analysed with GC–MS(EI) in 11 laboratories, seven of which were OPCW designated. The participating laboratories analysed the sample multiple times on consecutive days, as well as after the analysis of a set of complex matrix samples. Retention times, retention indices, peak areas, peak tailing values, signal-to-noise ratios, and isotope ratios were extracted from the GC–MS data, and statistical multivariate analyses with principal component analysis and Hotelling's T2-tests were conducted. The results from these analyses indicate that differences between GC–MS analyses by multiple laboratories were not statistically significant at the 5% level, as the approximate p-value for the null hypothesis of “no differences between the runs” was 0.69. However, similar data processing methods and data normalisation are essential for enabling the reliable comparison of chemical fingerprints between laboratories. A composition for the QC sample and criteria for acceptable GC–MS performance for chemical forensics are proposed. The composition and criteria differ from the currently used chemical weapons verification analysis QC sample by e.g. broadening the range for retention index calculations by addition of new alkane compounds, including new chemicals with concentrations close to the limit of detection (10–100 ng/ml), and including compounds with higher polarity to emulate real-life forensic samples. The proposed criteria include monitoring of retention indices, isotope ratios, peak tailing, signal-to-noise ratios, peak height, mass spectra, and sensitivity of the instrument. The new compounds and criteria will be the subject of future confidence building exercises to validate their relevancy on a large scale.</p

A semi-automatic approach to collaboratively populate an ontology for ontology-illiterate users

If we can represent the knowledge as a fully machine interpreted way, it offers many advantages to solving various kinds of problems in knowledge engineering. Most of the knowledge can be found scattered with in a domain of interest as websites, televisions, radios, publications, etc. This knowledge needs to be extracted and to be represented, so that can be used in many applications. Ontology is one of the knowledge representation techniques that is suitable for modeling domain knowledge. Knowledge evolves over time. With respect to that, we should maintain the ontology for better usage of knowledge. Ontology population is a key aspect of the ontology maintenance. However, the existing approaches for ontology populating are complex and designed for knowledge-engineering experts. Ontology Population looks for instantiating the constituent elements of an ontology. Manual population by domain experts and knowledge engineers is an expensive and time-consuming task. Thus, automatic or semi-automatic approaches are needed. The purpose of this study is to investigate in addressing the said limitation by proposing a user-friendly mechanism to incorporate evolving knowledge into ontologies, targeting ontology-illiterate end users. Maintaining ontology population and accurate inference of new knowledge are considered prime objectives of the research. A framework with flexible means of populating the ontology was developed while hiding the underlying ontology base from users. A web-based approach was adopted to support easy access and collaboratively populate. We implemented a tool based on the proposed method and checked the correctness of the method with respect to the mapping rules and all the SQL database components manually. Results proved that the proposed approach provides correct OWL-based ontology sources for the population performed through the interface. The proposed framework is designed to use any domain irrespective of the content