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Validation of the identification reliability of known and assumed UDMH transformation products using gas chromatographic retention indices and machine learningстатья
Статья опубликована в высокорейтинговом журнале
Статья опубликована в журнале из списка Web of Science и/или ScopusДата последнего поиска статьи во внешних источниках: 26 июня 2024 г.
- Авторы: Karnaeva Anastasia E., Sholokhova Anastasia Yu
- Журнал: Chemosphere
- Том: 362
- Год издания: 2024
- Издательство: Pergamon Press Ltd.
- Местоположение издательства: United Kingdom
- Номер статьи: 142679
- DOI: 10.1016/j.chemosphere.2024.142679
- Аннотация: Thirty two commercially available standards were used to determine chromatographic retention indices for three different stationary phases (non-polar, polar and mid-polar) commonly used in gas chromatography. The selected compounds were nitrogen-containing heterocycles and amides, which are referred to in the literature as unsymmetrical dimethylhydrazine (UDMH) transformation products or its assumed transformation products. UDMH is a highly toxic compound widely used in the space industry. It is a reactive substance that forms a large number of different compounds in the environment. Well-known transformation products may exceed UDMH itself in their toxicity, but most of the products are poorly investigated, while posing a huge environmental threat. Experimental retention indices for the three stationary phases, retention indices from the NIST database, and predicted retention indices are presented in this paper. It is shown that there are virtually no retention indices for UDMH transformation products in the NIST database. In addition, even among those compounds for which retention indices were known, inconsistencies were identified. Adding retention indices to the database and eliminating erroneous data would allow for more reliable identification when standards are not available. The discrepancies identified between experimental retention index values and predicted values will allow for adjustments to the machine learning models that are used for prediction. Previously proposed compounds as possible transformation products without the use of standards and NMR method were confirmed.
- Добавил в систему: Карнаева Анастасия Евгеньевна
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