Majority of studies identify UDMH transformation products by a library search of the MS-spectra database. At the same time, a database search in around 20% of times shows an incorrect result even if the mass spectrum is available in the database. Nevertheless, the structure elucidation of isomeric compounds based only on their fragmentation pattern, may sometimes not be feasible, since they produce common fragments and the reference standards are not always available . At the moment many substances known as possible UDMH transformation products are not available in any databases . Only several works use standards and nuclear magnetic resonance (NMR) . At the same time, even the use of highresolution MS does not allow to assume a possible structure, since tens of thousands of possible structures with different properties can correspond to the brutto-formula. GC retention index (RI) can be used as an additional factor to confirm or reject candidates during GC-MS identification . However, reference RIs are available for less than half of the compounds in the NIST database, and reference RIs are not available for most UDMH transformation products . This problem can be partially solved by predicting RI using machine learning . During the last decade prediction of RIs with machine learning has been applied more and more widely to the problems of non-targeted analysis in environmental chemistry The work of our team demonstrated the successful use of a combination of high-performance liquid chromatography-high resolution mass spectrometry (HPLC-HRMS) and GC-MS with artificial intelligence techniques to cross-check the proposed structures of new UDMH transformation products . However, here we would like to highlight a very important nuance that machine learning is conducted on known databases, including NIST, and the accuracy of prediction directly depends on the correctness of the available values. Khrisanfov et al. developed a novel approach to find potentially erroneous entries in a large-scale database with mostly unique entries . The authors also raise concerns that some RI values in the database may be erroneous. RI filtering in the NIST database is an important task because about 80% of the data have only one RI value per stationary phase (SP) . Identifying incorrect entries will improve the prediction accuracy of machine learning models.