If the difference exceeded the threshold, the respective experimental value was flagged for manual inspection as potentially erroneous. In our recent work on filtering a retention index database , a similar but a bit more sophisticated, effective and robust approach was employed, where the predictions of several independent models were considered simultaneously. This approach allowed us to flag about 0.7% of the entries in the database as potentially erroneous. Notably, the maintainers of the NIST RI database have independently come to the same conclusion and removed some suspicious entries from the subsequent release of the database, further reinforcing the validity of our approach. We adopted an approach suggested by our group earlier to find potentially erroneous retention indices in the NIST RI database and applied it to METLIN SMRT. The basic methodology remained the same, while the details of the implementation changed and some improvements were introduced. The database was split into 5 parts, similar to a 5-fold cross-validation split, then each of the 5 independent predictive models was trained on four parts to predict the fifth one. The cycle was repeated 5 times resulting in 5 predicted retention times for each experimental one.