Automating GC Retention Index Calibration to Enable more Confident GC/MS Search

Retention Index (RI) matching is likely the most important complementary metric for identifying unknown compounds with GC/MS library search. Combining the two can dramatically improve the confidence of unknown compound identification. However, proper RI calibration of GCs can be time-consuming and error-prone, especially for sampling techniques such as SPME and LVI, which can produce significant chemical background interferences. Recent improvements in MS library RI metadata (NIST20, NIST23) have addressed gaps in experimental RI compound coverage using advanced AI modeling , allowing comprehensive RI coverage for the entire NIST library of commonly used GC column types. To take advantage of the RI metadata, one must calibrate the GC for retention index. This is commonly done using a series of n-alkanes such as C6, C7, C8… where C? is the number of carbons in the n-alkane. The series is typically set to Cn, Cn+1…Cn+m where n and m are selected to bracket the retention time (RT) range of the sample run. In the presence of large background interferences, carry over, and impurities, it may not be very clear where the n-alkane peaks are located, particularly for an automated system. Further complicating the matter, one cannot depend on using library search to locate the n-alkanes as it is well known that a library search cannot reliably differentiate the heavier alkanes. These issues complicate the GC calibration process and incorrectly locating and assigning the C? number of the n-alkanes will give incorrect results. In this presentation we will explore several different methods to reliably and automatically locate the n-alkanes based on preliminary search results combined with RI retention time prediction.