Re-evaluation of effective carbon number (ECN) approach to predict response factors of 'compounds lacking authentic standards or surrogates' (CLASS) by thermal desorption analysis with GC-MS.

In our recent study, we experimentally demonstrated the feasibility of an effective carbon number (ECN) approach for the prediction of the response factor (RF) values of 'compounds lacking authentic standards or surrogates' (CLASS) using a certified 54-mix containing 38 halogenated analytes as a pseudo-unknown. Although our recent analysis performed well in terms of RF predictive power for a 25-component learning set (for both Q-MS and TOF-MS detection), large physically unrealistic negative ECN and carbon number equivalent (CNE) values were noted for TOF-MS detection, e.g., ECN (acetic acid)=-16.96. Hence, to further improve the ECN-based quantitation procedure of CLASS, we re-challenged RF vs. ECN linear regression analysis with additional descriptors (i.e., Cl, Br, CC, and a group ECN offset (Ok)) using the 1-point RF values. With an Ok, all compound classes, e.g., halo-alkanes/-alkenes and aromatics can now be fitted to yield consistently positive set of ECN values for most analytes (e.g., 3 outliers out of 29, Q-MS detection). In this way, we were able to further refine our approach so that the absolute percentage difference (PD)±standard deviation (SD) between mass detected vs. mass loaded is reduced from 39.0±34.1% (previous work) to 13.1±12.0% (this work) for 29 C1C4 halocarbons (Q-MS detector).