Easy and accurate calculation of programmed temperature gas chromatographic retention times by back-calculation of temperature and hold-up time profiles

Linear retention indices are commonly used to identify compounds in programmed-temperature gas chromatography (GC), but they are unreliable unless the original experimental conditions used to measure them are stringently reproduced. However, differences in many experimental conditions may be properly taken into account by calculating programmed-temperature retention times of compounds from their measured isothermal retention vs. temperature relationships. We call this approach “retention projection”. Until now, retention projection has been impractical because it required very precise, meticulous measurement of the temperature vs. time and hold-up time vs. temperature profiles actually produced by a specific GC instrument to be accurate. Here we present a new, easy-to-use methodology to precisely measure those profiles: we spike a sample with 25 n-alkanes and use their measured, programmed-temperature retention times to precisely back-calculate what the instrument profiles must have been. Then, when we use those back-calculated profiles to project retention times of 63 chemically diverse compounds, we found that the projections are extremely accurate (e.g. to ±0.9 s in a 40 min ramp). They remained accurate with different temperature programs, GC instruments, inlet pressures, flow rates, and with columns taken from different batches of stationary phase while the accuracy of retention indices became worse the more the experimental conditions were changed from the original ones used to measure them. We also developed new, open-source software (http://www.retentionprediction.org/gc) to demonstrate the system.

► Temperature program non-idealities are back-calculated from alkane retention times.
► Hold-up time non-idealities are back-calculated from alkane retention times.
► This new approach provides an easy way to measure the non-idealities.
► By accounting for non-idealities this way, retention predictions are very accurate.
► This retention prediction approach is easy and more general than existing approaches.