In gas chromatography, analytes are separated by differences in their partition between a mobile phase and a stationary phase. Temperature-program, column dimensions, stationary and mobile phases, and flow rate are all parameters that can affect the quality of the separation in gas chromatography. To achieve a good separation (in a short amount of time) it is necessary to optimize these parameters. This can often be quite a tedious task. Using computer simulations, it is possible to both gain a better understanding of how the different parameters govern retention and separation of a given set of analytes, and to optimize the parameters within minutes. In the research presented here, this was achieved by taking a thermodynamic approach that used the two parameters ΔH (enthalpy change) and ΔS (entropy change) to predict retention times for gas chromatography. By determining these compound partition parameters, it was possible to predict retention times for analytes in temperature-programmed runs. This was achieved through the measurement of the retention times of n-alkanes, PAHs, alcohols, amines and compounds in the Grob calibration mixture in isothermal runs. The isothermally obtained partition coefficients, together with the column dimensions and specifications, were then used for computer simulation using in-house software. The two-parameter model was found to be both robust and precise and could be a useful tool for the prediction of retention times. It was shown that it is possible to calculate retention times with good precision and accuracy using this model. The relative differences between the predicted and experimental retention times for different compound groups were generally less than 1%. The scientific studies (Papers I-IV) are summarized and discussed in the main text of this thesis. / At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Submitted.

In shotgun proteomics, the liquid chromatography step is used to separate peptides in order to analyze as few as possible at the same time in the mass spectrometry step. Each peptide has a retention time, that is how long it takes to pass through the chromatography column. Prediction of the retention time can be used to gain increased identification of peptides or in order to create targeted proteomics experiments. Using machine learning methods such as support vector machines has given a high prediction accuracy, but such methods require known features that the retention time depends on. In this thesis we let a convolutional network, learn to rank the retention times instead of predicting the retention times themselves. We also tested how the prediction accuracy depends on the size of the training set. We found that pairwise ranking of peptides outperforms pointwise ranking and that adding more training data increased accuracy until the end without an increase in training time. This implies that accuracy can be further increased by training on even greater training sets.