A method to optimize different objectives (total analysis time, total peak capacity, and total dilution) has been applied to comprehensive two-dimensional liquid chromatography. The approach is based on Pareto-optimality, and it yields optimal parameters (column particle sizes, column diameters, and modulation times). Losses in the peak capacities in the first dimension (due to undersampling) and in the second dimension (due to high injection volumes) have been taken into account. The first effect (detection band broadening) reduces the original peak capacity by about a half, the second effect can reduce the total peak capacity by an additional half. Thus, the total loss in peak capacity may be 75% of its theoretical value. Analytical expressions to calculate these effects under gradient and isocratic conditions are derived. Of particular interest is the study of the optimal modulation times, which corresponded to between 2 and 3 two-dimensional runs per one-dimensional peak. The effects of using gradient or isocratic elution, conventional (40 MPa) or "ultra-high" (100 MPa) pressures, and focusing between the first and second dimensions were also studied. A trade-off between total peak capacity, total analysis time, and total dilution can be established.

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A method to select the optimal window size of the Savitzky-Golay (SG) algorithm is presented. The approach is based on a comparison of the fitting residuals (i.e., the differences between the input signal and the smoothed signal) with the noise of the instrument. The window size that yields an autocorrelation of the residuals closest to the autocorrelation of the noise of the instrument is considered optimal. The method is applied in two steps. In a first step, the lag-one autocorrelation value of the noise of the instrument is computed through the study of a blank signal. In a second step, the SG algorithm is applied to "smooth" the signal using different window sizes. The method was applied to data from NMR, chromatography, and mass spectrometry and was shown to be robust. It finds the optimal window size for different signal features. This allows the method to be used in an unsupervised way, embedded in a more complex algorithm in which smoothing and/or differentiation of signals is required, provided that the lag-one autocorrelation value of the instrument noise does not change.

The challenge of fully optimizing LC×LC separations is horrendous. Yet, it is essential to address this challenge if sophisticated LC×LC instruments are to be utilized to their full potential in an efficient manner. Currently, lengthy method development is a major obstacle to the proliferation of the technique, especially in industry. A program was developed for the rigorous optimization of LC×LC separations, using gradient-elution in both dimensions. The program establishes two linear retention models (one for each dimension) based on just two LC×LC experiments. It predicts LC×LC chromatograms using a simple van-Deemter model to generalize band-broadening. Various objectives (analysis time, resolution, orthogonality) can be implemented in a Pareto-optimization framework to establish the optimal conditions. The program was successfully applied to a separation of a complex mixture of 54 aged, authentic synthetic dyestuffs, separated by ion-exchange chromatography and ion pair chromatography. The main limitation experienced was the retention-time stability in the first (ion-exchange) dimension. Using the PIOTR program LC×LC method development can be greatly accelerated, typically from a few months to a few days.

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Pancreatic cancer has the worst prognosis among all cancers. Cancer screening of body fluids may improve the survival time prognosis of patients, who are often diagnosed too late at an incurable stage. Several studies report the dysregulation of lipid metabolism in tumor cells, suggesting that changes in the blood lipidome may accompany tumor growth. Here we show that the comprehensive mass spectrometric determination of a wide range of serum lipids reveals statistically significant differences between pancreatic cancer patients and healthy controls, as visualized by multivariate data analysis. Three phases of biomarker discovery research (discovery, qualification, and verification) are applied for 830 samples in total, which shows the dysregulation of some very long chain sphingomyelins, ceramides, and (lyso)phosphatidylcholines. The sensitivity and specificity to diagnose pancreatic cancer are over 90%, which outperforms CA 19-9, especially at an early stage, and is comparable to established diagnostic imaging methods. Furthermore, selected lipid species indicate a potential as prognostic biomarkers.