π-Selective stationary phases: (I) Influence of the spacer chain length of phenyl type phases on the aromatic and methylene selectivity of aromatic compounds in reversed phase high performance liquid chromatography

Phenyl type stationary phases of increasing spacer chain length (phenyl, methyl phenyl, ethyl phenyl, propyl phenyl and butyl phenyl, with 0–4 carbon atoms in the spacer chain, respectively) were synthesised and packed in house to determine the impact that the spacer chain length has on the retention process. Two trends in the aromatic selectivity, q_aromatic, were observed, depending on whether the number of carbon atoms in the spacer chain is even or odd. Linear log k′ vs ϕ plots were obtained for each stationary phase and the S coefficient was determined from the gradient of these plots. For the phenyl type phases, the S vs n_c plots of the retention factors of linear polycyclic aromatic hydrocarbons vs the number of rings exhibit a distinct discontinuity that between 3 and 4 rings, which increases with increasing spacer chain length for even phases but decreases for odd phases. Accordingly, we suggest that the retention factors depend differently on the number of carbon atoms in the spacer chain depending on whether this number is even or odd and that this effect is caused by different orientations of the aromatic ring relative to the silica surface.

Dimensionality reduction of protein mass spectrometry data using random projection

Protein mass spectrometry (MS) pattern recognition has recently emerged as a new method for cancer diagnosis. Unfortunately, classification performance may degrade owing to the enormously high dimensionality of the data. This paper investigates the use of Random Projection in protein MS data dimensionality reduction. The effectiveness of Random Projection (RP) is analyzed and compared against Principal Component Analysis (PCA) by using three classification algorithms, namely Support Vector Machine, Feed-forward Neural Networks and K-Nearest Neighbour. Three real-world cancer data sets are employed to evaluate the performances of RP and PCA. Through the investigations, RP method demonstrated better or at least comparable classification performance as PCA if the dimensionality of the projection matrix is sufficiently large. This paper also explores the use of RP as a pre-processing step prior to PCA. The results show that without sacrificing classification accuracy, performing RP prior to PCA significantly improves the computational time.