Partial least squares and principal components analysis of wine vintage by high performance liquid chromatography with chemiluminescence detection

HPLC with acidic potassium permanganate chemiluminescence detection was employed to analyse 17 Cabernet Sauvignon wines across a range of vintages (1971–2003). Partial least squares regression analysis and principal components analysis was used in order to investigate the relationship between wine composition and vintage. Tartaric acid, vanillic acid, catechin, sinapic acid, ethyl gallate, myricetin, procyanadin B and resveratrol were found to be important components in terms of differences between the vintages.

Analysis of self-assembled monolayer interfaces by electrospray mass spectrometry : a gentle approach

A general mass spectrometry technique for the characterization of alkanethiol-modified surfaces is presented. Alkanethiol self-assembled onto a gold surface (in this case, peptides were attached to the gold surface via a thiolate bond) was reductively desorbed in 0.05 M KOH in the presence of octadecyl-derivatized silica gel. The peptide adsorbed onto the silica gel, whereupon it could be filtered, washed to remove any salts, and then eluted using a mixture of 4:1 v/v methanol/water. The eluant containing the peptide was injected into a Fourier transform ion-cyclotron resonance mass spectrometer (FTICR/MS) via electrospray ionization. The spectrum showed no fragmentation of the peptide, demonstrating the gentleness of the technique. This simple procedure is not limited to FTICR/MS and could be adapted to other mass spectrometers.

Determination of intracellular glutathione and glutathione disulfide using high performance liquid chromatography with acidic potassium permanganate chemiluminescence detection

Measurement of glutathione (GSH) and glutathione disulfide (GSSG) is a crucial tool to assess cellular redox state. Herein we report a direct approach to determine intracellular GSH based on a rapid chromatographic separation coupled with acidic potassium permanganate chemiluminescence detection, which was extended to GSSG by incorporating thiol blocking and disulfide bond reduction. Importantly, this simple procedure avoids derivatisation of GSH (thus minimising auto-oxidation) and overcomes problems encountered when deriving the concentration of GSSG from ‘total GSH’. The linear range and limit of detection for both analytes were 7.5 × 10−7 to 1 × 10−5 M, and 5 × 10−7 M, respectively. GSH and GSSG were determined in cultured muscle cells treated for 24 h with glucose oxidase (0, 15, 30, 100, 250 and 500 mU mL−1), which exposed them to a continuous source of reactive oxygen species (ROS). Both analyte concentrations were greater in myotubes treated with 100 or 250 mU mL−1 glucose oxidase (compared to untreated controls), but were significantly lower in myotubes treated with 500 mU mL−1 (p < 0.05), which was rationalised by considering measurements of H2O2 and cell viability. However, the GSH/GSSG ratio in myotubes treated with 100, 250 and 500 mU mL−1 glucose oxidase exhibited a dose-dependent decrease that reflected the increase in intracellular ROS.

Chemiluminescence detection flow cells for flow injection analysis and high-performance liquid chromatography

We have examined a range of new and previously described flow cells for chemiluminescence detection. The reactions of acidic potassium permanganate with morphine and amoxicillin were used as model systems representing the many fast chemiluminescence reactions between oxidising agents and organic analytes, and the preliminary partial reduction of the reagent was exploited to further increase the rates of reaction. The comparison was then extended to high-performance liquid chromatography separations of α- and β-adrenergic agonists, with permanganate chemiluminescence detection. Flow cells constructed by machining novel channel designs into white polymer materials (sealed with transparent films or plates) have enabled improvements in mixing efficiency and overall transmission of light to the photodetector.

Peak picking and the assessment of separation performance in two-dimensional high performance liquid chromatography

An algorithm was developed for 2DHPLC that automated the process of peak recognition, measuring their retention times, and then subsequently plotting the information in a two-dimensional retention plane. Following the recognition of peaks, the software then performed a series of statistical assessments of the separation performance, measuring for example, correlation between dimensions, peak capacity and the percentage of usage of the separation space. Peak recognition was achieved by interpreting the first and second derivatives of each respective one-dimensional chromatogram to determine the 1D retention times of each solute and then compiling these retention times for each respective fraction ‘cut’. Due to the nature of comprehensive 2DHPLC adjacent cut fractions may contain peaks common to more than one cut fraction. The algorithm determined which components were common in adjacent cuts and subsequently calculated the peak maximum profile by interpolating the space between adjacent peaks. This algorithm was applied to the analysis of a two-dimensional separation of an apple flesh extract separated in a first dimension comprising a cyano stationary phase and an aqueous/THF mobile phase as the first dimension and a second dimension comprising C18-Hydro with an aqueous/MeOH mobile phase. A total of 187 peaks were detected.

π-Selective stationary phases: (III) Influence of the propyl phenyl ligand density on the aromatic and methylene selectivity of aromatic compounds in reversed phase liquid chromatography

The retention characteristics of phenyl type stationary phases for reversed phase high performance liquid chromatography are still largely unknown. This paper explores the retention process of these types of stationary phases by examining the retention behaviour of linear PAHs and n-alkylbenzenes on a series of propyl phenyl stationary phases that have changes in their ligand density (1.23, 1.31, 1.97, 2.50 μmol m⁻²). The aromatic and methylene selectivities increased with increasing ligand density until a point where a plateau was observed, overall the propyl phenyl phases had a higher degree of aromatic selectivity than methylene selectivity indicating that these columns are suitable for separations involving aromatic compounds. Also, retention characteristics relating to the size of the solute molecule were observed to be influenced by the ligand density. It is likely that the changing retention characteristics are caused by the different topologies of the stationary phases at different ligand densities. At high ligand densities, the partition coefficient became constant.

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.