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The total ion current chromatogram (TICC) obtained by liquid–chromatography-mass spectrometry (LC–MS) is often extremely complex and ‘noisy’ in appearance, particularly when an electrospray ionization source is used. Accordingly, meaningful qualitative and quantitative information can be obtained in LC–MS by data mining processes. Here, one or more higher-quality mass chromatograms can be identified/extracted/isolated and combined to form a TICC, wherein much of the background mass noise is eliminated, and quantitative data for chromatographic peaks can be obtained. Pattern Recognition Entropy (PRE) is a new application of Shannon’s statistical concept of entropy. PRE is both a pattern recognition tool and a summary statistic that can be used to identify information-containing mass chromatograms, where higher quality data (higher signal-to-noise mass chromatograms) usually have lower PRE values. Reduced TICCs are obtained by first calculating the PRE values of the component mass chromatograms. A plot of PRE value vs. m / z for the mass chromatograms is then generated, and the resulting band of PRE values is fit to a piecewise spline polynomial. The distribution of the differences between the individual PRE values and the spline fit is then used to select ‘good’ mass chromatograms. For the data set considered herein, best results were obtained with a threshold of 0.5 standard deviations below the average value (value of the spline). PRE reduces the number of component mass chromatograms significantly (by an order of magnitude) and at the same time preserves most of the chemical information that is collectively in them. It can also distinguish between mass chromatograms of chemically similar species. PRE is arguably a less computationally intensive alternative to the widely used CODA algorithm for variable reduction. It produces reduced TICCs of comparable if not higher quality, and it requires only a single user input for variable selection. Reduced TICCs generated by PRE can be smoothed to further improve their signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

Display glasses meet the demands of the flat panel display industry vis-à-vis their composition, flatness, and forming processes. Here, we report the high-resolution time-of-flight secondary ion mass spectrometry (ToF-SIMS) characterization of Corning® EAGLE XG®, a widely used display glass, and subsequent chemometric analyses of these data. Samples analyzed included the as-formed glass, fracture surfaces from remelt bars, and as-formed surfaces subsequently exposed to process-relevant treatments, including strong acids and bases, two industrial detergents, and an atmospheric-pressure plasma treatment. Elemental signals in the positive ion ToF-SIMS spectra respond to surface treatments. Acidic conditions leach non-silica components from the surfaces, while basic treatments extract these species less efficiently. The detergents leave residues of Na+ and K+. The atmospheric pressure (AP) plasma treatment had little effect on the surface composition, while the melt surface differs significantly from the bulk fracture surface. Above ca. 75 m/z, the negative ion spectra are dominated by two series of homologous cluster ions with compositions of Si nO2 n+2Al− and Si mHO2 m+1H−. The presence of these clusters suggests that analogous structures exist at the near surface regions of the samples. In a series of multivariate curve resolution (MCR) analyses, two or three MCR components captured >95% of the variance in the data for these samples. [ABSTRACT FROM AUTHOR]