Temperature dependence of C-terminal carboxylic group IR absorptions in the amide I' region

Compensation of temperature effects on spectra through evolutionary rank analysis

Spectra measured in various ranges of temperature are usually slightly different from each other in shape and position of the bands. Although the displayed inconsistencies are rather small, yet may lead to incorrect analysis and interpretation of the collected spectrothermal data. Thus the unspecific spectral effects induced by temperature, in particular the thermal shifts and broadening of the bands, have to be compensated. In the paper, a simple two-step method of thermospectral dataset uniformisation is presented. Thermally induced 'movement' of the bands is approximated as a linear function of the difference of temperatures, so the co-shifting of the spectra is done linearly. Thermal broadening is mimicked by convoluting the low-temperature signal (spectrum) with a Gaussian or Lorentzian spreading filter. Proper widths (values of FWHM) of these filters, used to uniform the whole dataset, are assumed to depend on the difference of temperatures, in a form of one-parameter functions. This assumption, which has been empirically confirmed, is a fundamental premise of the method of Partial Compensation for Thermal Broadening (PCTB). Optimal values of the parameters of all the functions, used to compensate both thermal shifting and broadening, are found by the Evolutionary Rank Analysis (ERA) applied on an evolving data matrix. Efficiency of the proposed approach was verified on the UV-Vis thermospectral dataset of one-component model systems. In addition, since the method is aimed at making uniformed the thermospectral datasets of multi-component systems with similar spectral properties of individual components, the two-component conformer system of t-APE (trans-1-(2'-anthryl)-2-phenylethene) has also been analyzed.

A new peak detection algorithm for MALDI mass spectrometry data based on a modified Asymmetric Pseudo-Voigt model

BACKGROUND

Mass Spectrometry (MS) is a ubiquitous analytical tool in biological research and is used to measure the mass-to-charge ratio of bio-molecules. Peak detection is the essential first step in MS data analysis. Precise estimation of peak parameters such as peak summit location and peak area are critical to identify underlying bio-molecules and to estimate their abundances accurately. We propose a new method to detect and quantify peaks in mass spectra. It uses dual-tree complex wavelet transformation along with Stein's unbiased risk estimator for spectra smoothing. Then, a new method, based on the modified Asymmetric Pseudo-Voigt (mAPV) model and hierarchical particle swarm optimization, is used for peak parameter estimation.

RESULTS

Using simulated data, we demonstrated the benefit of using the mAPV model over Gaussian, Lorentz and Bi-Gaussian functions for MS peak modelling. The proposed mAPV model achieved the best fitting accuracy for asymmetric peaks, with lower percentage errors in peak summit location estimation, which were 0.17% to 4.46% less than that of the other models. It also outperformed the other models in peak area estimation, delivering lower percentage errors, which were about 0.7% less than its closest competitor - the Bi-Gaussian model. In addition, using data generated from a MALDI-TOF computer model, we showed that the proposed overall algorithm outperformed the existing methods mainly in terms of sensitivity. It achieved a sensitivity of 85%, compared to 77% and 71% of the two benchmark algorithms, continuous wavelet transformation based method and Cromwell respectively.

CONCLUSIONS

The proposed algorithm is particularly useful for peak detection and parameter estimation in MS data with overlapping peak distributions and asymmetric peaks. The algorithm is implemented using MATLAB and the source code is freely available at http://mapv.sourceforge.net.

Fabrication and characterization of a novel nanofiltration membrane by the interfacial polymerization of 1,4-diaminocyclohexane (DCH) and trimesoyl chloride (TMC)

A novel thin-film composite polyamide membrane for nanofiltration is prepared, and the addition of sodium N-cyclohexylsulfamate is found to have a significant influence on its performance.