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Selected papers from the latest issue:nFeature selection versus feature compression in the building of calibration models from FTIR-spectrophotometry datasets
21 October 2011, 03:12:41
Publication year: 2011
Source: Talanta, Available online 20 October 2011
Alexander Vergara, Eduard Llobet
Undoubtedly, FTIR-spectrophotometry has become a standard in chemical industry for monitoring, on-the-fly, the different concentrations of reagents and by-products. However, representing chemical samples by FTIR spectra, which spectra are characterized by hundreds if not thousands of variables, conveys their own set of particular challenges because they necessitate to be analyzed in a high-dimensional feature space, where many of these features are likely to be highly correlated and many others surely affected by noise. Therefore, identifying a subset of features that preserves the classifier/regressor performance seems imperative prior any attempt to build an appropriate pattern recognition method. In this context, we investigate the benefit of utilizing two different dimensionality reduction methods, namely the minimum Redundancy-Maximum Relevance (mRMR) feature selection scheme and a new self-organized map (SOM) based feature compression, coupled to regression methods to quantitatively analyze two-component liquid samples utilizing FTIR spectrophotometry. Since these methods give us the possibility of selecting a small subset of relevant features from FTIR spectra preserving the statistical characteristics of the target variable being analyzed, we claim that expressing the FTIR spectra by these dimensionality-reduced set of features may be beneficial. We demonstrate the utility of these novel feature selection schemes in quantifying the distinct analytes within their binary mixtures utilizing a FTIR-spectrophotometer.
Source: Talanta, Available online 20 October 2011
Alexander Vergara, Eduard Llobet
Undoubtedly, FTIR-spectrophotometry has become a standard in chemical industry for monitoring, on-the-fly, the different concentrations of reagents and by-products. However, representing chemical samples by FTIR spectra, which spectra are characterized by hundreds if not thousands of variables, conveys their own set of particular challenges because they necessitate to be analyzed in a high-dimensional feature space, where many of these features are likely to be highly correlated and many others surely affected by noise. Therefore, identifying a subset of features that preserves the classifier/regressor performance seems imperative prior any attempt to build an appropriate pattern recognition method. In this context, we investigate the benefit of utilizing two different dimensionality reduction methods, namely the minimum Redundancy-Maximum Relevance (mRMR) feature selection scheme and a new self-organized map (SOM) based feature compression, coupled to regression methods to quantitatively analyze two-component liquid samples utilizing FTIR spectrophotometry. Since these methods give us the possibility of selecting a small subset of relevant features from FTIR spectra preserving the statistical characteristics of the target variable being analyzed, we claim that expressing the FTIR spectra by these dimensionality-reduced set of features may be beneficial. We demonstrate the utility of these novel feature selection schemes in quantifying the distinct analytes within their binary mixtures utilizing a FTIR-spectrophotometer.
Highlights
► We explore the benefit of dimensionality reduction for FTIR datasets. ► We tested the mRMR and SOM dimensionality reduction methods in a quantification task. ► Increasing information will increase the quality of the data if redundancy is low. ► Increasing information will reduce computational costs if redundancy is low. ► Results showed outstanding improvements in quantification performance.Thermo-optical determination of vapor pressures of TNT and RDX nanofilms
21 October 2011, 03:12:41
Publication year: 2011
Source: Talanta, Available online 20 October 2011
Walid M. Hikal, Jeffrey T. Paden, Brandon L. Weeks
Accurate thermodynamic parameters of thin films of explosives are important for understanding their behavior in the nanometer scale as well as in standoff detection. Using UV-absorbance spectroscopy technique, accurate thermodynamic parameters such as activation energies of sublimation, sublimation rates, and vapor pressures of the explosives cyclotrimethylenetrinitramine (RDX) and 2,4,6-trinitrotoluene (TNT) were determined. The values of these parameters are in excellent agreement with those reported using traditional experiments based on gravimetry. In terms of the Clapeyron equation, the dependence of RDX and TNT vapor pressures on temperature can be described by the relations LnP (Pa) = 39.6-15459/T (K) and LnP (Pa) = 34.9-12058/T (K), respectively. Heats of sublimation of RDX and TNT were also determined to be 128 kJ/mol and 100.2 kJ/mol, respectively.
Source: Talanta, Available online 20 October 2011
Walid M. Hikal, Jeffrey T. Paden, Brandon L. Weeks
Accurate thermodynamic parameters of thin films of explosives are important for understanding their behavior in the nanometer scale as well as in standoff detection. Using UV-absorbance spectroscopy technique, accurate thermodynamic parameters such as activation energies of sublimation, sublimation rates, and vapor pressures of the explosives cyclotrimethylenetrinitramine (RDX) and 2,4,6-trinitrotoluene (TNT) were determined. The values of these parameters are in excellent agreement with those reported using traditional experiments based on gravimetry. In terms of the Clapeyron equation, the dependence of RDX and TNT vapor pressures on temperature can be described by the relations LnP (Pa) = 39.6-15459/T (K) and LnP (Pa) = 34.9-12058/T (K), respectively. Heats of sublimation of RDX and TNT were also determined to be 128 kJ/mol and 100.2 kJ/mol, respectively.
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