Analysis of residues of prometryne and acetochlor in soil-water system by solid-phase extraction and gas chromatography/mass spectrometry

A highly sensitive and simple analytical method was developed for analyzing the binary mixed pesticides of prometryne and acetochlor in soil–water system by gas chromatography/mass spectrometry (GC/MS). The sample solution was first purified by C18 solid-phase extraction column, which was leached by acetone. The leachate was enriched to 1.0 mL by pressure blowing concentrator and then analyzed by GC/MS. The linear calibration curves were showed in the range of 1–15 μg/mL with a correlation coefficient of 0.9991. The average recoveries (n = 5) were between 95.3 and 115.7%, with relative standard deviations ranged from 1.71 and 7.95%. The limits of detection of Prometryne/Acetochlor were up to 0.06 and 0.17 μg/mL, respectively. This method provides a reliable approach to examine and evaluate the residues of prometryne and acetochlor in the soil–water system.

Mass spectrometry cancer data classification using wavelets and genetic algorithm

This paper introduces a hybrid feature extraction method applied to mass spectrometry (MS) data for cancer classification. Haar wavelets are employed to transform MS data into orthogonal wavelet coefficients. The most prominent discriminant wavelets are then selected by genetic algorithm (GA) to form feature sets. The combination of wavelets and GA yields highly distinct feature sets that serve as inputs to classification algorithms. Experimental results show the robustness and significant dominance of the wavelet-GA against competitive methods. The proposed method therefore can be applied to cancer classification models that are useful as real clinical decision support systems for medical practitioners.

A tandem liquid chromatography–mass spectrometry (LC–MS) method for profiling small molecules in complex samples

Liquid chromatography–mass spectrometry (LC–MS) methods using either aqueous normal phase (ANP) or reversed phase (RP) columns are routinely used in small molecule or metabolomic analyses. These stationary phases enable chromatographic fractionation of polar and non-polar compounds, respectively. The application of a single chromatographic stationary phase to a complex biological extract results in a significant proportion of compounds which elute in the non-retained fraction, where they are poorly detected because of a combination of ion suppression and the co-elution of isomeric compounds. Thus coverage of both polar and non-polar components of the metabolome generally involves multiple analyses of the same sample, increasing the analysis time and complexity. In this study we describe a novel tandem in-line LC–MS method, in which compounds from one injection are sequentially separated in a single run on both ANP and RP LC-columns. This method is simple, robust, and enables the use of independent gradients customized for both RP and ANP columns. The MS signal is acquired in a single chromatogram which reduces instrument time and operator and data analysis errors. This method has been used to analyze a range of biological extracts, from plant and animal tissues, human serum and urine, microbial cell and culture supernatants. Optimized sample preparation protocols are described for this method as well as a library containing the retention times and accurate masses of 127 compounds.

Mass spectrometry-based proteomic data for cancer diagnosis using interval type-2 fuzzy system

An interval type-2 fuzzy logic system is introduced for cancer diagnosis using mass spectrometry-based proteomic data. The fuzzy system is incorporated with a feature extraction procedure that combines wavelet transform and Wilcoxon ranking test. The proposed feature extraction generates feature sets that serve as inputs to the type-2 fuzzy classifier. Uncertainty, noise and outliers that are common in the proteomic data motivate the use of type-2 fuzzy system. Tabu search is applied for structure learning of the fuzzy classifier. Experiments are performed using two benchmark proteomic datasets for the prediction of ovarian and pancreatic cancer. The dominance of the suggested feature extraction as well as type-2 fuzzy classifier against their competing methods is showcased through experimental results. The proposed approach therefore is helpful to clinicians and practitioners as it can be implemented as a medical decision support system in practice.