Chemometric approach for fast analysis of prometryn in human hair by GC-MS

Independent components analysis (ICA) at the "cocktail-party" in analytical chemistry

Independent components analysis (ICA) is a probabilistic method, whose goal is to extract underlying component signals, that are maximally independent and non-Gaussian, from mixed observed signals. Since the data acquired in many applications in analytical chemistry are mixtures of component signals, such a method is of great interest. In this article recent ICA applications for quantitative and qualitative analysis in analytical chemistry are reviewed. The following experimental techniques are covered: fluorescence, UV-VIS, NMR, vibrational spectroscopies as well as chromatographic profiles. Furthermore, we reviewed ICA as a preprocessing tool as well as existing hybrid ICA-based multivariate approaches. Finally, further research directions are proposed. Our review shows that ICA is starting to play an important role in analytical chemistry, and this will definitely increase in the future.

High-Throughput and Direct Sample Screening Using a Laser Spray Ionization Miniature Mass Spectrometer

Over the past two decades, great efforts have been made toward mass spectrometer instrument miniaturization. With increasing analytical performances, miniature mass spectrometers are on the edge of being applied to more application scenarios. Besides sensitivity, mass resolution, and instrument portability, high-throughput and little or no sample preparation are also critical features in practical applications. In this study, we report the development of a miniature mass spectrometry (MS) system equipped with a 2D moving platform and a laser spray ionization (LSI) source. The method to make a patterned sample holder was also introduced and optimized for automatic high-throughput sample analyses. With the LSI source, analytes in complex matrix could be directly mass analyzed; in addition to the 2D moving platform, different samples could be analyzed in a high-throughput fashion. Results show that good linearity of quantitation could be achieved for multiple samples. Tens of nanograms of drugs, peptides, and vitamin B could be identified in diluted whole blood samples, and it takes 10 s on average to scan one sample.

Determination of Prometryn in Vetiver Grass and Water Using Gas Chromatography-Nitrogen Chemiluminescence Detection

Nitrogen chemiluminescence detector (NCD) is a nitrogen-specific detector that responds to ammonia, hydrazine, hydrogen cyanide and nitrogen oxide. A method to analyze the herbicide prometryn in plant and water samples was developed using gas chromatograph (GC) coupled with NCD. Extracts from plant (vetiver grass) and water matrices were analyzed for prometryn using an Agilent 7890A GC coupled with an Agilent 255 NCD in a split injection mode with a ratio of 2 : 1. Separation was carried out at 200°C and combustion at 1,018°C with H2 and O2 following optimized method development conditions. The percent recovery of prometryn in the two different matrices tested ranged from 81 to 107%, with relative standard deviations varying from 0.10 to 3.30% for spiked samples. Detection limit of the proposed method was 0.02 µg mL(-1) and the limit of quantification was 0.06 µg mL(-1). The proposed GC-NCD method was successfully applied to determine prometryn extracted from plant and water samples without potential interference of S-triazine, a pesticide from the same group.

Independent components analysis to increase efficiency of discriminant analysis methods (FDA and LDA): Application to NMR fingerprinting of wine

Discriminant analysis (DA) methods, such as linear discriminant analysis (LDA) or factorial discriminant analysis (FDA), are well-known chemometric approaches for solving classification problems in chemistry. In most applications, principle components analysis (PCA) is used as the first step to generate orthogonal eigenvectors and the corresponding sample scores are utilized to generate discriminant features for the discrimination. Independent components analysis (ICA) based on the minimization of mutual information can be used as an alternative to PCA as a preprocessing tool for LDA and FDA classification. To illustrate the performance of this ICA/DA methodology, four representative nuclear magnetic resonance (NMR) data sets of wine samples were used. The classification was performed regarding grape variety, year of vintage and geographical origin. The average increase for ICA/DA in comparison with PCA/DA in the percentage of correct classification varied between 6±1% and 8±2%. The maximum increase in classification efficiency of 11±2% was observed for discrimination of the year of vintage (ICA/FDA) and geographical origin (ICA/LDA). The procedure to determine the number of extracted features (PCs, ICs) for the optimum DA models was discussed. The use of independent components (ICs) instead of principle components (PCs) resulted in improved classification performance of DA methods. The ICA/LDA method is preferable to ICA/FDA for recognition tasks based on NMR spectroscopic measurements.