High-performance thin-layer chromatography chemical fingerprinting: a modern technique for comparative assessment of a multivariate chromatogram analysis of Padina boergesenii

JPC – Journal of Planar Chromatography – Modern TLC - Gujarat’s coastline holds immense storage of valuable marine macroalgae. Padina boergesenii is normally familiar as leafy...     

Supervised principal components: a new method for multivariate spectral analysis

The supervised principal components (SPC) method was proposed by Bair and Tibshirani for statistics regression problems where the number of variables greatly exceeds the number of samples. This case is extremely common in multivariate spectral analysis. The objective of this research is to apply SPC to near‐infrared and Raman spectral calibration. SPC is similar to traditional principal components analysis except that it selects the most significant part of wavelength from the high‐dimensional spectral data, which can reduce the risk of overfitting and the effect of collinearity in modeling according to a semi‐supervised strategy. In this study, four conventional regression methods, including principal component regression, partial least squares regression, ridge regression, and support vector regression, were compared with SPC. Three evaluation criteria, coefficient of determination (R²), external correlation coefficient (Q²), and root mean square error of prediction, were calculated to evaluate the performance of each algorithm on both near‐infrared and Raman datasets. The comparison results illustrated that the SPC model had a desirable ability of regression and prediction. We believe that this method might be an alternative method for multivariate spectral analysis. Copyright © 2013 John Wiley & Sons, Ltd.     

Stepwise adsorption in a mesoporous metal-organic framework: experimental and computational analysis

Stepwise adsorption in a metal-organic framework with both micro- and meso-pores is caused by adsorbates first filling the micropores, then adsorbing along the mesopore walls, and finally filling the mesopores.     

Stepwise injection analysis as a new method of flow analysis

Various versions of flow analysis techniques (continuous-flow, flow-injection, and sequential-injection analysis) and their relative advantages and disadvantages are considered. Cyclic (stepwise) injection analysis is proposed, and the general principles of this technique are described.     

Immunosorbents: A new tool for pesticide sample handling in environmental analysis

 Immunoaffinity techniques have been widely used for the determination of different analytes in the medical field. However the use of antibodies immobilized in an appropriate support material to preconcentrate pesticides from environmental samples is only recent. The production of antibodies, election of supports, antibody immobilization procedures, elution of analytes from immunosorbents and the more recent applications in the field of pesticide analysis are reviewed. The present review concludes that immunosorbents have great potential and discusses the present limitations and expected future trends. Received: 29 July 1996 / Accepted: 14 August 1996     

MALDI-TOF-MS analysis of bacterial spores: Wet heat-treatment as a new releasing technique for biomarkers and the influence of different experimental parameters and microbiological handling

Short wet heat-treatment is presented as a new technique to release high-mass biomarkers to obtain strain-specific fingerprints of intact bacterial spores by matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS). Wet heat-treatment was applied for several minutes (3-30) by two techniques using either a screw-cap tube submerged in a glycerol bath at 120 degrees C or an Eppendorff-tube submerged in a water bath at 100 degrees C. Both techniques turned out to be successful for releasing high-mass biomarkers. The influence of different experimental parameters and microbiological handling on the peak pattern of the released high-mass biomarkers was studied. While the sporulation medium, the applied washing procedure, and the choice of matrix crucially influenced the peak pattern, other parameters like storage conditions were found to be insignificant. A protocol of optimized experimental conditions for MALDI-MS of wet heat-treated spores is presented.     

Solid-phase microextraction: a promising technique for sample preparation in environmental analysis

Solid-phase microextraction (SPME) is a simple and effective adsorption and desorption technique, which eliminates the need for solvents or complicated apparatus, for concentrating volatile or nonvolatile compounds in liquid samples or headspace. SPME is compatible with analyte separation and detection by gas chromatography and high-performance liquid chromatography, and provides linear results for wide concentrations of analytes. By controlling the polarity and thickness of the coating on the fibre, maintaining consistent sampling time, and adjusting other extraction parameters, an analyst can ensure highly consistent, quantifiable results for low concentration analytes. To date, about 400 articles on SPME have been published in different fields, including environment (water, soil, air), food, natural products, pharmaceuticals, biology, toxicology, forensics and theory. As the scope of SPME grew, new improvements were made with the appearance of new coatings that allowed an increase in the specificity of this extraction technique. The key part of the SPME fibre is of course the fibre coating. At the moment, 27 variations of fibre coating and size are available. Among the newest are a fibre assembly with a dual coating of divinylbenzene and Carboxen suspended in poly(dimethylsiloxane), and a series of 23 gauge fibres intended for specific septumless injection system. The growth of SPME is also reflected in the expanding number of the accessories that make the technology even easier to use Also available is a portable field sampler which is a self-contained unit that stores the SPME fibre after sampling and during the shipment to the laboratory. Several scientific publications show the results obtained in inter-laboratory validation studies in which SPME was applied to determine the presence of different organic compounds at ppt levels, which demonstrates the reliability of this extraction technique for quantitative analysis.     

Sampling and local models for multivariate image analysis

Local models are a very important concept for microscopic and macroscopic imaging. Different methods of sub-sampling a multivariate image are described both in general and for three examples. The need for sub-sampling and its influence on multivariate image analysis and visualization are studied. Examples from MRI (256 × 256), satellite imaging (7 × 512 × 512) and biofuel studies (6 × 512 × 512) are used to illustrate some of the principles involved.     

Pulsed fast/thermal neutron analysis: a technique for explosives detection

Explosives, narcotics and other contraband material contain various chemical elements, such as hydrogen, carbon, nitrogen and oxygen, etc. in quantities and ratios that differentiate them from each other and from other innocuous substances. Neutrons and gamma-rays have the ability to penetrate through various materials to large depths. They are able to interrogate, in a non-intrusive manner, volumes ranging from suitcases to Sea-Land containers. Pulsed fast/thermal neutron analysis (PFTNA) is a neutron-based technique which utilizes the (n,n'gamma), (n,pgamma), and (n,gamma) reactions to identify and quantify a large number of elements. The elements emit characteristic gamma-rays that are the 'fingerprints' of each isotope. This technique is being employed in a variety of applications: bulk coal analysis, contraband detection and detection of explosives.     

Sample handling of clinical specimens for ultratrace element analysis

This paper brings forward some simple logistics to an improved sample handling of clinical specimens. This comprises clean room conditions, clean laboratory ware, ultra-pure reagents and good analytical practise. Sample handling procedures for blood, urine, soft tissues and pharmaceuticals will be briefly discussed.     

A new method for handling liquid samples in organic elemental analysis

Summary A method for handling liquid samples in automatic CHN analysis is described. It involves the use of an aluminum pan in which the sample is hermatically sealed by means of a sealing press. Satisfactory results have been obtained with liquids boiling between 35° and 290°.

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Zusammenfassung Zur Einwaage flüssiger Proben für die automatische CHN-Analyse werden Aluminiumschälehen verwendet, die sich mit einer Presse hermetisch verschließen lassen. Zwischen 35 und 290° siedende Flüssigkeiten lassen sich so mit gutem Erfolg analysieren.

     

Multivariate range modeling, a new technique for multivariate class modeling: the uncertainty of the estimates of sensitivity and specificity

MRM, multivariate range modeling, is based on models built as parallelepipeds in the space of the original variables and/or of discriminant variables as those of linear discriminant analysis. The ranges of these variables define the boundary of the model. The ranges are increased by a "tolerance" factor to take into account the uncertainty of their estimate. MRM is compared with UNEQ (the modeling technique based on the hypothesis of multivariate normal distribution) and with SIMCA (based on principal components) by means of the sensitivities and specificities of the models, the estimates of type I (sensitivity) and II error rates (specificity) evaluated both with the final model built with all the available objects and by means of cross validation. UNEQ and SIMCA models were obtained with the usual critical significance value of 5% and with the model forced to accept all the objects of the modeled category. The performance parameters of the class models are critically discussed focusing on their uncertainty.     

Membrane protein and peptide sample handling for MS analysis using a structured MALDI target

Different sample handling methods for hydrophobic proteins and peptides were evaluated in association with the utilization of a structured matrix-assisted laser/desorption ionization (MALDI) target for increased sensitivity. The fluorinated organic solvent hexafluoroisopropanol (HFIP) was used for the solubilization of both the full-length protein bacteriorhodopsin (BR) and a cyanogen bromide digest thereof, and compared to the performance of the non-ionic detergents octyl--d-glucopyranoside (OG), dodecyl--d-maltoside (DM), and Triton X-100. A concentrating effect was seen when using the structured MALDI plate for BR dissolved in all the different detergents, of which OG generated the best-quality spectra for the full-length integral membrane protein as well as for the hydrophobic peptides. However, the uneven analyte distribution obtained with the detergent preparations required selective and thus time-consuming acquisition of spectra. When instead HFIP was used as sample solvent, a tenfold increase in sensitivity was achieved for full-length BR. Addition of acids to the HFIP-solubilized sample, or to the MALDI matrix solution, improved the signals for a few of the peptides, while degrading the spectra of others. Consequently, the addition of acid could be used as a complementary sample preparation method for hydrophobic peptides. On-target washing to remove contaminants (e.g., salt) was performed, and a recrystallization protocol for signal improvement specifically suited for hydrophobic peptides is described. Results from digestion and solubilization in different micro centrifuge tubes were examined to determine the influence of different materials on the possible sample loss due to wall adhesion. Studies of sample solution storage times suggest immediate analysis after solubilization to obtain best results.     

Plasma analysis of celiprolol by HPTLC: a useful technique for pharmacokinetic studies

A rapid and sensitive high performance, thin-layer chromatographic (HPTLC) method has been developed for the measurement of celiprolol in human plasma and its use in pharmacokinetic studies has been evaluated. Detection and quantitation were performed without using an internal standard. A simple extraction procedure was followed for extracting celiprolol from plasma and a known amount of the extract was spotted on precoated silica gel 60 F254 plates using a Camag Linomat IV autosampler. Celiprolol was quantitated using a Camag TLC Scanner 3. The average recovery of authentic analytes (20 to 200 ng/mL) added to plasma was 72.06 +/- 2.8% and the lowest amount of celiprolol that could be detected was 10 ng/mL. The method provides a direct estimate of the amount of celiprolol present in plasma. Pharmacokinetic parameters of 2 marketed preparations have also been determined after oral administration to 12 healthy human volunteers.     

Variable-weighted least-squares support vector machine for multivariate spectral analysis

Multivariate spectral analysis has been widely applied in chemistry and other fields. Spectral data consisting of measurements at hundreds and even thousands of analytical channels can now be obtained in a few seconds. It is widely accepted that before a multivariate regression model is built, a well-performed variable selection can be helpful to improve the predictive ability of the model. In this paper, the concept of traditional wavelength variable selection has been extended and the idea of variable weighting is incorporated into least-squares support vector machine (LS-SVM). A recently proposed global optimization method, particle swarm optimization (PSO) algorithm is used to search for the weights of variables and the hyper-parameters involved in LS-SVM optimizing the training of a calibration set and the prediction of an independent validation set. All the computation process of this method is automatic. Two real data sets are investigated and the results are compared those of PLS, uninformative variable elimination-PLS (UVE-PLS) and LS-SVM models to demonstrate the advantages of the proposed method.     

Using variable combination population analysis for variable selection in multivariate calibration

Variable (wavelength or feature) selection techniques have become a critical step for the analysis of datasets with high number of variables and relatively few samples. In this study, a novel variable selection strategy, variable combination population analysis (VCPA), was proposed. This strategy consists of two crucial procedures. First, the exponentially decreasing function (EDF), which is the simple and effective principle of ‘survival of the fittest’ from Darwin’s natural evolution theory, is employed to determine the number of variables to keep and continuously shrink the variable space. Second, in each EDF run, binary matrix sampling (BMS) strategy that gives each variable the same chance to be selected and generates different variable combinations, is used to produce a population of subsets to construct a population of sub-models. Then, model population analysis (MPA) is employed to find the variable subsets with the lower root mean squares error of cross validation (RMSECV). The frequency of each variable appearing in the best 10% sub-models is computed. The higher the frequency is, the more important the variable is. The performance of the proposed procedure was investigated using three real NIR datasets. The results indicate that VCPA is a good variable selection strategy when compared with four high performing variable selection methods: genetic algorithm–partial least squares (GA–PLS), Monte Carlo uninformative variable elimination by PLS (MC-UVE-PLS), competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV). The MATLAB source code of VCPA is available for academic research on the website: http://www.mathworks.com/matlabcentral/fileexchange/authors/498750.