Determination of marker pteridines in urine by HPLC with fluorimetric detection and second-order multivariate calibration using MCR-ALS

A liquid chromatographic method has been developed, in combination with the multivariate curve resolution-alternating least squares algorithm (MCR-ALS), for the simultaneous determination of marker pteridines in urine samples. A central composite design has been applied to optimize the factors influencing the separation (buffer concentration, buffer pH, flow rate, oven temperature, mobile-phase composition). A set of 15 calibration samples were randomly prepared, in a concentration range of 0.5–10.5 ng mL⁻¹ for neopterin, biopterin, and pterin; 4.0–8.0 ng mL⁻¹ for xanthopterin; and 0.5–4.5 ng mL⁻¹ for isoxanthopterin. The validation was carried out with fortified urine samples from healthy adults. The optimized conditions were a mobile-phase composition of 10 mM citric buffer at pH 5.44 and acetonitrile (94.5/5.5, v/v), a flow rate of 1.0 mL min⁻¹, and an oven temperature of 25 °C. The detection system consisted of a fast-scanning spectrofluorimeter, which allows obtaining of second-order data matrices containing the fluorescence intensity as a function of retention time and emission wavelength. In this work, MCR-ALS was used to cope with coeluting interferences, on account of the second-order advantage inherent to this algorithm which, in addition, is able to handle data sets deviating from trilinearity, like the high-performance liquid chromatography data analyzed in the present report. The developed approach enabled us to determine five pteridines, some of them with overlapped profiles, reducing the experimental time and reagent consumption. Ratio values for pteridines/creatinine in urine, for infected children with different pathologies, are reported in this work.     

Novel combination of non-aqueous capillary electrophoresis and multivariate curve resolution-alternating least squares to determine phenolic acids in virgin olive oil

This paper presents the development of a non-aqueous capillary electrophoresis method coupled to UV detection combined with multivariate curve resolution-alternating least-squares (MCR-ALS) to carry out the resolution and quantitation of a mixture of six phenolic acids in virgin olive oil samples. p-Coumaric, caffeic, ferulic, 3,4-dihydroxyphenylacetic, vanillic and 4-hydroxyphenilacetic acids have been the analytes under study. All of them present different absorption spectra and overlapped time profiles with the olive oil matrix interferences and between them. The modeling strategy involves the building of a single MCR-ALS model composed of matrices augmented in the temporal mode, namely spectra remain invariant while time profiles may change from sample to sample. So MCR-ALS was used to cope with the coeluting interferences, on accounting the second order advantage inherent to this algorithm which, in addition, is able to handle data sets deviating from trilinearity, like the data herein analyzed. The method was firstly applied to resolve standard mixtures of the analytes randomly prepared in 1-propanol and, secondly, in real virgin olive oil samples, getting recovery values near to 100% in all cases. The importance and novelty of this methodology relies on the combination of non-aqueous capillary electrophoresis second-order data and MCR-ALS algorithm which allows performing the resolution of these compounds simplifying the previous sample pretreatment stages.     

Time shift correction in second-order liquid chromatographic data with iterative target transformation factor analysis

When the generalized rank annihilation method (GRAM) is applied to liquid chromatographic data with diode-array detection, an important problem is the time shift of the peak of the analyte in the test sample. This problem leads to erroneous predictions. This time shift can be corrected if a time window is selected so that the chromatographic profile of the analyte in the test sample is trilinear with the peak of the analyte in the calibration sample. In this paper we present a new method to determine when this condition is met. This method is based on the curve resolution with iterative target transformation factor analysis (ITTFA). The calibration and test matrices are independently decomposed into profiles and spectra, and aligned before GRAM is applied. Here we study two situations: first, when the calibration matrix has one analyte and second, when it has two analytes. When the calibration matrix has two analytes, we selectively determine the time window for the analyte to be quantified. There were considerably fewer prediction errors after correction.     

Ultrafast quantitation of six quinolones in water samples by second-order capillary electrophoresis data modeling with multivariate curve resolution–alternating least squares

This paper presents the development of a capillary electrophoresis method with diode array detector coupled to multivariate curve resolution–alternating least squares (MCR-ALS) to conduct the resolution and quantitation of a mixture of six quinolones in the presence of several unexpected components. Overlapping of time profiles between analytes and water matrix interferences were mathematically solved by data modeling with the well-known MCR-ALS algorithm. With the aim of overcoming the drawback originated by two compounds with similar spectra, a special strategy was implemented to model the complete electropherogram instead of dividing the data in the region as usually performed in previous works. The method was first applied to quantitate analytes in standard mixtures which were randomly prepared in ultrapure water. Then, tap water samples spiked with several interferences were analyzed. Recoveries between 76.7 and 125 % and limits of detection between 5 and 18 μg L^?1 were achieved.     

The new approach of standardization of capillary electrophoresis

In this paper, we develope the new standardization methods to eliminate the influence in capillary electrophoresis (CE). The markers were used to determine the basis position and then correct the data of sample by the migration time of standard sample, and make the migration time of samples consistent with the standard sample by the criterion of the marker. The problem of time transition was corrected in this way. Then according to the peak height or peak area of the marker in the sample (peak height was used here) compared with the standard sample, the sample data was zoomed appropriately. The absorbance error was made to be correct. The wavelet de-noise method was also used to make the data smooth and get a good baseline.     

The new approach of standardization of capillary electrophoresis

In this paper, we develope the new standardization methods to eliminate the influence in capillary electrophoresis (CE). The markers were used to determine the basis position and then correct the data of sample by the migration time of standard sample, and make the migration time of samples consistent with the standard sample by the criterion of the marker. The problem of time transition was corrected in this way. Then according to the peak height or peak area of the marker in the sample (peak height was used here) compared with the standard sample, the sample data was zoomed appropriately. The absorbance error was made to be correct. The wavelet de-noise method was also used to make the data smooth and get a good baseline.     

Room-temperature combinatorial screening of cyclometallated iridium(III) complexes for a step towards molecular control of colour purity

A library of emission spectra of 90 bis-cyclometallated iridium complexes has been obtained using a simple combinatorial approach performed at room temperature. Trends in emission maxima are rationalized using Hammett parameters and invoking inter ligand energy transfer (ILET) processes. The screening approach allowed us to observe trends in the broadness of emission spectra opening the way for a rational approach to the engineering of the emission colour purity at a molecular level. Finally limitations to the screening strategy are discussed using a case study that involves two different monodentate ligands.     

An approach to the spectral simulation of infrared hollow waveguide gas sensors

Optical simulations enable to model an entire chemical gas sensing platform based on hollow waveguides (HWGs) operating in the mid-infrared spectral regime using a three-dimensional representation of the sensor components and taking the spectral response to virtual analytes into account. Furthermore, a strategy for including the spectral response of dielectrically coated HWGs is demonstrated. Utilizing experimentally obtained spectroscopic data recorded at well-defined conditions, the complex refractive indices of selected target analytes (i.e., methane, butane, and isobutylene) have been derived based on a refined harmonic oscillator model. In turn, these parameters have enabled to directly assign the dielectric functions of these analytes to virtual objects representing the analyte within the modeled sensor setup. In a next step, spectroscopic sensor response functions have been simulated as absorbance spectra across selected wavelength regimes utilizing spectrally resolved ray-tracing techniques and have been compared to experimental data.     

Considerations for the automated collection of thermodynamic data in gas chromatography

Thermodynamic modeling of retention times in gas chromatography depends on the accurate estimation of thermodynamic parameters. Previous research has used manual injections of samples with coinjection of a dead time marker to obtain accurate measurements of the retention factor of analytes. Ideally this process would be automated. Herein an approach is presented by which thermodynamic parameters can be estimated both autonomously and accurately. This method also allows for a consistent estimation of thermodynamic parameters regardless of factors such as data system delays and the nature of the void time marker employed. Ignoring these factors can lead to significant errors in the prediction of retention times when using thermodynamic models.     

Achieving second order advantage with multi-way partial least squares and residual bi-linearization with total synchronous fluorescence data of monohydroxy–polycyclic aromatic hydrocarbons in urine samples

An attractive approach to handle matrix interference in samples of unknown composition is to generate second- or higher-order data formats and process them with appropriate chemometric algorithms. Several strategies exist to generate high-order data in fluorescence spectroscopy, including wavelength time matrices, excitation–emission matrices and time-resolved excitation–emission matrices. This article tackles a different aspect of generating high-order fluorescence data as it focuses on total synchronous fluorescence spectroscopy. This approach refers to recording synchronous fluorescence spectra at various wavelength offsets. Analogous to the concept of an excitation–emission data format, total synchronous data arrays fit into the category of second-order data. The main difference between them is the non-bilinear behavior of synchronous fluorescence data. Synchronous spectral profiles change with the wavelength offset used for sample excitation. The work presented here reports the first application of total synchronous fluorescence spectroscopy to the analysis of monohydroxy-polycyclic aromatic hydrocarbons in urine samples of unknown composition. Matrix interference is appropriately handled by processing the data either with unfolded-partial least squares and multi-way partial least squares, both followed by residual bi-linearization.     

Quantitative fluorescence excitation spectra of synthetic eumelanin

Previously reported excitation spectra for eumelanin are sparse and inconsistent. Moreover, these studies have failed to account for probe beam attenuation and emission reabsorption within the samples, making them qualitative at best. We report for the first time quantitative excitation spectra for synthetic eumelanin, acquired for a range of solution concentrations and emission wavelengths. Our data indicate that probe beam attenuation and emission reabsorption significantly affect the spectra even in low-concentration eumelanin solutions and that previously published data do not reflect the true excitation profile. We apply a correction procedure (previously applied to emission spectra) to account for these effects. Application of this procedure reconstructs the expected relationship of signal intensity with concentration, and the normalized spectra show a similarity in form to the absorption profiles. These spectra reveal valuable information regarding the photophysics and photochemistry of eumelanin. Most notably, an excitation peak at 365 nm (3.40 eV), whose position is independent of emission wavelength, is possibly attributable to a 5,6-dihydroxyindole-2-carboxylic acid (DHICA) component singly linked to a polymeric structure.     

Charge exchange spectra of simple ketones

Charge exchange spectra of three simple 2-ketones are reported. Comparison of charge exchange data with low voltage electron-impact spectra provides information on the nature of the electron-impact energy distribution. The existence of high energy molecular ions which do not decompose in the mass spectral time scale led us to postulate that isolated states or photon emission reactions are operative in the present system.     

Room-temperature excitation-emission phosphorescence matrices and second-order multivariate calibration for the simultaneous determination of pyrene and benzo[a]pyrene

The present article describes the simultaneous phosphorimetric determination of pyrene and benzo[a]pyrene, two highly toxic polycyclic aromatic hydrocarbons, through excitation-emission phosphorescence matrices (EEPMs) and second-order calibration. The developed approach enabled us to determine both compounds at μg L⁻¹ concentration levels without the necessity of applying separation steps, as well as significantly reducing the experimental time. An artificial neural network (ANN) approach was applied to optimize the chemical variables which have an influence on the room-temperature phosphorescence emission of the studied analytes. The present study was employed for the discussion of the scopes of the applied second-order chemometric tools: parallel factor analysis (PARAFAC) and partial least-squares with residual bilinearization (PLS/RBL). The superior capability of PLS/RBL to model the profiles of other potentially interferent polycyclic aromatic hydrocarbons (PAHs) was demonstrated. The quality of the proposed method was established with the determination of both pyrene and benzo[a]pyrene in artificial and real water samples.     

Second-order multivariate calibration procedures applied to high-performance liquid chromatography coupled to fast-scanning fluorescence detection for the determination of fluoroquinolones

Different second-order multivariate calibration algorithms, namely parallel factor analysis (PARAFAC), N-dimensional partial least-squares (N-PLS) and multivariate curve resolution-alternating least-squares (MCR-ALS) have been compared for the analysis of four fluoroquinolones in aqueous solutions, including some human urine samples (additional four fluoroquinolones were simultaneously determined by univariate calibration). Data were measured in a short time with a chromatographic system operating in the isocratic mode. The detection system consisted of a fast-scanning spectrofluorimeter, which allows one to obtain second-order data matrices containing the fluorescence intensity as a function of retention time and emission wavelength. The developed approach enabled us to determine eight analytes, some of them with overlapped profiles, without the necessity of applying an elution gradient, and thus significantly reducing both the experimental time and complexity. The study was employed for the discussion of the scopes of the applied second-order chemometric tools. The quality of the proposed technique coupled to each of the evaluated algorithms was assessed on the basis of the figures of merit for the determination of fluoroquinolones in the analyzed water and urine samples. Univariate calibration of four analytes led to limits of detection in the range 20–40 ng mL⁻¹ and root mean square errors for the validation samples in the range 30–60 ng mL⁻¹ (corresponding to relative prediction errors of 3–8%). The ranges for second-order multivariate calibration (using PARAFAC and N-PLS) of the remaining four analytes were: limit of detection, 2–8 ng mL⁻¹, root mean square errors, 3–50 ng mL⁻¹ and relative prediction errors, 1–5%.     

Semicontinuous GC analysis and receptor modelling for source apportionment of ozone precursor hydrocarbons in Bresso, Milan, 2003

The European Ozone Directive 2002/3/EC specifies the analysis of 30 individual C2-C9 hydrocarbons in urban air with the attribution of emission sources to pollution concentrations as a major objective. In the present study, we investigate an approach for source apportionment of these ozone precursor hydrocarbons in urban air based on reliable semi continuous volatile organic compound (VOC) analysis in the field and in vehicle emission laboratory combined with multivariate receptor modeling. The GC system relies on an hourly analytical cycle based on a trap sample enrichment phase followed by a dual column gas chromatographic flame ionisation detector (FID) analysis and has successfully been tested during an air monitoring campaign at an urban site (Milan, Italy, September 2003) and in the vehicle laboratory performing exhaust emission measurements while running driving cycles on a chassis dynamometer (mopeds, gasoline and diesel cars). The receptor modeling relies on two complementary principles. The chemical mass balance (CMB) modeling apportions well characterized source profiles for the 30 individual C2-C9 hydrocarbons in the Ozone Directive to the concentrations in ambient air and produces source contribution estimates (SCE) as output. The positive matrix factorization (PMF) analyses variability in the ambient air concentration data and searches for latent variables consisting of co-varying hydrocarbons and produces profiles as output, which in this study could be attributed to known emission sources. Both CMB and PMF rely on an estimated uncertainty for each input data. A new approach is presented, by which the uncertainty is allowed to float as function of the photochemical reactivity of the atmosphere and the stability of each individual compound.     

Implementation of Hadamard encoding for rapid multisample analysis in liquid chromatography

Multiplexing based on pseudo‐binary modulation sequences is known to increase the signal‐to‐noise ratio. In this work, Hadamard transform multiplexing is used in high‐performance liquid chromatography to increase the sample throughput. Using structured modulation sequences to encode and control sample injections in combination with a fitting algorithm to deconvolute the complex data allowed us to evaluate convoluted chromatograms of up to 128 samples containing three and five analytes, respectively, with good accuracy (<2% deviation). In comparison to conventional high‐performance liquid chromatography the analysis time could be reduced by 30 and 55%, respectively.     

Headspace solid phase micro extraction GC-ECD determination of volatile organic chlorinated hydrocarbons in soils

Soil samples were suspended in a suitable aqueous solvent and a solid phase microextraction (SPME) fibre was used to sample the headspace (HS) for five volatile chlorinated compounds (VOX). Their determination was made by GC-ECD technique in the splitless mode. Preliminary studies on the effects of methanol and of the sand/clay ratio on the fibre extraction were made. Four experimental factors, namely, extraction time, extraction temperature, pH and NaCl%, able to affect distribution of the analytes among the four different phases, were varied in suitable ranges. A multivariate approach applied to the face centred cube (FCC) experimental design, was used to try to optimise the overall sample response. The suitable set of factors found for the determination of chloroform, 1,2-dichloroethane, trichloroethylene, 1,1 ,2-trichloroethane, 1,1,2,2-tetrachloroethane, was a compromise among the relevant optimal factor sets of the single analytes. Detection limits of 0.003 ng, 0.022 ng, 0.001 ng, 0.015 ng and 0.002 ng were found respectively for the five cited analytes. The method was successfully used to determine the analyte contents in two real soils sampled in an industrial area.     

Methodology for porting retention prediction data from old to new columns and from conventional-scale to miniaturised ion chromatography systems

Several procedures are available for simulating and optimising separations in ion chromatography (IC), based on the application of retention models to an extensive database of analyte retention times on a wide range of columns. These procedures are subject to errors arising from batch-to-batch variability in the synthesis of stationary phases, or when using a column having a different diameter to that used when the database was acquired originally. Approaches are described in which the retention database can be recalibrated to accommodate changes in the stationary phase (ion-exchange selectivity coefficient and ion-exchange capacity) or in the column diameter which lead to changes in phase ratio. The entire database can be recalibrated for all analytes on a particular column by performing three isocratic separations with two analyte ions. The retention data so obtained are then used to derive a "porting" equation which is employed to generate the required simulated separation. Accurate prediction of retention times is demonstrated for both anions and cations on 2mm and 0.4mm diameter columns under elution conditions which consist of up to five sequential isocratic or linear gradient elution steps. The proposed approach gives average errors in retention time prediction of less than 3% and the correlation coefficient was 0.9849 between predicted and observed retention times for 344 data points comprising 33 anionic or cationic analytes, 5 column internal diameters and 8 complex elution profiles.     

Handling within run retention time shifts in two-dimensional chromatography data using shift correction and modeling

The use of PARAFAC for modeling GC × GC-TOFMS peaks is well documented. This success is due to the trilinear structure of these data under ideal, or sufficiently close to ideal, chromatographic conditions. However, using temperature programming to cope with the general elution problem, deviations from trilinearity within a run are more likely to be seen for the following three cases: (1) compounds (i.e., analytes) severely broadened on the first column hence defined by many modulation periods, (2) analytes with a very high retention factor on the second column and likely wrapped around in that dimension, or (3) with fast temperature program rates. This deviation from trilinearity is seen as retention time-shifted peak profiles in subsequent modulation periods (first column fractions). In this report, a relaxed yet powerful version of PARAFAC, known as PARAFAC2 has been applied to handle this shift within the model step by allowing generation of individual peak profiles in subsequent first column fractions. An alternative approach was also studied, utilizing a standard retention time shift correction to restore the data trilinearity structure followed by PARAFAC. These two approaches are compared when identifying and quantifying a known analyte over a large concentration series where a certain shift is simulated in the successive first column fractions. Finally, the methods are applied to real chromatographic data showing severely shifted peak profiles. The pros and cons of the presented approaches are discussed in relation to the model parameters, the signal-to-noise ratio and the degree of shift.     

A multivariate chemometric approach to fluorescence spectroscopy

A multivariate approach to the solution of problems often encountered in the spectrofluorometry of natural samples, utilising information from whole spectra is presented. (a) Piecewise direct standardisation is implemented and employed to transfer emission spectra measured with two different xenon lamps of different ages as if the spectra were measured with the same lamp. (b) It has been shown using a multivariate analysis approach that it is possible to use the raw data points instead of the smoothed data based on an algorithm included in the instrument software by the manufacturer. (c) It is documented that Raman scattering does not hamper the performance of multivariate calibration; on the contrary, in an experiment with sugar samples the concentration prediction errors become about five times lower by including the whole emission spectrum in the analysis instead of using a univariate calibration based on an emission wavelength that only reflects the analyte of interest. (d) An algorithm for variable selection is implemented and employed in the selection of optimal excitation wavelengths. Among 13 emission spectra recorded for a sugar sample at different excitation wavelengths, four of these are chosen that describe 98.51% of the total variance in the original data. (e) Finally the combination of fluorescence spectroscopy and multivariate calibration with conventional chemical data according to the near-infrared black box model is presented. The refined sugar quality parameter, the ash content and the fluorescence emission spectra are correlated by a partial least-squares regression model. Five experiments employing different monochromator slit widths and sugar concentrations are performed, and the best correlation obtained by full cross-validation of the 15 sugar samples is R = 0.98.