Second-order advantage achieved with four-way fluorescence excitation-emission-kinetic data processed by parallel factor analysis and trilinear least-squares. Determination of methotrexate and leucovorin in human urine

Four-way fluorescence data recorded by following the kinetic evolution of excitation-emission fluorescence matrices (EEMs) have been analyzed by parallel factor analysis and trilinear least-squares algorithms. These methodologies exploit the second-order advantage of the studied data, allowing analyte concentrations to be estimated even in the presence of an uncalibrated fluorescent background. They were applied to the simultaneous determination of the components of the anticancer combination of methotrexate and leucovorin in human urine samples. Both analytes were converted into highly fluorescent compounds by oxidation with potassium permanganate, and the kinetics of the reaction was continuously monitored by recording full EEM of the samples at different reaction times. A commercial fast scanning spectrofluorometer has been used for the first time to measure the four-way EEM kinetic data. The rapid scanning instrument allows the acquisition of a complete EEM in 12 s at a wavelength scanning speed of 24 000 nm/min. The emission spectra were recorded from 335 to 490 nm at 5-nm intervals, exciting from 255 to 315 nm at 6-nm intervals. Ten successive EEMs were measured at 72-s intervals, to follow the fluorescence kinetic evolution of the mixture components. Good recoveries were obtained in synthetic binary samples and also in spiked urine samples. The excitation, emission, and kinetic time profiles recovered by both chemometric techniques are in good agreement with experimental observations.     

Characterization and matching of oil samples using fluorescence spectroscopy and parallel factor analysis

A novel approach for matching oil samples by fluorescence spectroscopy combined with three-way decomposition of spectra is presented. It offers an objective fingerprinting based on the relative composition of polycyclic aromatic compounds (PACs) in oils. The method is complementary to GC-FID for initial screening of oil samples but can also be used for prescreening in the field, onboard ships, using a portable fluorescence spectrometer. Parallel factor analysis (PARAFAC) was applied to fluorescence excitation-emission matrixes (EEMs) of heavy fuel oils (HFOs), light fuel oils, lubricating oils, crude oils, unknown oils, and a sample collected in the spill area two weeks after the Baltic Carrier oil spill (Denmark, 2001). A total of 112 EEMs were decomposed into a five-factor PARAFAC model using excitation wavelengths from 245 to 400 nm and emission wavelengths from 280 to 550 nm. The PARAFAC factors were compared to EEMs of PAC standards with two to five rings, and the comparisons indicate that each of the factors can be related to a mixture of PACs with similar fluorescence characteristics: a mixture of naphthalenes and dibenzothiophenes, fluorenes, phenanthrenes, chrysenes, and five-ring PACs, respectively. Oils were grouped in score plots according to oil type. Except for HFOs and crude oils, the method easily discriminated between the four oil types. Minor overlaps of HFOs and crude oils were observed along all five PARAFAC factors, and the variability of crude oils was large along factor 2 due to a varying content of five-ring PACs. The spill sample was correctly assigned as a HFO with similar PAC pattern as oil from the cargo tank of the Baltic Carrier by comparing the correlation coefficient of scores for the oil spill sample and possible source oils (i.e., oils in the database).     

Automated resolution of nontarget analyte signals in GC x GC-TOFMS data using parallel factor analysis

We previously reported a method for the automated (objective) selection of a PARAFAC model having an appropriate number of factors for mathematical resolution of signal from a target analyte in GC x GC-TOFMS data (i.e., for an analysis in which the identity of the analyte is known a priori). While the previous target method has been successfully applied in several studies, the target method requires that the identity of the analyte be known. Also, multiple applications of the target method are required in cases where several analytes of interest are present in a single subsection of the chromatogram. Thus, having to know the analyte identity a priori restricts the applicability of the automated implementation of PARAFAC. The method presented in this report generalizes the previous method to allow analysis of one or more nontarget analyte signals in a subsection of a GC x GC-TOFMS chromatogram (i.e., for analyses when identities of analyte and interferences are not known a priori), thereby addressing and overcoming the limitations of the target method. Herein, we put the nontarget analyte PARAFAC method into theoretical context and illustrate the mechanics of the method using simulated data. We use real experimental GC x GC-TOFMS data to demonstrate the broad applicability of the method, with various analysis situations selected to illustrate challenging chemical analysis scenarios.     

An efficient method for determining the chemical rank of three-way fluorescence data arrays

An efficient method is proposed for determining the chemical rank of three-way fluorescence data arrays. At first, the original three-way fluorescence data arrays are preprocessed by Monte Carlo simulation and a new set of data arrays is generated. The new set of data arrays obtained does not only keep all the useful information, but the noises from the common background are largely removed, which results in the improvement of the signal to noise ratio of the data and is beneficial for the later frequency analysis. Then, we perform singular value decomposition over the new data and frequency analysis on the subsequent eigenvectors, with which it is very easy to distinguish the spectra from the noises. Furthermore, a new quantity frequency localization is introduced to quantify the frequency characteristics of the eigenvectors. With this quantity, we can easily and accurately select out the spectra from the mess of data. The feasibility of the method is verified by determining the chemical rank of two-component mixtures with simple calculation procedures and high efficiency. Finally, the efficiency of our method is further illustrated by comparison with the core consistency diagnostic (CORCONDIA) method in the analysis of mixtures with different concentration and different number of components.     

Nonlinear four-way kinetic-excitation-emission fluorescence data processed by a variant of parallel factor analysis and by a neural network model achieving the second-order advantage: malonaldehyde determination in olive oil samples

Four-way data were obtained by recording the kinetic evolution of excitation-emission fluorescence matrices for the product of the Hantzsch reaction between the analyte malonaldehyde and methylamine. The reaction product, 1,4-disubstituted-1,4-dihydropyridine-3,5-dicarbaldehyde, is a highly fluorescent compound. The nonlinear nature of the kinetic fluorescence data has been demonstrated, and therefore the four-way data were processed with parallel factor analysis combined with a nonlinear pseudounivariate regression, based on a quadratic polynomial fit, and also with a recently introduced neural network methodology, based on the combination of unfolded principal component analysis, residual trilinearization, and radial basis functions. The applied chemometric strategies are not only able to adequately model the nonlinear data but also to successfully determine malonaldehyde in olive oil samples. This is possible since the experimentally recorded four-way data, modeled with the above-mentioned advanced chemometric approaches, permit the achievement of the second-order advantage. This allows us to predict the analyte concentration in a complex background, in spite of the nonlinear behavior and in the presence of uncalibrated interferences. The present work is a new example of the use of higher-order data for the resolution of a complex nonlinear system, successfully employed in the context of food chemical analysis.     

Determination of human serum alcohol dehydrogenase using isozyme-specific fluorescent substrates.

Both class I and class II alcohol dehydrogenase (ADH) activities are present in human serum. The contribution of each class can be measured using two class-specific, fluorogenic substrates, 4-methoxy-1-naphthaldehyde and 6-methoxy-2-naphthaldehyde. The former is highly selective for class I isozymes, especially those containing alpha or gamma subunits, whereas class II (pi) ADH preferentially reduces the latter. Selective inhibition of class I ADH by 4-methylpyrazole further increases the specificity. Specificity, accuracy, and precision of the assay for serum measurements have been determined. The activity of class I ADH in normal human serum is below the limit of detection of this method, i.e., less than 1.0 nM/min. The activity of class II ADH in normal individuals is 15 +/- 5 nM/min. In some patients values as high as 2100 nM/min are observed for class I, but in all instances, the amount of class II found was higher than that of class I ADH.     

Metabolic profiling based on two-dimensional J-resolved 1H NMR data and parallel factor analysis

Metabolic profiling of natural products is used to map correlated concentration variances of known and unknown secondary metabolites in extracts. NMR-spectroscopy is in this respect regarded as a convenient and reproducible technique with the ability to detect a wide range of small organic compounds. Two-dimensional J-resolved NMR-spectra are used in this context to resolve overlapping signals by separating the effect of J-coupling from the effect of chemical shifts. Often one-dimensional projections of these data are used as input for standard multivariate statistical methods, and only the intensity variances along the chemical shift axis are taken into account. Here, we describe the use of parallel factor analysis (PARAFAC) as a tool to preprocess a set of two-dimensional J-resolved spectra with the aim of keeping the J-coupling information intact. PARAFAC is a mathematical decomposition method that fits three-way experimental data to a model whose parameters in this case reflect concentrations and individual component spectra along the chemical shift axis and corresponding profiles along the J-coupling axis. A set of saffron samples, directly extracted with methanol-d(4), were used as a model system to evaluate the feasibility and merits of the method. To successfully use PARAFAC, the two-dimensional spectra (n = 96) had to be aligned and processed in narrow windows (0.04 ppm wide) along the chemical shift axis. Selection of windows and number of components for each PARAFAC-model was done automatically by evaluating amount of explained variance and core consistency values. Score plots showing the distribution of objects in relation to each other, and loading plots in the form of two-dimensional pseudospectra with the same appearance as the original J-resolved spectra but with positive and negative contributions are presented. Loadings are interpreted not only in terms of signals with different chemical shifts but also the associated J-coupling profiles.     

A test field for the second-order advantage in bilinear least-squares and parallel factor analyses: fluorescence determination of ciprofloxacin in human urine

The analytical performances of two algorithms, the recently introduced bilinear least-squares (BLLS) and the popular parallel factor analysis (PARAFAC), are compared as regards second-order fluorescence data recorded for the determination of the fluoroquinolone antibiotic ciprofloxacin in human urine samples. The applied chemometric methodologies employ different strategies for exploiting the so-called second-order advantage, which allows one to obtain individual concentrations of calibrated analytes in the presence of any number of uncalibrated (urine) components. Analysis of a spiked urine test set (in the analyte concentration range 0-200 mg L(-1)) showed that BLLS provides results of slightly better quality than PARAFAC. Satisfactory results have been obtained on comparing the concentrations predicted for a series of real urine samples with those furnished by liquid chromatography. The limit of detection of the fluorescence-based methods is approximately 5 mg L(-1).     

Determination of tin in human blood serum by radiochemical neutron activation analysis

A method was developed for the determination of tin in human serum by radiochemical neutron activation analysis, using the long-lived radioisotope Sn(T1/2 = 115.09 days). This radioisotope decays to a daughter isotope 113mIn, the most suitable nuclide for counting (T1/2 = 1.658 h, gamma-ray of 391.7 keV). Experience showed that, with the exception of the serum samples with the lowest tin levels, in the experimental conditions of the present study tin could mostly also be determined by using its radioisotope 117mSn(T1/2 = 13.61 days, gamma-ray of 158.5 keV). Samples were collected and prepared by using the procedure elaborated by the authors, which proved its effectiveness in preventing significant sample contamination on several occasions. Because samples had to be irradiated at 10(14) n.cm-2.s-1, dry ashing was necessary. After irradiation, tin was separated by solvent extraction of tin(IV) iodide from a sulfuric acid-ammonium iodide solution with toluene. The dry ashing and solvent extraction steps were exhaustively tested by means of radioactive tracer experiments whereas the accuracy and precision of the analytical method were thoroughly checked by analyzing biological reference materials (Bowen's kale powder, the NBS' bovine liver, the NBS' nonfat milk powder, and the "second-generation" biological reference material--freeze-dried human serum--for trace element determinations, developed by the authors).(ABSTRACT TRUNCATED AT 250 WORDS)     

On-line parallel factor analysis. A step forward in the monitoring of bioprocesses in real time

There is a widely growing interest to obtain robust and rapid methodologies capable of monitoring bioprocesses in real time. Different analytical methods have been adapted to measure cell density evolution throughout a culture, and fluorescence spectroscopy is becoming promising technique due to its sensitivity, selectivity towards important chemical analytes and its easy implementation as a non-invasive procedure.

This work is focused on showing the advantages of coupling the trilinear algorithm Parallel Factor Analysis (PARAFAC) to the Multivariate Statistical Process Control (MSPC) as a monitoring and real-time control tool for bioprocesses. In this context, both induced (growing on methanol) and non induced (growing on glycerol) Pichia pastoris cultures were monitored by multiwavelength-fluorescence. In the first one methanol was used as substrate; whereas glycerol was used in the second one. Taking advantages of the mathematical properties of PARAFAC, batches of a bioprocess measured under normal operating conditions (NOC) were used to develop a calibration models. Residuals of the model in combination with MSPC were used to establish two control limits. The control limits were used for new batches in real time.

Fluorescence spectroscopy combined with PARAFAC and MSPC is a feasible approach for controlling and performing fault diagnosis of bioprocesses offering the opportunity of performing real-time process surveillance based on relevant quality measurements.     

Determination of bone mineral volume fraction using impedance analysis and Bruggeman model

Measurements by impedance spectroscopy and Bruggeman effective medium approximation model were employed in order to determine the mineral volume fraction of dry bone. This approach assumes that two or more phases are present into the composite: the matrix (environment) and the other ones are inclusion phases. A fragment of femur diaphysis dense bone from a young pig was investigated in its dehydrated state. Measuring the dielectric properties of bone and its main components (hydroxyapatite and collagen) and using the Bruggeman approach, the mineral volume filling factor was determined. The computed volume fraction of the mineral volume fraction was confirmed by a histogram test analysis based on the SEM microstructures. In spite of its simplicity, the method provides a good approximation for the bone mineral volume fraction. The method which uses impedance spectroscopy and EMA modeling can be further developed by considering the conductive components of the bone tissue as a non-invasive in situ impedance technique for bone composition evaluation and monitoring.     

The dynamic water vapour sorption behaviour of natural fibres and kinetic analysis using the parallel exponential kinetics model

Hygroscopic behaviour is an inherent characteristic of natural fibres which can influence their applications as textile fabrics and composite reinforcements. In this study, the water vapour sorption kinetic properties of cotton, filter paper, flax, hemp, jute, and sisal fibres were determined using a dynamic vapour sorption apparatus and the results were analyzed by use of a parallel exponential kinetics (PEK) model. With all of the fibres tested, the magnitude of the sorption hysteresis observed varied, but it was always greatest at the higher end of the hygroscopic range. Flax and sisal fibres displayed the lowest and highest total hysteresis, respectively. The PEK model, which is comprised of fast and slow sorption components, exhibited hysteresis in terms of mass for both processes between the adsorption and desorption isotherm. The hysteresis derived from the slow sorption process was less than from the fast process for all tested fibres. The fast processes for cotton and filter paper dominated the isotherm process; however, the hemp and sisal fibres displayed a dominant slow process in the isotherm run. The characteristic time for the fast sorption process did not vary between adsorption and desorption, except at the top end of the hygroscopic range. The characteristic time for the slow process was invariably larger for the desorption process. The physical interpretation of the PEK model is discussed.     

Determination of the stress intensity factor for parallel and symmetric edge cracks under pure bending

An experimental and semi-theoretical investigation concerning thin plates having “Parallel and Symmetric” (P.A.S.)² edge cracks and subjected to pure bending will be discussed in this paper. Experiments were performed extensively on plates with P.A.S. equal edge cracks in order to study the effect of the variation of the length of the cracks and crack spacing seperately.Observations show that when crack spacing becomes larger than crack length, the diameter of the caustic for the equal P.A.S. edge cracks approaches the diameter of the caustic of the double edge cracks. Based on this fact, the equation that expresses the semi-theoretical and experimental stress intensity factor for the double edge cracks was first modified and then utilized in order to express the strese intensity factor of P.A.S. edge cracks. Moreover, an alternative formula for the stress intensity factor was established which is based on a cubic interpolating polynomial. For large crack spacing the cubic interpolating polynomial converges to unity and as a result, the diameter of the caustic and the stress intensity factor approach those of a double edge crack.For the case of unequal P.A.S. edge cracks, a crack closure phenonmenon under the pure bending condition was investigated. Experimental measurements showed that the relative position of the shorter crack with respect to the longer cracks for which the crack closure phenomenon happens is related not only to the crack spacing of the parallel edge cracks in the tension region, but also strongly related to the variation of the existing symmetric edge crack in the compression region.     

A quantitative analysis of determinants of non-citation using a panel data model

The term “non-citation factor” refers to the percentage of never-cited papers in a citation time window, a common phenomenon in the science world. Some scholars have qualitatively explored the reasons for not citing a publication, and quantitatively analyzed the mathematical functional relations between the “non-citation factor” and “impact factor of a journal.” This study simultaneously considers the mutual relations and closeness degree between the “non-citation factor” and different influencing factors from a novel perspective—that of using a more structuralized panel data model. The analysis revealed that the determinants, including “impact factor of journal,” “age of journal,” “average number of references per paper in journal,” and “issues of journal,” exerted an extremely small but positive influence (<?0.025) on the decline of “percentage of never-cited papers in the citation time window of publication year or 3 years.” That means the improvement of these determinants can decrease the percentage of never-cited papers. The “impact factor of the journal” always had the biggest positive influence, while the “average number of references per paper in journal” always had the smallest positive influence. In wider citation time windows of 3 or 6 years, two determinants—“number of publications in journal” and “amount of interdisciplinarity in journal”—began to exert a negative effect with a positive correlation coefficient on the decline of the “non-citation factor.” That means the improvement of these two determinants cannot decrease the value of the “non-citation factor,” even though they can increase its value. It is worth noting that the “impact factor of the journal” had a positive influence on the decline of the percentages of never-cited papers in the citation time window of publication year or 3 years, and began to play a negative role in the decline of percentage of never-cited papers in the citation time window of 6 years. Finally, three variables—“average number of authors per paper in journal,” “average number of references per paper in journal,” and “issues of journal”—no longer exerted an influence on the decline of percentages of never-cited papers in the citation time window of 6 years, while “age of journal” and “average number of pages per paper in journal” still made a positive contribution. Our findings could help research institutions, researchers, editors, and publishers understand the positively or negatively influential factors that lead to non-citation, thus improving the chance of papers being cited and having some academic influence.     

Four-way data coupled to parallel factor model applied to environmental analysis: determination of 2,3,7,8-tetrachloro-dibenzo-para-dioxin in highly contaminated waters by solid-liquid extraction laser-excited time-resolved Shpol'skii spectroscopy

This article reports the first application of parallel factor analysis to high-order instrumental data generated from Shpol'skii matrixes at liquid helium temperature. Third-order data arrays-consisting of excitation modulated wavelength time matrixes-are collected with the aid of a cryogenic fiber-optic probe, a tunable dye laser, and a multichannel system for phosphorescence detection. The multidimensional data formats are applied to the analysis of 2,3,7,8-tetrachloro-dibenzo-para-dioxin in water samples. The experimental procedure is rapid and environmentally friendly. Complete sample analysis is accomplished in less than 15 min with only 100 muL of organic solvent (n-heptane). The feasibility to directly determine parts-per-trillion concentration levels of the target compound is demonstrated with heavily contaminated samples of unknown composition. The limits of detection, estimated from calibrations based on the univariate method and parallel factor analysis, are 0.060 and 0.092 ng.mL(-1), respectively; both referred to a 100 mL water sample.     

Determination of uranium and/or plutonium using X-ray fluorescence analysis excited by 192Ir sealed sources

The determination of uranium and/or plutonium at various stages of the nuclear fuel cycle (ores, reprocessing solutions, mixed oxyde fuel pellets) has been carried out using specially designed equipment. The matrix effects (light elements, particles size) are minimized by detecting only the high energy X_K spectrum lines which are able to pass through the walls of specimens containers. The observed sample volume is well defined by a narrow collimation of the incident and emergent beams. The effects of the relative position and of the self radioactivity of the sample are also reduced. The use of a semi-conductor detector (HPGe) associated to a high speed multichannel analyzer and an on-line microcomputer allows sensitivities of 20 mg/l U for low concentrations solutions or 50 ppm for ore containing less than 2000 ppm and 1% (relative) for higher concentrations. Such analyzers are in working order for controlling in glove box uranium and plutonium nitric solutions or sintered (U, Pu) oxyde pellets.     

Effects of mild heating and acidification on the molecular structure of milk components as investigated by synchronous front-face fluorescence spectroscopy coupled with parallel factor analysis

This paper reports the potential of synchronous front-face fluorescence spectroscopy in the characterization at the molecular level of milk changes during mild heating from 4 to 50 degrees C and acidification in the pH range of 6.8 to 5.1. Synchronous fluorescence spectra were collected in the 250-550 nm excitation wavelength range using offsets of 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, and 240 nm between excitation and emission monochromators. The potential of parallel factor (PARAFAC) analysis in the decomposition of the whole synchronous fluorescence data set into the contribution of each of the fluorescent compounds present in milk has been investigated for heating and acidification data sets. Models were fitted from 1 to 7 components. Considering the core consistency values, PARAFAC models with three components have been considered. The first three components explained 94.43% and 94.13% of the total variance for heating and acidification data sets, respectively. The loading profiles of the first and second components derived from PARAFAC analysis performed on heating and acidification data sets corresponded quite well with the characteristics of tryptophan and vitamin A fluorescence spectra, respectively. The third component corresponded to the riboflavin fluorescence spectrum. Considering the heating experiment, the profile of the concentration mode for the second component showed large variations according to the temperature, which were assigned to the melting of triglycerides between 4 and 50 degrees C. For the acidification experiment, drastic changes in the concentration modes of the three components were observed for pH below 5.6, in agreement with structural changes in casein micelles.     

Determination of optical parameters of human breast tissue from spatially resolved fluorescence: a diffusion theory model

We report the measurement of optical transport parameters of pathologically characterized malignant tissues, normal tissues, and different types of benign tumors of the human breast in the visible wavelength region. A spatially resolved steady-state diffuse fluorescence reflectance technique was used to estimate the values for the reduced-scattering coefficient (mu(s)') and the absorption coefficient (mu(a)) of human breast tissues at three wavelengths (530, 550, and 590 nm). Different breast tissues could be well differentiated from one another, and different benign tumors could also be distinguished by their measured transport parameters. A diffusion theory model was developed to describe fluorescence light energy distribution, especially its spatial variation in a turbid and multiply scattering medium such as human tissue. The validity of the model was checked with a Monte Carlo simulation and also with different tissue phantoms prepared with polystyrene microspheres as scatterers, riboflavin as fluorophores, and methylene blue as absorbers.