Standard addition analysis of fluoroquinolones in human serum in the presence of the interferent salicylate using lanthanide-sensitized excitation-time decay luminescence data and multivariate curve resolution

Three fluoroquinolone antibiotics (ciprofloxacin, norfloxacin and danofloxacin) have been determined in human serum in the presence of the potential interferent salicylate, by processing lanthanide-sensitized excitation-time decay matrix data for their terbium (III) complexes. The algorithm employed, multivariate curve resolution-alternating least-squares, is one of the few methodologies which permit the achievement of the second-order advantage in the presence of a high degree of overlapping between the time decay profiles for the analyte and the interferent complexes. Furthermore, the presence of analyte-background interactions makes it necessary to employ the standard addition method for successful quantitation. Both simulations and experiments showed that the modified standard addition method was suitable for this purpose, in which the test data matrix was subtracted from the standard addition matrices, and quantitation proceeded using classical external calibration procedure. The analyte concentration ranges were all within the therapeutic range, i.e., 0-6 mg L⁻¹ in serum, with final concentrations in the measuring cell in the order of 0.2 mg L⁻¹.     

Evaluation of partial least-squares with second-order advantage for the multi-way spectroscopic analysis of complex biological samples in the presence of analyte-background interactions

The combination of unfolded partial least-squares (U-PLS) with residual bilinearization (RBL) has not been properly exploited to process experimental second-order spectroscopic information, although it is able to achieve the important second-order advantage. Among other desirable properties, the technique can handle incomplete calibration information, i.e., when only certain analyte concentrations are known in the training set. It can also cope with analyte spectral changes from sample to sample, due to its latent variable structure. In this work, U-PLS/RBL has been successfully applied to experimental fluorescence excitation-emission matrix data aimed at the quantitation of analytes in complex samples: these were the antibiotic tetracycline and the anti-inflammatory salicylate, in both cases in the presence of human serum, where significant analyte-background interactions occur. The interactions of the analyte with the serum proteins modify their spectral fluorescence properties, making it necessary to employ training sets of samples where the biological background is present, possibly causing analyte spectral changes from sample to sample. The predictive ability of the studied model has been compared with that of parallel factor analysis (PARAFAC), as regards test samples containing different sera, and also other pharmaceuticals which could act as potential interferents.     

The application to wastewaters of chemometric approaches to handling problems of highly complex matrices

This overview covers current chemometric methodologies using second-order advantage to solve problems of analyzing highly complex matrices. Among the existing algorithms, it focuses on those most frequently used (e.g., the standard for second-order approaches to data analysis, PARAFAC (parallel factor analysis), and MCR-ALS (multivariate curve resolution alternating least squares), as well as the most recently implemented BLLS (bilinear least-squares), and U-PLS/RBL (unfolded partial least squares/residual bilinearization)). All of these are based on linear models. The overview also covers ANN/RBL (artificial neural networks followed by residual bilinearization), which achieves the second-order advantage in systems involving non-linear behavior. In addition, the overview deals with the drawbacks of these approaches, as well as other drawbacks that are inherent in the analytical techniques to question.     

Modeling nonbilinear total synchronous fluorescence data matrices with a novel adapted partial least squares method

A new residual modeling algorithm for nonbilinear data is presented, namely unfolded partial least squares with interference modeling of non bilinear data by multivariate curve resolution by alternating least squares (U-PLS/IMNB/MCR-ALS). Nonbilinearity represents a challenging data structure problem to achieve analyte quantitation from second-order data in the presence of uncalibrated components. Total synchronous fluorescence spectroscopy (TSFS) generates matrices which constitute a typical example of this kind of data. Although the nonbilinear profile of the interferent can be achieved by modeling TSFS data with unfolded partial least squares with residual bilinearization (U-PLS/RBL), an extremely large number of RBL factors has to be considered. Simulated data show that the new model can conveniently handle the studied analytical problem with better performance than PARAFAC, U-PLS/RBL and MCR-ALS, the latter modeling the unfolded data. Besides, one example involving TSFS real matrices illustrates the ability of the new method to handle experimental data, which consists in the determination of ciprofloxacin in the presence of norfloxacin as interferent in water samples.     

Determination of atenolol in human urine by emission-excitation fluorescence matrices and unfolded partial least-squares with residual bilinearization

A second-order multivariate calibration method based on a combination of unfolded partial least-squares (U-PLS) with residual bilinearization (RBL) has been applied to second-order data obtained from excitation-emission fluorescence matrices for determining atenolol in human urine, even in the presence of background interactions and fluorescence inner filter effects, which are both sample dependent. Atenolol is a cardioselective beta-blocker, which is considered a doping agent in shoot practice, so that its determination in urine can be required for monitoring the drug. Loss of trilinearity due to analyte-background interactions which may vary between samples, as well as inner filter effects, precludes the use of methods like parallel factor analysis (PARAFAC) that cannot handle trilinearity deviations, and justifies the employment of U-PLS. Successful analysis required to include the background in the calibration set. Unexpected components appear in new urine samples, different from those used in calibration set, requiring the second-order advantage which is obtained from a separate procedure known as residual bilinearization (RBL). Satisfactory results were obtained for artificially spiked urines, and also for real urine samples. They were statistically compared with those obtained applying a reference method based on high-performance liquid chromatography (HPLC).     

Analysis of amoxicillin in human urine by photo-activated generation of fluorescence excitation-emission matrices and artificial neural networks combined with residual bilinearization

Fluorescence excitation-emission data recorded for amoxicillin after photo-activated reaction with periodate have been processed by a novel second-order multivariate method based on the combination of artificial neural networks and residual bilinearization (ANN/RBL), since the signals bear a strong non-linear relation with the analyte concentration. The selected chemometric methodology is employed for the first time to evaluate experimental non-linear second-order spectral information. Due to severe overlapping between the emission profiles for the analyte reaction product and for the urine background, calibration was done using different spiked urine samples. This allowed for the determination of amoxicillin in test spiked urines, other than those employed for calibration. When new urine samples containing a fluorescent anti-inflammatory were analyzed, accurate prediction in the presence of unexpected components required the achievement of the second-order advantage, which is provided by the post-training RBL procedure. Amoxicillin was also determined by ANN/RBL in a series of real urine samples, which allowed one to perform a comparison study with the reference high-performance liquid chromatographic technique.     

Multi-way partial least-squares and residual bi-linearization for the direct determination of monohydroxy-polycyclic aromatic hydrocarbons on octadecyl membranes via room-temperature fluorescence excitation emission matrices

Multi-way partial least-squares (N-PLS) is combined to the residual bi-linearization procedure (RBL) for the direct analysis of metabolites of polycyclic aromatic hydrocarbons in urine samples. Metabolite analysis is carried out via a two-step experimental procedure based on solid-phase extraction and room temperature fluorescence spectroscopy. Excitation-emission matrices are recorded from octadecyl (C18) membranes that serve as solid substrates for sample extraction and spectroscopic measurements. Excellent metabolite recoveries were obtained in all cases, which varied from 96.2 ± 1.35% (9-hydroxyphenanthrene) to 99.7 ± 0.49% (3-hydroxybenzo[a]pyrene). Background correction of extraction membranes is carried out with a new alternating least-squares (ALS) procedure adapted to second order data. The performance of N-PLS/RBL is compared to the well-established multivariate curve resolution-alternating least-squares (MCR-ALS) algorithm. Both algorithms provided similar analytical figures of merit, including their ability to handle unknown interference in urine samples. With only 10 mL of sample, the limits of detection varied between 0.06–0.08 ng mL⁻¹ (1-hydroxypyrene) and 0.016–0.018 ng mL⁻¹ (2-hydroxyfluorene). When compared to previously reported univariate calibration data, the limits of detection via N-PLS/RBL and MCR-ALS are approximately one order of magnitude higher. This was somehow expected due to the effect of unexpected components in multivariate figures of merit, i.e. a more realistic approach to the analysis of metabolites in human urine samples.     

Chemometric processing of second-order liquid chromatographic data with UV–vis and fluorescence detection. A comparison of multivariate curve resolution and parallel factor analysis 2

Second-order liquid chromatographic data with multivariate spectral (UV–vis or fluorescence) detection usually show changes in elution time profiles from sample to sample, causing a loss of trilinearity in the data. In order to analyze them with an appropriate model, the latter should permit a given component to have different time profiles in different samples. Two popular models in this regard are multivariate curve resolution-alternating least-squares (MCR-ALS) and parallel factor analysis 2 (PARAFAC2). The conditions to be fulfilled for successful application of the latter model are discussed on the basis of simple chromatographic concepts. An exhaustive analysis of the multivariate calibration models is carried out, employing both simulated and experimental chromatographic data sets. The latter involve the quantitation of benzimidazolic and carbamate pesticides in fruit and juice samples using liquid chromatography with diode array detection, and of polycyclic aromatic hydrocarbons in water samples, in both cases in the presence of potential interferents using liquid chromatography with fluorescence spectral detection, thereby achieving the second-order advantage. The overall results seem to favor MCR-ALS over PARAFAC2, especially in the presence of potential interferents.     

Different strategies for the direct determination of amoxicillin in human urine by second-order multivariate analysis of kinetic-spectrophotometric data

The kinetic evolution of UV-visible absorption spectra of amoxicillin in the presence of copper(II) ions has been processed by the second-order multivariate methods parallel factor analysis (PARAFAC) and also by a novel approach based on partial least-squares with residual bilinearization (PLS/RBL). The latter one is employed for the first time to evaluate kinetic-spectral information. The mechanism of the analyte metal-catalyzed hydrolysis involves a reaction intermediate and a final reaction product, both with spectra which may allow for the determination of amoxicillin in human urine, even in the presence of unsuspected sample components. This is possible thanks to the second-order advantage exploited by the employed chemometric algorithms, among which PARAFAC and PLS/RBL gave the best results. Amoxicillin was determined in a series of spiked and real urine samples, which allowed to perform, respectively, a recovery study and a comparison with the reference high-performance liquid chromatographic technique. The best figures of merit were obtained with PLS/RBL, namely sensitivity, 0.5 AU L mg⁻¹ (AU = absorbance units), analytical sensitivity, 500 L mg⁻¹ and limit of detection, 6 mg L⁻¹. Relative advantages and disadvantages of the employed algorithms are discussed.     

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%.     

Photoinduced fluorimetric determination of folic acid and 5-methyltetrahydrofolic acid in serum using the kinetic evolution of the emission spectra accomplished with multivariate second-order calibration methods

Second-order multivariate calibration methods in combination with a continuous flow system, which allows for the continuous on-line irradiation of the analytes, have been employed for the determination of folic acid and its main metabolite 5-methyltetrahydrofolic acid in serum samples. An experimental central composite design, together with response surface methodology, has been used to find the optimum instrumental variables to perform the photochemical reaction. The time evolution of the emission spectra of the generated photoproducts, in the range 330-540 nm, after irradiation at 275 nm for 20 min, provided the three-way data set employed. On the basis of the differences on the kinetic rates of the photoreaction of both analytes, direct determination of the compounds in human plasma has been accomplished. The second-order methods assayed were parallel factor analysis (PARAFAC), self-weighted alternating trilinear decomposition (SWATLD), and unfolded partial least-squares (U-PLS), multidimensional partial least-squares (N-PLS), and bilinear least-squares (BLLS), all three in combination with the residual bilinearization procedure (RBL).     

Second-order advantage from kinetic-spectroscopic data matrices in the presence of extreme spectral overlapping A multivariate curve resolution--alternating least-squares approach

Multivariate curve resolution coupled to alternating least-squares (MCR-ALS) has been employed to model kinetic-spectroscopic second-order data, with focus on the achievement of the important second-order advantage, under conditions of extreme spectral overlapping among sample components. A series of simulated examples shows that MCR-ALS can conveniently handle the studied analytical problem unlike other second-order multivariate calibration algorithms, provided matrix augmentation is implemented in the spectral mode instead of in the usual kinetic mode. The approach has also been applied to three experimental examples, which involve the determination of: (1) the antiparkinsonian carbidopa (analyte) in the presence of levodopa as a potential interferent, both reacting with cerium (IV) to produce the fluorescent species cerium (III) with different kinetics; (2) Fe(II) (analyte) in the presence of the interferent Zn(II), both catalyzing the oxidation of methyl orange with potassium bromate; and (3) tartrazine (analyte) in the presence of the interferent brilliant blue, both oxidized with potassium bromate, with the interferent leading to a product with an absorption spectrum very similar to tartrazine. The results indicate good analytical performance towards the analytes, despite the intense spectral overlapping and the presence of unexpected constituents in the test samples.     

Spectrofluorimetry in organized media coupled to second-order multivariate calibration for the determination of galantamine in the presence of uncalibrated interferences

The present article describes the spectrofluorimetric determination of galantamine, a widely used acetylcholinesterase inhibitor, through excitation-emission fluorescence matrices and second-order calibration. With the purpose of enhancing the fluorescence intensity of this substance, the effect of different organized assemblies was evaluated. Although the interaction of galantamine with different cyclodextrins is weak, it was corroborated that the fluorescence intensity of this pharmaceutical in the presence of α-cyclodextrin is increased by a twofold factor. Among the studied micellar media, the anionic surfactant sodium dodecyl sulfate produced the largest signals for the compound of interest (sixfold enhancement), and was selected as auxiliary reagent for the subsequent determinations. The developed approach enabled the determination of galantamine at the ng mL⁻¹ level without the necessity of applying separation steps, and in the presence of uncalibrated interferences. The applied second-order chemometric tools were parallel factor analysis (PARAFAC), unfolded partial least-squares coupled to residual bilinearization (U-PLS/RBL), and multidimensional partial least-squares coupled to residual bilinearization (N-PLS/RBL). The ability of U-PLS/RBL to successfully overcome spectral interference problems is demonstrated. The quality of the proposed method was established with the determination of galantamine in both artificial and natural water samples.     

Three-way partial least-squares/residual bilinearization study of second-order lanthanide-sensitized luminescence excitation-time decay data: analysis of benzoic acid in beverage samples

Lanthanide-sensitized luminescence excitation-time decay matrices were employed for achieving the second-order advantage using as chemometric algorithms parallel factor analysis (PARAFAC) and multidimensional partial least-squares with residual bilinearization (N-PLS/RBL). The second-order data were measured for a calibration set of samples containing the analyte benzoic acid in the concentration range from 0.00 to 5.00 mg L⁻¹, for a validation set containing the analyte and the potential interferent saccharin (in the range 0.00–6.00 mg L⁻¹), and for real samples of beverages containing benzoic acid as preservant, saccharin, and other potentially interfering compounds. All samples were treated with terbium(III), trioctylphosphine oxide as a synergistic ligand, and contained a suitable imidazol buffer, in order to ensure maximum intensity of the luminescence signals. The results indicate a slightly better predictive ability of the newly introduced N-PLS/RBL procedure over standard PARAFAC, both in what concerns the comparison with nominal analyte concentrations in the validation sample set and with results provided by the reference high-performance liquid chromatographic technique for the real sample set.     

A review of multivariate calibration methods applied to biomedical analysis

The determination of the contents of therapeutic drugs, metabolites and other important biomedical analytes in biological samples is usually performed by using high-performance liquid chromatography (HPLC). Modern multivariate calibration methods constitute an attractive alternative, even when they are applied to intrinsically unselective spectroscopic or electrochemical signals. First-order (i.e., vectorized) data are conveniently analyzed with classical chemometric tools such as partial least-squares (PLS). Certain analytical problems require more sophisticated models, such as artificial neural networks (ANNs), which are especially able to cope with non-linearities in the data structure. Finally, models based on the acquisition and processing of second- or higher-order data (i.e., matrices or higher dimensional data arrays) present the phenomenon known as “second-order advantage”, which permits quantitation of calibrated analytes in the presence of interferents. The latter models show immense potentialities in the field of biomedical analysis. Pertinent literature examples are reviewed.     

Multivariate curve-resolution analysis of pesticides in water samples from liquid chromatographic-diode array data

Liquid chromatographic-diode array detection data recorded for aqueous mixtures of 11 pesticides show the combined presence of strongly coeluting peaks, distortions in the time dimension between experimental runs, and the presence of potential interferents not modeled by the calibration phase in certain test samples. Due to the complexity of these phenomena, data were processed by a second-order multivariate algorithm based on multivariate curve resolution and alternating least-squares, which allows one to successfully model both the spectral and retention time behavior for all sample constituents. This led to the accurate quantitation of all analytes in a set of validation samples: aldicarb sulfoxide, oxamyl, aldicarb sulfone, methomyl, 3-hydroxy-carbofuran, aldicarb, propoxur, carbofuran, carbaryl, 1-naphthol and methiocarb. Limits of detection in the range 0.1-2 μg mL⁻¹ were obtained. Additionally, the second-order advantage for several analytes was achieved in samples containing several uncalibrated interferences. The limits of detection for all analytes were decreased by solid phase pre-concentration to values compatible to those officially recommended, i.e., in the order of 5 ng mL⁻¹.     

Direct sampling tandem mass spectrometry (MS/MS) and multiway calibration for isomer quantitation

Direct sampling tandem mass spectrometry (MS/MS) was used for the quantitation of mixtures of the isomers 2-, 3- and 4-ethyl pyridine. The similarity between the analytes and the second-order nature of MS/MS data require the use of multivariate calibration techniques capable of handling multiway data. Multilinear PLS (N-PLS) was applied here, as well as the alternative technique of unfolding the data and using standard two-way PLS. Particular attention was paid to the optimal type of spectral preprocessing. Due to the presence of heteroscedastic noise the logarithmic transform of the spectra prior to calibration gives the best results. Predictions errors of the order of 10-15% were obtained, which compare well with other results found in the literature.     

The Successive Projections Algorithm for interval selection in trilinear partial least-squares with residual bilinearization

In this work the Successive Projection Algorithm is presented for intervals selection in N-PLS for three-way data modeling. The proposed algorithm combines noise-reduction properties of PLS with the possibility of discarding uninformative variables in SPA. In addition, second-order advantage can be achieved by the residual bilinearization (RBL) procedure when an unexpected constituent is present in a test sample. For this purpose, SPA was modified in order to select intervals for use in trilinear PLS. The ability of the proposed algorithm, namely iSPA-N-PLS, was evaluated on one simulated and two experimental data sets, comparing the results to those obtained by N-PLS. In the simulated system, two analytes were quantitated in two test sets, with and without unexpected constituent. In the first experimental system, the determination of the four fluorophores (l-phenylalanine; l-3,4-dihydroxyphenylalanine; 1,4-dihydroxybenzene and l-tryptophan) was conducted with excitation-emission data matrices. In the second experimental system, quantitation of ofloxacin was performed in water samples containing two other uncalibrated quinolones (ciprofloxacin and danofloxacin) by high performance liquid chromatography with UV–vis diode array detector. For comparison purpose, a GA algorithm coupled with N-PLS/RBL was also used in this work. In most of the studied cases iSPA-N-PLS proved to be a promising tool for selection of variables in second-order calibration, generating models with smaller RMSEP, when compared to both the global model using all of the sensors in two dimensions and GA-NPLS/RBL.     

Chemometric-assisted MIP-optosensing system for the simultaneous determination of monoamine naphthalenes in drinking waters

In the present work a chemometric-assisted molecularly imprinted polymer (MIP)-fluorescence optosensing system has been developed for determining monoamines naphthalene compounds in drinking waters. The use of chemometrics for processing flow injection analysis with MIP fluorescence optosensor data allowed the simultaneous determination of the principal monoamine naphthalene compounds 1-naphthylamine (1-NA) and 2-naphthylamine (2-NA) even in presence of potential interferent 1-naphthalenemethylamine (1-NMA). Classical chemometrics tools such as partial least-squares (PLS-1), as well as second-order algorithms like multiway PLS (N-PLS) and unfolded PLS (U-PLS), were successfully applied, assisting fluorescence emission spectra at a fixed excitation wavelength or excitation-emission fluorescence matrices (EEM), respectively, when interferents are considered in the calibration set. The combinations of both N-PLS and U-PLS with residual bilinearization (RBL), achieving the second-order advantage, were satisfactory applied for the simultaneous determination of the main monoamine naphthalene compounds in drinking water, in the presence of a potential interferent without sample pretreatment, even when the later is not modeled in calibration set. Predictive ability, accuracy, figures of merit, as well as advantages and disadvantages of the different strategies were discussed.     

Matrix-free analysis of aflatoxins in pistachio nuts using parallel factor modeling of liquid chromatography diode-array detection data

In the present study a second-order calibration strategy for high performance liquid chromatography with diode-array detection (HPLC-DAD) has been developed using parallel factor analysis (PARAFAC) and has been applied for simultaneous determination of aflatoxins B₁, B₂, G₁ and G₂ in pistachio nuts in the presence of matrix interferences. Sample preparation was based on solvent extraction (SE) followed by solid phase extraction (SPE) on Bond Elut C18 cartridges. Since the sample preparation procedure was not selective to the analytes of interest, exploiting second-order advantage to obtain concentrations of individual analytes in the presence of uncalibrated interfering compounds seemed necessary. Appropriate pre-processing steps have been applied to correct background signals and the effect of retention time shifts. Transferred calibration data set obtained from standardization of solvent based calibration data has been used in prediction step. The results of PARAFAC on a set of spiked and naturally contaminated pistachio nuts indicated that the four aflatoxins could be successfully determined. The method was validated and multivariate analytical figures of merit were calculated. The advantages of the proposed method are using a low-cost SPE step relative to standard method of aflatoxin analysis (immune affinity column assay), a unique and simple isocratic elution program for all samples and a calibration transfer for saving both chemicals and time of analysis. This study show that coupling of SPE-HPLC-DAD with PARAFAC as a powerful second-order calibration method can be considered as an alternative method for resolution and quantification of aflatoxins in the presence of unknown interferences obtained through analysis of highly complex matrix of pistachio samples and cost per analysis can be reduced significantly.