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

Rapid determination of three textile surfactants in environmental samples by modeling excitation-emission second-order data with multi-way calibration methods

The textile industry is an important potential source of environmental pollution due to the use of chemical products. Dyes, hydrolyzed dyes, and surfactants, among others, are chemical compounds present in wastewater of textile plant. Moreover, the anionic surfactants have toxic effects for various aquatic organisms even in low concentrations. The methodologies investigated to quantify surfactants, in general, consume a lot of analysis time and frequently use toxic or environmentally objectionable reagents. For these reasons, the objective of this work was to develop a quick and simple method to quantify surfactants without the use of expensive reagents and equipment, avoiding extraction and preconcentration stages. The proposed method is based on fluorescent spectroscopy measurements for the acquisition of second-order data in excitation-emission matrices and multivariate calibration techniques applied to the data. The unfolded partial least squares combined to residual bilinearization (U-PLS/RBL) algorithm was better than parallel factor analysis (PARAFAC). U-PLS/RBL accurately quantified alkylnonylphenolethoxylated (APEO), dodecylbenzenesulfonic acid (ADBS), and 2-phenoxy-ethoxylated fatty alcohol (AGFE) surfactants. The chemometric model obtained good analytical figures of merit: REP% between 5 and 13 and LOQ between 0.45 and 2.77 μg mL^-1. This methodology had no significant difference compared with results obtained by a HPLC-FD reference technique, in addition with a considerable reduction in analysis time, reagent consumption, and therefore lower cost. For environmental applications, APEO, ADBS, and AGFE were quantify in textile wastewater treatment and in the receiving water body. The concentrations varied from 8.73 to 73.94 μg mL^-1 in the textile wastewater and were not detected in the receiving water body.

Multiway resolution of spectrochromatographic measurements for the quantification of echinuline in marine-derived fungi Aspergillus chevalieri using parallel factor analysis

A multiway resolution of incomplete chromatographic separation was presented for spectrochromatographic quantification of echinuline in marine-derived fungi Aspergillus chevalieri. Two-dimensional spectrochromatographic maps of calibration, validation and real samples were recorded as a function of time and wavelength using UPLC-PDA instrument under non-optimized chromatographic conditions, which gave rise to co-elution of echinuline and the constituents of sample matrix. A three-way array was obtained by concatenating the data matrices of the spectrochromatographic maps. Then, parallel factor analysis was applied to the multiway array to extract the individual contribution of echinuline in three modes (time, wavelength and sample). While time and wavelength profiles were used for the characterization of echinuline, the sample profile was used for its quantitative determination of the analyte in validation set and in real samples. Validity of the analytical method was evaluated by analyzing the validation set, which consist of test samples, standard addition samples, intra-day and inter-day samples. The proposed multiway analysis method was then applied to marine-derived fungi extracts and echinuline content was found to be 31.9 µg/g based on the average of ten assay results. The assay results provided by PARAFAC model were statistically compared with those obtained by a newly developed classical UPLC method, which ensured the complete separation of echinuline in a run time of nine minutes. The assay results were found to be comparable due to the fact that there was no significant difference between the analysis results (F = 1.63, Fcrit = 3.17; t = 0.69, tcrit = 2.11) at the significance level of 95%). Consequently, the PARAFAC method permitted the accurate determination of echinuline in fungal extracts despite the partial chromatographic separation with a run time of only three minutes.

Green approach for simultaneous determination of multi-pesticide residue in environmental water samples using excitation-emission matrix fluorescence and multivariate calibration

Pesticides are among the most widespread organic contaminants in aquatic environments. In this work, a new green fluorescence application was proposed for the simultaneous determination of four widely employed pesticides in environmental water samples. To overcome the highly overlapped spectra within the analytes, and with the tissue matrix interferences in complex solutions, we have used the multivariate calibration methods such as parallel factor analysis (PARAFAC) and unfolded partial least squares coupled to residual bilinearization (U-PLS/RBL). These four pesticides can be identified simultaneously, and the correlation coefficients between resolved and actual spectra are all above 0.95. The second-order advantage allowed the determination of four pesticides at the ng mL^-1 level, even in the presence of humic acid (HA). The best results were obtained with the limits of detection of 1.72-18.69 for Carbendazim (CBZ), 0.30-5.19 for carbaryl (CAR), 0.35-6.32 for chlorothalonil (CHL), and 4.92-29.96 for tsumacide (TSU) (ng mL^-1), which can fully meet the quantitative detection and analysis requirements of trace pesticides in water samples. The real water sample of Bohai Seawater was used to check the performance of this approach in practical applications, which have achieved good prediction results of U-PLS/RBL. This study demonstrated the proposed method is rapid, accurate, sensitive, low detection limit, and environmentally friendly to determinate multi-pesticide residues in environmental water samples.

Simultaneous determination of α-asarone and β-asarone in Acorus tatarinowii using excitation-emission matrix fluorescence coupled with chemometrics methods

A chemometrics-assisted excitation-emission matrix (EEM) fluorescence method was proposed for simultaneous determination of α-asarone and β-asarone in Acorus tatarinowii. Using the strategy of combining EEM data with chemometrics methods, the simultaneous determination of α-asarone and β-asarone in the complex Traditional Chinese medicine system was achieved successfully, even in the presence of unexpected interferents. The physical or chemical separation step was avoided due to the use of "mathematical separation". Six second-order calibration methods were used including parallel factor analysis (PARAFAC), alternating trilinear decomposition (ATLD), alternating penalty trilinear decomposition (APTLD), self-weighted alternating trilinear decomposition (SWATLD), the unfolded partial least-squares (U-PLS) and multidimensional partial least-squares (N-PLS) with residual bilinearization (RBL). In addition, HPLC method was developed to further validate the presented strategy. Consequently, for the validation samples, the analytical results obtained by six second-order calibration methods were almost accurate. But for the Acorus tatarinowii samples, the results indicated a slightly better predictive ability of N-PLS/RBL procedure over other methods.

Quality assessment of the saffron samples using second-order spectrophotometric data assisted by three-way chemometric methods via quantitative analysis of synthetic colorants in adulterated saffron

Saffron is a valuable culinary spice that can be used not only for dyes and cooking, but also for many medical purposes. Due to its high price and restriction of its production, various fraud manners in its production have been growing. Addition of synthetic colorants to saffron is the most common way for adulteration. In this work, chemometric methods are proposed to resolve the three-dimensional absorbance spectra-pH data for simultaneous determination of the two colorants Tartrazin and Sunset yellow, in adulterated saffron. The rank deficiency in the concentration mode impaired the system. Therefore, to extirpate the ambiguity, which results from rank deficiency, three-way variation array V was generated by subtracting the first pH spectrum from each spectrum at each pH. This allows the extraction of extent reaction profile and mixture reaction spectral profiles, as well as the relative concentrations of the analytes.

Second-order advantage maintenance with voltammetric data modeling for quantitation of ethiofencarb in the presence of interferences

This work presents the development of a method based on a voltammetric determination coupled to second-order data modeling with multivariate curve resolution-alternating least-square (MCR-ALS) and unfolded partial least squares regression followed by residual bilinearization (U-PLS/RBL) for the quantitation of the pesticide ethiofencarb in the presence of interferences in tap water. The determination of ethiofencarb by voltammetry is possible on the basis that this pesticide is hydrolysed in alkaline media giving rise to a compound which shows an oxidation peak at Ep=0.80 V. But some other pesticides belonging to the same family can be considered as interferences due to the high overlapping in their signals. The two such interferences are fenobucarb and bendiocarb, which have not been included in the calibration step. The possibility of second-order multivariate calibration was studied by using the hydrolysis time as the third variable, and MCR-ALS and U-PLS/RBL. Good results were obtained in this determination in spite of the high overlapping in voltammetric signals.

Total synchronous fluorescence spectroscopic data modeled with first- and second-order algorithms for the determination of doxorubicin in human plasma

In this work, we present the development of a method for the determination of doxorubicin in plasma samples in the presence of an unexpected component (riboflavin) by using total synchronous fluorescence spectroscopic data matrices. To the best of our knowledge, this is the first time that the second-order advantage is obtained with this kind of data. Two strategies including unfolding the data and: (a) processing with multivariate curve resolution coupled to alternating least-squares as first-order data or (b) processing with unfolded partial least-squares and exploiting the second-order advantage by the residual bilinearization procedure were considered. The calibration set was built with human plasma samples spiked with doxorubicin, while the validation set was prepared with human plasma samples spiked with both doxorubicin and riboflavin, a drug whose spectrum highly overlaps with the one corresponding to doxorubicin. Both methodologies reached good indicators of accuracy: recoveries of ca. 100 ± 8% and REP of ca. 5%; and precision: coefficient of variations between 7 and 9%.

Pharmaceutical analysis in solids using front face fluorescence spectroscopy and multivariate calibration with matrix correction by piecewise direct standardization

This paper reports the application of piecewise direct standardization (PDS) for matrix correction in front face fluorescence spectroscopy of solids when different excipients are used in a pharmaceutical preparation based on a mixture of acetylsalicylic acid (ASA), paracetamol (acetaminophen) and caffeine. As verified in earlier studies, the use of different excipients and their ratio can cause a displacement, change in fluorescence intensity or band profile. To overcome this important drawback, a standardization strategy was adopted to convert all the excitation-emission fluorescence spectra into those used for model development. An excitation-emission matrix (EEM) for which excitation and emission wavelengths ranging from 265 to 405 nm and 300 to 480 nm, respectively, was used. Excellent results were obtained using unfolded partial least squares (U-PLS), with RMSEP values of 8.2 mg/g, 10.9 mg/g and 2.7 mg/g for ASA, paracetamol and caffeine, respectively, and with relative errors lesser than 5% for the three analytes.

Simultaneous determination of Solvent Yellow 124 and Solvent Red 19 in diesel oil using fluorescence spectroscopy and chemometrics

Differences in tax levels for diesel oil stimulate the illegal removal of characteristic diazo compounds purposely added to designate its possible usage. In order to reduce the losses in the national income, there is a strong need to develop a sensitive and cost-effective analytical procedure for the detection of this illegal action. In this study, we describe a novel analytical approach for a qualitative and quantitative determination of two diazo compounds (Solvent Yellow 124 and Solvent Red 19) that are usually added to diesel oil. The methodology proposed combines the use of excitation-emission matrix fluorescence spectroscopy as an analytical technique and partial least squares regression as a multiple modeling tool. With this new methodology, relatively low root mean square errors of prediction (for independent set of test samples) that are equal to 0.223 for Solvent Red 19 and 0.263 for Solvent Yellow 124, were obtained and the results were stable, which were indicated by an analysis performed after 48 and 96 h. The methodology is also nondestructive and allows for (i) simultaneous detection of diesel oil additives, (ii) determination of satisfactory limits of detection (0.048 and 0.042 mg L(-1) for Solvent Red 19 and Solvent Yellow 124, respectively), and (iii) obtaining of considerably low relative standard deviations of 2.33% for Solvent Yellow 124 and of 3.23% for Solvent Red 19 in comparison with the existing norm level.

Matrix effect in second-order data

The aim of this paper is to determine the concentration of three dyes throughout the tanning process of leather using vegetable tanning agents with a sequential injection analyser with second-order data treatment. As the vegetable tanning agents used are highly absorbent species, we focus on three aspects: (i) difficulties with the resolution (ii) the reduction in the working concentration range; and (iii) matrix effects. Ideally, second-order instruments provide "second-order advantage"; i.e. calibration is possible in the presence of uncalibrated interfering species. However, if the interfering species change the instrumental response of the analyte (in scale or shape), standard additions must be used to ensure the accuracy of the estimated analyte concentration. Here we study the presence of matrix effects for three dyes in several samples in order to significantly improve the accuracy of predictions in the presence of such effects. We found that there were matrix effects in at least 80% of the samples with an alpha risk of 5%. We used this method to study the exhaustion of dyes in the dyeing process.

Multiway Partial Least-Squares Coupled to Residual Trilinearization: A Genuine Multidimensional Tool for the Study of Third-Order Data. Simultaneous Analysis of Procaine and Its Metabolitep-Aminobenzoic Acid in Equine Serum

A new third-order multivariate calibration approach, based on the combination of multiway-partial least-squares with a separate procedure called residual trilinearization (N-PLS/RTL), is presented and applied to multicomponent analysis using third-order data. The proposed chemometric algorithm is able to predict analyte concentrations in the presence of unexpected sample components, which require strict adherence to the second-order advantage. Results for the determination of procaine and its metabolite p-aminobenzoic acid in equine serum are discussed, based on kinetic fluorescence excitation-emission four-way measurements and application of the newly developed multiway methodology. Since the analytes are also the reagent and product of the hydrolysis reaction followed by fast-scanning fluorescence spectroscopy, the classical approach based on parallel factor analysis is challenged by strong linear dependencies and multilinearity losses. In comparison, N-PLS/RTL appears an appealing genuine multiway alternative that avoids the latter complications, yielding analytical results that are statistically comparable to those rendered by related unfolded algorithms, which are also able to process four-way data. Prediction was made on validation samples with a qualitative composition similar to the calibration set and also on test samples containing unexpected equine serum components.

Direct determination of propranolol in urine by spectrofluorimetry with the aid of second order advantage

This work presented an application of the second-order advantage provided by parallel factor analysis (PARAFAC) aiming at direct determination of propranolol, a beta-blocker also used as doping agent, in human urine by spectrofluorimetry. The adopted strategy combined the use of PARAFAC, for extraction of the pure analyte signal, with the standard addition method, for a determination in the presence of an individual matrix effect caused by the quenching action of the proteins present in the urine. The urine samples were previously 100 times diluted. For each sample, four standard additions were performed, in triplicates. A specific PARAFAC model was built for each triplicate of each sample, from three-way arrays formed by 231 emission wavelengths, 8 excitation wavelengths and 5 measurements (sample plus 4 additions). The models were built with three factors and always explained more than 99.87% of the total variance. The obtained loadings were related to PRO and two background interferences. The scores related to PRO were used for a linear regression in the standard addition method. The obtained determinations in the PRO concentration range from 5.0 to 20.0 microg ml(-1) provided recoveries between 91.1 and 108.4%.

Experimental study of non-linear second-order analytical data with focus on the second-order advantage

Three different experimental systems have been studied regarding the determination of analytes in complex samples, using non-linear second-order instrumental data, which are intrinsically able to provide the second-order advantage. This permits the quantitation of calibrated analytes in the presence of unexpected sample components, although a suitable algorithm is required. The recently described combination of artificial neural networks with post-training residual bilinearization has been applied to the three data sets, with successful results concerning prediction accuracy and precision, as well as profile recovery for the potential interferents in test samples. The studies involve: (1) the determination of two pharmaceuticals in the presence of an unexpected excipient by absorbance-pH matrix measurements, (2) the quantitation of iron(II) by its catalytic effect on the kinetics of the bromate oxidation of a colorant in the presence of a second interfering organic dye, and (3) the analysis of the antibiotic amoxicillin by fluorescence excitation-emission matrices in the presence of a fluorescent anti-inflammatory. The prediction results were compared and shown to be significantly better than those yielded by the unfolded partial least-squares/residual bilinearization model, due to the non-linear nature of the studied data.

Second-Order Advantage Achieved by Unfolded-Partial Least-Squares/Residual Bilinearization Modeling of Excitation−Emission Fluorescence Data Presenting Inner Filter Effects

A second-order multivariate calibration approach, based on a combination of unfolded-partial least-squares with residual bilinearization (U-PLS/RBL), has been applied to fluorescence excitation-emission matrix data for multicomponent mixtures showing inner filter effects. The employed chemometric algorithm is the most successful one regarding the prediction of analyte concentrations when significant inner filter effects occur, even in the presence of unexpected sample components, which require strict adherence to the second-order advantage. Results for simulated fluorescence excitation-emission data are described, in comparison with the classical approach based on parallel factor analysis and other second-order algorithms, including generalized rank annihilation, bilinear least squares combined with residual bilinearization and multivariate curve resolution-alternating leastsquares. A set of experimental data was also studied, in which calibration was performed with fluorescence excitation-emission matrices for samples containing mixtures of chrysene (the analyte of interest) and benzopyrene (which produced strong inner filter effect across the useful wavelength range). Prediction was made on validation samples with a qualitative composition similar to the calibration set, and also on test samples containing an unexpected component (pyrene). In this latter case, U-PLS/RBL showed a unique success for the analysis of the calibrated component chrysene, achieving the useful second-order advantage.