Sensitivity Equation for Quantitative Analysis with Multivariate Curve Resolution-Alternating Least-Squares: Theoretical and Experimental Approach

Spatial occurrence of emerging contaminants in rivers and wastewater. Analysis of environmental and human risks

This study assesses the occurrence of emerging contaminants (ECs) from agricultural and livestock production activities along the Salado River (Santa Fe province, Argentina). Of the 23 ECs studied, 8 were detected and quantified in river and wastewater samples, including ciprofloxacin, enrofloxacin, chlorpyrifos-methyl, albendazole, fenbendazole, levamisole, diazepam, and thiamethoxam. In river samples, the highest concentrations corresponded to ciprofloxacin, chlorpyrifos-methyl, and enrofloxacin. In wastewater samples, albendazole, fenbendazole, ciprofloxacin, enrofloxacin, and thiamethoxam were found. The detection frequency ranged from 4.2% to 54.2% in river samples and from 11.1% to 22.2% in wastewater samples. The spatial distribution of contaminants showed different concentrations, with higher levels often found near urban and agricultural areas, suggesting anthropogenic sources. Ecological risk assessments for different organisms were conducted. Ciprofloxacin posed the highest risk, especially affecting bacteria, cyanobacteria, and algae. Diazepam also was found to pose significant risks to algae, crustaceans, and fishes. Chlorpyrifos-methyl was identified as highly hazardous to multiple living organisms, which is in agreement with the fish mortality that occurred at the sampling sites. The risk assessment in humans showed differences among children, adolescents, and adults, with infants (6 months to 1 year) being at a higher risk than adults. Children may be at a higher daily intake of contaminants than adults, raising concerns about the long-term effects of exposure. This work underscores the critical need for monitoring and regulating ECs in aquatic environments. Further studies are necessary to fully understand their impact and to develop effective strategies for mitigating their presence in water systems.

Area of Feasible Figures of Merit (AF-FOMs) for second-order multivariate calibrations in Multivariate Curve Resolution (MCR)

BACKGROUND

The significance and necessity of using powerful multivariate curve resolution (MCR) techniques in the study and investigation of chemical systems are clear and obvious. It has long been recognized the importance of using second-order data to extract both quantitative and qualitative information in analytical chemistry through multivariate calibration instead of univariate calibration. Although the calculation of analytical figures of merit (AFOMs) in multivariate calibrations seems to be complicated, in recent years these parameters have been reported for each developed analytical method based on multivariate calibrations.

RESULTS

It is well-known that using MCR to analyze second-order data may not produce a unique solution, a phenomenon associated with rotational ambiguity, which leads to the existence of a region or area of feasible solutions (AFS). This fact led us to argue that, instead of having uniquely defined AFOMs (sensitivity, selectivity, limit of detection, limit of quantitation, etc.), there should be an AFOM for every possible solution in the AFS. Following this argument, we report for the first time the generation of the Area of Feasible FOMs (AF-FOMs). The existence of a range of different FOMs in the AFS can be fully interpreted. It can also be predicted which AFOMs will have maximum or minimum values in each feasible band, and what kind of incremental or decremental changes will occur. Herein, the systematic grid search method was used to compute all feasible solutions and to calculate the AFOMs inside the feasible band.

SIGNIFICANCE

The claims were supported by analyzing artificially generated two-component data sets. The data sets include a single calibrated analyte and a single uncalibrated interferent, which was only present in the test samples. In addition, real experimental data aimed at the determination of therapeutic drugs in both water and human urine samples were analyzed. Finally, the arguments were generalized to a three-component simulated system, having a single analyte and two uncalibrated interferents.

Discrimination of the harvesting season of green tea by alcohol/salt-based aqueous two-phase systems combined with chemometric analysis

The flavor and aroma quality of green tea are closely related to the harvest season. The aim of this study was to identify the harvesting season of green tea by alcohol/salt-based aqueous two-phase system (ATPS) combined with chemometric analysis. In this paper, the single factor experiments (SFM) and response surface methodology (RSM) optimization were designed to investigate and select the optimal ATPS. A total of 180 green tea samples were studied in this work, including 86 spring tea and 94 autumn tea. After the active components in green tea samples were extracted by the optimal ethanol/(NH₄)₂SO₄ ATPS, the qualitative and quantitative analysis was realized based on HPLC-DAD combined with alternating trilinear decomposition-assisted multivariate curve resolution (ATLD-MCR) algorithm, with satisfactory spiked recoveries (86.00 %-112.45 %). The quantitative results obtained from ATLD-MCR model were subjected to chemometric pattern recognition analysis. The constructed partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) models showed better results than the principal component analysis (PCA) model, and the R²X_cum values (>0.835) and R²Y_cum (>0.937) were close to 1, the Q²_cum values were greater than 0.75 (>0.933), and the differences between R²Y_cum and Q²_cum were not larger than 0.2, indicating excellent cross-validation prediction performance of the models. Furthermore, the classification results based on the hierarchical clustering analysis (HCA) were consistent with the PCA, PLS-DA and OPLS-DA results, establishing a good correlation between tea active components and the harvesting seasons of green tea. Overall, the combination of ATPS and chemometric methods is accurate, sensitive, fast and reliable for the qualitative and quantitative determination of tea active components, providing guidance for the quality control of green tea.

A tutorial on multi-way data processing of excitation-emission fluorescence matrices acquired from semiconductor quantum dots sensing platforms

This tutorial demonstrates how to exploit the second-order advantage on excitation-emission fluorescence matrices (EEFMs) acquired from sensing platforms based on analyte-triggered semiconductor quantum dots (QDs) fluorescence modulation (quenching/enhancing). The advantage in processing such second-order EEFMs data from complex samples, seeking successful quantification, is comprehensively addressed. It is worth emphasizing that, aiming to exploit the second-order advantage, the selection of the most appropriate advanced chemometric model should rely on the matching between the data structure and the physicochemical chemometric model assumption. In this sense, the achievement of second-order advantage after EEFMs' processing is extensively addressed throughout this tutorial taking into consideration three different analytical situations, each involving a specific data structure: i) parallel factor analysis (PARAFAC), which is applied in a real dataset stacked in a three-way data array containing a trilinear data structure acquired from QDs-based detection with non-selective species; ii) multivariate curve resolution - alternating least-squares (MCR-ALS), which is also employed in a real dataset arranged in an augmented data matrix containing non-trilinear data structure acquired from QDs-based detection with a single breaking mode caused by background signals; iii) unfolded partial least-squares with residual bilinearization (U-PLS/RBL), which is applied in a dataset containing non-trilinear data acquired from a classical fluorescence system with two breaking modes caused by inner filter effect (IFE) in both instrumental modes (excitation and emission). The latter challenging data structure can be acquired via fluorescence quenching from IFE-based sensing platforms and chemometrically handled in two main steps. First, a set of calibration EEFMs data is converted into an unfolded data matrix during the unfolding process, followed by applying U-PLS model. Second, a post-calibration procedure using RBL analysis is applied to a test sample of EEFM maintained in its matrix form, in order to handle potential interferents. In the last section, the state-of-the-art of second-order EEFMs data acquired from semiconductor QDs-based sensing platforms and coupled to multi-way fluorescence data processing to accomplish a successful quantification, even with substantial interfering species, is critically reviewed.

Chromatographic Applications in the Multi-Way Calibration Field

In this review, recent advances and applications using multi-way calibration protocols based on the processing of multi-dimensional chromatographic data are discussed. We first describe the various modes in which multi-way chromatographic data sets can be generated, including some important characteristics that should be taken into account for the selection of an adequate data processing model. We then discuss the different manners in which the collected instrumental data can be arranged, and the most usually applied models and algorithms for the decomposition of the data arrays. The latter activity leads to the estimation of surrogate variables (scores), useful for analyte quantitation in the presence of uncalibrated interferences, achieving the second-order advantage. Recent experimental reports based on multi-way liquid and gas chromatographic data are then reviewed. Finally, analytical figures of merit that should always accompany quantitative calibration reports are described.

Three-way calibration using PARAFAC and MCR-ALS with previous synchronization of second-order chromatographic data through a new functional alignment of pure vectors for the quantification in the presence of retention time shifts in peak position and shape

In the present contribution is shown the application of the recently developed functional alignment of pure vectors (FAPV) as a proper algorithm to align second-order chromatographic data with severe retention time shifts in peak position and shape. FAPV decomposed a three-way chromatographic data array in their three modes (sample, spectral and elution time vectors), using a basis function to pre-process the non-linear mode (elution time) and then it aligns the functionalized pure vectors and reshapes the transformed vectors into matrices, restoring the trilinearity of second-order chromatographic data. The well-aligned three-way chromatographic data array is then successfully decomposed by advanced chemometric models such as parallel factor analysis (PARAFAC) and multivariate curve resolution - alternating least-squares (MCR-ALS) with the trilinearity constraint. The performance of this innovative analytical strategy based on PARAFAC and MCR-ALS with previous synchronization of data through FAPV algorithm is properly evaluated using real second-order chromatographic data with multiple artifacts, i.e., shifts in peak position and shape for the simultaneous quantification of amoxicillin and potassium clavulanate in commercial medicinal drugs. The present contribution compares some analytical results achieved by: (1) the usual MCR-ALS as a bilinear model applied in augmented data matrix without previous synchronization and with interval correlation optimized shifting (ICOSHIFT) and FAPV and (2) trilinear models using PARAFAC with ICOSHIFT and FAPV and trilinearity constraint in MCR-ALS with FAPV. Available results suggest that these strongly shifted and warped elution time profiles cause for the loss of trilinearity, which can be adequately restored by FAPV algorithm. PARAFAC performed a successful trilinear decomposition of three-way chromatographic data array with law values of relative prediction error (REP) in the order of 1.34-1.42% in both analytes.

Eco-friendly multivariate curve resolution-alternating least squares and chromatographic quantifications of some veterinary drug residues in pharmaceutical industrial wastewater

Three eco-friendly and cost-effective analytical methods were developed and optimized for quantitative analysis of some veterinary drug residues in production wastewater samples. The studied drugs were ivermectin, rafoxanide and sulfadimidine. A solid-phase extraction procedure was employed using Bond Elut C18 cartridges, prior to analysis. The first method was a chemometric approach called multivariate curve resolution - alternating least squares (MCR-ALS). A calibration model was developed and several figures of merit (RMSEP, SEP, bias, RE%) were calculated. The second method was a thin layer chromatography followed by densitometric measurements at 245 nm. The separation was performed using silica gel 60 F254 plates and ethyl acetate : acetonitrile : toluene : ammonia (20 : 3 : 2 : 1, by volume) as a developing system. The third method was a high performance liquid chromatographic separation on HiQsil C18 HS column with UV detection at 245 nm. The mobile phase consisted of acetonitrile : methanol : water (60 : 25 : 15, by volume), with a flow rate of 1.5 mL min-1. The proposed methods were validated according to ICH guidelines. The described procedures were applied to quantify the studied drug residues in synthetic and real industrial wastewater samples. The proposed methods were statistically compared with the official and the reported methods, showing no significant difference with respect to accuracy and precision at P = 0.05.

Multivariate optimization of a dispersive liquid-liquid microextraction method for the determination of six antiparasite drugs in kennel effluent waters by using second-order chromatographic data

Six veterinary active ingredients (imidacloprid, albendazole, fenbendazole, praziquantel, fipronil and permethrin) were extracted and quantified by liquid chromatography with diode array detection in water samples from a wetland system used for the treatment of waste from a dog breeding plant. Response surface methodology, based on least-squares and artificial neural networks modelling, was applied for the optimization of a dispersive liquid-liquid microextraction (DLLME) procedure. Firstly, two experimental designs were built for screening and optimization, respectively. Then, the desirability function was implemented for the simultaneous optimization of the six recoveries (chromatographic areas of the six compounds). The optimum conditions were: 600 μL of acetone (dispersive solvent), 670 μL of dichloromethane (extractant solvent) and 0.6 min of vortex mixing. The preconcentration factor was 37.5. Then, in order to identify and quantify the six drugs, second-order calibration with MCR-ALS modeling of HPLC-DAD data was implemented attaining successful results. The limits of quantification were 4 ng mL^-1 for imidaclopril, albendazole and fenbendazole; 8 ng mL^-1 for praziquantel and fipronil; and 26 ng mL^-1 for permethrin. The developed method allowed the quantitation of the target analytes, even in the presence of unexpected compounds from dirty water samples. The following maximum levels of veterinary drugs were found (in ng mL^-1): imidaclopril, 7; albendazole, 46; fenbendazole, 21; praziquantel, 29; fipronil, 29 and permethrin, 217.

Simultaneous Determination of Medicinal Drugs with Overlapping Profiles Contained in Low Chromatographic Resolution Data using HPLC-DAD and Multivariate Curve Resolution

Background:

The increasing demand of effective pharmaceutical products directed to fight against malaria lead to the combination of at least two antimalarial drugs. This combination aims to minimize the Plasmodium falciparum resistance which is found when the most frequently used drugs are taken individually. Within this context, proguanil hydrochloride and chloroquine phosphate which have independent modes of action are taken together to prevent malaria. This paper aims to develop a fast and powerful analytical method for the simultaneous determination of proguanil hydrochloride and chloroquine phosphate in the commercial Paludrine/Avloclor dosage forms using a multi-way chromatographic calibration based on high-performance liquid chromatography with diode array detection (HPLC-DAD) and multivariate curve resolution – alternating leastsquares (MCR-ALS).

Methods:

A rapid and powerful analytical method based on HPLC-DAD and MCR-ALS was developed for the simultaneous quantification of proguanil hydrochloride and chloroquine phosphate in the commercial Paludrine/Avloclor antimalarial drugs. An isocratic mobile phase composed by 0.2 M ammonium acetate, acetonitrile, and methanol (40:25:35) and a flow rate of 1.2 mL min-1 were employed in the chromatographic runs with an elution time about 5 min.

Results:

This approach demonstrates that chromatographic analysis may become considerably simpler and economical in terms of time, cost, and organic solvent consumption when coupled to multiway calibration models such as MCR-ALS. In fact, this multi-way chromatographic calibration based on second-order HPLC-DAD data matrices (with extremely low chromatographic resolution) and MCR-ALS allows the development of greener analytical methods for complex samples. The proposed analytical method allowed the simultaneous quantification of two antimalarial APIs present in the commercial Paludrine/Avloclor drugs with low REP values below 8% for the simultaneous determination of proguanil hydrochloride and chloroquine phosphate.

Conclusion:

The proposed multi-way chromatographic strategy can be used for routine control of pharmaceutical dosage forms. It should be highlighted that MCR-ALS allowed to: (a) achieve the second- order advantage and the quantification of analytes in the presence of uncalibrated compounds such as coeluted profile measured in different magnitude of the signal in each successive chromatographic run and significant overlapping profiles and (b) separate the contribution of several components from chromatographic runs with extremely low separation of peaks through the deconvolution of the signal obtained, performing the so-called mathematical chromatography.

Portable and benchtop Raman spectrometers coupled to cluster analysis to identify quinine sulfate polymorphs in solid dosage forms and antimalarial drug quantification in solution by AuNPs-SERS with MCR-ALS

This paper proposes for the first time: (a) a qualitative analytical method based on portable and benchtop backscattering Raman spectrometers coupled to hierarchical cluster analysis (HCA) and multivariate curve resolution - alternating least-squares (MCR-ALS) to identify two polymorphs of antimalarial quinine sulfate in commercial pharmaceutical tablets in their intact forms and (b) a quantitative analytical method based on gold nanoparticles (AuNPs) as active substrates for surface-enhanced Raman scattering (SERS) in combination with MCR-ALS to quantify quinine sulfate in commercial pharmaceutical tablets in solution. The pure concentration and spectral profiles recovered by MCR-ALS proved that both formulations present different polymorphs. These results were also confirmed by two clusters observed in the HCA model, according to their similarities within and among the samples that provided useful information about the homogeneity of different pharmaceutical manufacturing processes. AuNPs-SERS coupled to MCR-ALS was able to quantify quinine sulfate in the calibration range from 150.00 to 200.00 ng mL-1 even with the strong overlapping spectral profile of the background SERS signal, proving that it is a powerful ultrahigh sensitivity analytical method. This reduced linearity was validated throughout a large calibration range from 25.00 to 175.00 μg mL-1 used in a reference analytical method based on high performance liquid chromatography with a diode array detector (HPLC-DAD) coupled to MCR-ALS for analytical validation purposes, even in the presence of a coeluted compound. The analytical methods developed herein are fast, because second-order chromatographic data and first-order SERS spectroscopic data were obtained in less than 6 and 2 min, respectively. Concentrations of quinine sulfate were estimated with low root mean square error of prediction (RMSEP) values and a low relative error of prediction (REP%) in the range 1.8-4.5%.

Bioaccumulation and glutathione S-transferase activity on Rhinella arenarum tadpoles after short-term exposure to antiretrovirals

The aim of the present study was to investigate the bioaccumulation and toxicological effects of four antiretrovirals (lamivudine, stavudine, zidovudine and nevirapine) on Rhinella arenarum tadpoles, after short-term (48 h) exposure to these drugs at sublethal concentrations. The analytical procedure involved a simple extraction method followed by ultra-high performance liquid chromatography with diode array detection and chemometric analysis for data processing. Under the conditions studied, the analytes investigated, particularly nevirapine, showed possible bioaccumulation in tadpoles. Besides, an increase in the bioaccumulation was observed when increasing the exposure concentration. In addition, the enzymatic biomarkers measured to evaluate the toxicological effects showed that acethylcholinesterase activity was similar to that of the control group, while glutathione S-transferase activity was increased, indicating potential oxidative stress damage. Our results also allowed demonstrating the usefulness of chemometric algorithms to quantitate analytes in complex matrices, such as those absorbed by tadpoles in aquatic ecosystems. The results also evidenced the short-term antiretroviral bioaccumulation in tadpoles and the alteration of antioxidant systems, highlighting the need of environmental studies to elucidate the ecotoxicological risk of antiretrovirals in humans and wildlife.

Recent applications of chemometrics in one- and two-dimensional chromatography

The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

Analysis of Carcinogenic Polycyclic Aromatic Hydrocarbons in the Complex Background of the Petroleum Fluorescence Using Multivariate Curve Resolution Alternating Least Square and Total Synchronous Fluorescence Spectroscopic Technique

Multivariate curve resolution alternating least square (MCR-ALS) analysis allows the simultaneous retrieval of pure concentration and spectral profiles for each of the analysed chemical components from the composite spectrum even in the presence of unknown interferences. Total synchronous fluorescence spectroscopy (TSFS), a multidimensional fluorescence technique that describes the variation of synchronous fluorescence profile acquired as a function of increasing offset, has become a useful analytical technique. Suitably arranged TSFS data set can be easily processed using MCR-ALS and thereby a simple and sensitive analytical tool could be developed. The present work successfully used the combination of the MCR-ALS and TSFS to analyse the three carcinogenic and mutagenic polycyclic aromatic hydrocarbons (PAHs) namely Benzo[a]Pyrene, Chrysene and Pyrene in the presence of complex fluorescence background originated from petroleum product. MCR-ALS assisted TSFS can be used for the routine analyses of these carcinogenic PAHs to ensure the quality of water and other samples belonging to different part of the ecosystem.

Analytical Figures of Merit for Multisensor Arrays

Multisensor arrays employing various sensing principles are a rapidly developing field of research as they allow simple and inexpensive quantification of various parameters in complex samples. Quantitative analysis with such systems is based on multivariate regression techniques, and deriving of traditional analytical figures of merit (e.g., sensitivity, selectivity, limit of detection, and limit of quantitation) for such systems is not obvious and straightforward. Nevertheless, it is absolutely needed for further development of the multisensor research field and for introducing these instruments into the general context of analytical chemistry. Here, we report on the protocol for calculation of sensitivity, selectivity, and detection limits for multisensor arrays. The results are provided and discussed in detail for several real-world data sets.

Occurrence, Distribution, and Ecological Risk of Fluoroquinolones in Rivers and Wastewaters

The use of fluoroquinolones for the treatment of infections in humans and animals has increased in Argentina, and they can be found in large amounts in water bodies. The present study investigated the occurrence and associated ecological risk of 5 fluoroquinolones in rivers and farm wastewaters of San Luis, Santa Fe, Córdoba, Entre Ríos, and Buenos Aires provinces of Argentina by high-performance liquid chromatography coupled to fast-scanning fluorescence detection and ultra-high-performance liquid chromatography coupled to triple quadrupole mass spectrometry detection. The maximum concentrations of ciprofloxacin, enrofloxacin, ofloxacin, enoxacin, and difloxacin found in wastewater were 1.14, 11.9, 1.78, 22.1, and 14.2 μg L^-1 , respectively. In the case of river samples, only enrofloxacin was found, at a concentration of 0.97 μg L^-1 . The individual risk of aquatic organisms associated with water pollution due to fluoroquinolones was higher in bacteria, cyanobacteria, algae, plants, and anurans than in crustaceae and fish, with, in some cases, risk quotients >1. The proportion of samples classified as high risk was 87.5% for ofloxacin, 63.5% for enrofloxacin, 57.1% for ciprofloxacin, and 25% for enoxacin. Our results suggest that the prevalence of fluoroquinolones in water could be potentially risky for the aquatic ecosystem, and harmful to biodiversity. Environ Toxicol Chem 2019;38:2305-2313. © 2019 SETAC.

Rational design of mixtures for chromatographic peak tracking applications via multivariate selectivity

Chromatographic characterization and parameterization studies targeting many solutes require the judicious choice of operating conditions to minimize analysis time without compromising the accuracy of the results. To minimize analysis time, solutes are often grouped into a small number of mixtures; however, this increases the risk of peak overlap. While multivariate curve resolution methods are often able to resolve analyte signals based on their spectral qualities, these methods require that the chromatographically overlapped compounds have dissimilar spectra. In this work, a strategy for grouping compounds into sample mixtures containing solutes with distinct spectral and, optionally, with distinct chromatographic properties, in order to ensure successful solute resolution either chromatographically or with curve resolution methods is proposed. We name this strategy rational design of mixtures (RDM). RDM utilizes multivariate selectivity as a metric for making decisions regarding group membership (i.e., whether to add a particular solute to a particular sample). A group of 97 solutes was used to demonstrate this strategy. Utilizing both estimated chromatographic properties and measured spectra to group these 97 analytes, only 12 groups were required to avoid a situation where two or more solutes in the same group could not be resolved either chromatographically (i.e., they have significantly different retention times) or spectrally (i.e., spectra are different enough to enable resolution by curve resolution methods). When only spectral properties were utilized (i.e., the chromatographic properties are unknown ahead of time) the number of groups required to avoid unresolvable overlaps increased to 20. The grouping strategy developed here will improve the time and instrument efficiency of studies that aim to obtain retention data for solutes as a function of operating conditions, whether for method development or determination of the chromatographic parameters of solutes of interest (e.g., k_w).

•

A strategy for rational mixture design for chromatographic experiments is described.

•

Multivariate selectivity is used to optimize mixture compositions.

•

UV spectral information is used to distinguish overlapped chromatographic peaks.

A novel impedance enhancer for amperometric biosensor based ultrasensitive detection of matrix metalloproteinase-2

The detection range and sensitivity is crucial to the determination of tumor markers. For amperometric biosensors, the detection range relies on the initial current signals (I₀) and the sensitivity is tightly related to the current signals difference (ΔI) caused by per unit concentration target. Herein, an amperometric biosensor was fabricated using polyaniline gel as substrate and CS-AuNPs-Pb²⁺ as impedance enhancer. The sensing substrate exhibited strong current signal in [Fe(CN)₆]^3-/4- due to the excellent conductivity and large specific surface area of polyaniline gel. CS-AuNPs-Pb²⁺ with large hindrance effect can significantly increase interfacial resistance, resulting in the enhancement of ΔI. In addition, Pb²⁺ can react with sodium tartrate to produce non-conducting sodium tartrate gel on sensing interface, leading to further amplification of ΔI. Matrix metalloproteinase-2 (MMP-2) was analysed to prove the feasibility of this strategy. The proposed amperometric biosensor reveal both wide linear detection range from 1 pg mL^-1 to 1 μg mL^-1 and high sensitivity of 28.4 μA·(LgC_MMP-2)^-1. Therefore, this strategy will be of great significance to design other ultrasensitive amperometric biosensors.

A novel fluorimetric method for glyphosate and AMPA determination with NBD-Cl and MCR-ALS

We report the development of a new analytical method for the quantification of N-(phosphonomethyl)glycine (glyphosate) and (aminomethyl)phosphonic acid (AMPA) by combining spectrofluorimetry and multivariate calibration. In this study, fluorescence spectroscopy was used to quantify glyphosate and AMPA, which were previously derivatized with the fluorogenic reagent: 4-chloro-7-nitrobenzofurazan (NBD-Cl). Fluorescence excitation-emission matrices (EEM) were recorded by exciting between 400 and 500 nm, and measuring the emission between 500 and 610 nm. The second-order data obtained were processed using the Multivariate Curve Resolution with Alternating Least Square (MCR-ALS) methodology. The developed method was used to predict different concentrations of glyphosate and AMPA in validation samples. In addition, the presence of the herbicide was evaluated in real samples: a commercial formulation and a water sample from a cultivated area. For this purpose, the standard addition method was used to study the matrix effect in each case. The ranges of working concentrations obtained for this new method are in agreement with the amounts found in surface water samples near a direct sowing soybean growing region in Argentina.

Online Third-Order Liquid Chromatographic Data with Native and Photoinduced Fluorescence Detection for the Quantitation of Organic Pollutants in Environmental Water

Third-order liquid chromatographic data were generated online for the simultaneous quantitation of six organic environmental pollutants. The employed strategy consists in reducing the linear flow rate at the column outlet. A postcolumn UV reactor and a fluorimetric detector allowed to properly record both photoinduced and native excitation-emission fluorescence matrices (EEPIFMs and EEFMs, respectively). The obtained third-order liquid chromatography data were chemometrically processed with the multivariate curve resolution-alternating least-squares model. The sensitivity of the overall analytical method was enhanced by a very simple solid-phase extraction with C18 membranes, to be able to successfully apply it to natural water samples tested as real matrices. Favorable detection limits for the investigated pollutants, ranging from 0.02 to 0.27 ng mL^-1, were attained, with relative prediction errors between 2 and 7%. Since the studied samples contain uncalibrated interferents, the applied strategy achieves the second-order advantage. Implications regarding the potential achievement of the third-order advantage are discussed.

Random initialisation of the excitation-emission matrix fluorescence spectral variables in constraint fashion for subsequent multivariate curve resolution alternating least square analysis on a peculiarly designed calibration set: Simultaneous sensing of nine polycyclic aromatic hydrocarbons in water samples

Polycyclic aromatic hydrocarbons (PAHs) are carcinogenic and mutagenic in nature therefore their sensing in water sample is an important analytical task. In the present work, a novel approach that is based on the random initialisation of the excitation-emission matrix fluorescence (EEMF) spectral variables in constraint fashion for subsequent multivariate curve resolution alternating least Square (MCR-ALS) analysis is introduced for simultaneously sensing the complex dilute aqueous mixture of PAHs. The usefulness of the proposed analytical approach is successfully demonstrated by applying it intentionally on a calibration set that is peculiar in many senses. The peculiarity mainly arises because the designed (i) the calibration set consist of nine PAHS having significant spectral overlap, (ii) the concentration of each PAH in different samples are kept constant and (iii) any two samples differ only in the presence and absence of the PAHs. The proposed approach is found to make precise and accurate estimation of each of the nine PAHs without involving any pre-separation. In summary, the proposed approach provides a simple and cost-effective procedure for simultaneous sensing of several PAHs in water samples. The proposed approach could be very useful in developing countries.

Multivariate curve resolution applied to kinetic-spectroscopic data matrices: Dye determination in foods by means of enzymatic oxidation

In this work, the combination of chemometric techniques with kinetic-spectroscopic data allowed quantifying two dyes (tartrazine and carminic acid) in complex matrices as mustard, ketchup, asparagus soup powder, pumpkin soup powder, plum jam and orange-strawberry juice. Quantitative analysis was performed without the use of tedious sample pretreatment, due to the achievement of the second-order advantage. The results obtained showed an improvement in simplicity, speed and cost with respect to usual separation techniques, allowing to properly quantifying these dyes obtaining limits of detection below 0.6mgL^-1. In addition, to the best of our knowledge, is the first time that kinetic-spectroscopic data are obtained from the action of laccase for analytical purposes.