Removal of enrofloxacin using Eichhornia crassipes in microcosm wetlands.

The global consumption of antibiotics leads to their possible occurrence in the environment. In this context, nature-based solutions (NBS) can be used to sustainably manage and restore natural and modified ecosystems. In this work, we studied the efficiency of the NBS free-water surface wetlands (FWSWs) using Eichhornia crassipes in microcosm for enrofloxacin removal. We also explored the behavior of enrofloxacin in the system, its accumulation and distribution in plant tissues, the detoxification mechanisms, and the possible effects on plant growth. Enrofloxacin was initially taken up by E. crassipes (first 100 h). Notably, it accumulated in the sediment at the end of the experimental time. Removal rates above 94% were obtained in systems with sediment and sediment + E. crassipes. In addition, enrofloxacin was found in leaves, petioles, and roots (8.8-23.6 µg, 11-78.3 µg, and 10.2-70.7 µg, respectively). Furthermore, enrofloxacin, the main degradation product (ciprofloxacin), and other degradation products were quantified in the tissues and chlorosis was observed on days 5 and 9. Finally, the degradation products of enrofloxacin were analyzed, and four possible metabolic pathways of enrofloxacin in E. crassipes were described.

Binding the gap between experiments, statistics, and method comparison: A tutorial for computing limits of detection and quantification in univariate calibration for complex samples.

The present tutorial aims to review the most frequently reported criteria for the calculation of the limits of detection (LOD) and quantification (LOQ) in univariate calibration, summarizing their fundamentals, advantages, and limitations. The current criteria for estimating LOD and LOQ are based on diverse theoretical and/or empirical assumptions and require different amounts of experimental data, making the calculation rather complex in some cases. Moreover, alternative forms for calculating LOD/LOQ frequently lead to dissimilar results. This scenario might worsen in the case of complex analytical systems. Throughout this tutorial, different forms of calculating LOD/LOQ are illustrated using previously reported experimental datasets in the environmental chemistry field as examples. The influence of the sample matrix during the estimation of LOD/LOQ parameters is investigated through one calibration approache. The discrepancies in the obtained results with different criteria for the calculation of LOD/LOQ are highlighted. Finally, general guidelines and recommendations regarding experimental and data processing issues are proposed, aiming to promote fair criteria for the comparison of different analytical methodologies in terms of prediction ability and detection capability.

Natural deep eutectic solvent: A novelty alternative as multi-walled carbon nanotubes dispersing agent for the determination of paracetamol in urine.

This work reports for the first time the analytical performance of glassy carbon electrodes (GCE) modified with a dispersion of multi-wall carboxylated carbon nanotubes (MWCNTs-COOH) using a mixture of a natural deep eutectic solvent (NADES - LGH/lactic acid-glucose-water), ethylene glycol (EG) and water (GCE/MWCNT-LGH-EG) for the determination and N-(4-hydroxyphenyl) acetamide (paracetamol) (APAP) in urine samples. The optimization of both dispersion and measurement conditions was carried out using experimental design. The modified electrode exhibited enhanced current responses, demonstrating excellent electrochemical response towards APAP oxidation compared to MWCNTs-LGH, MWCNTs-EG, MWCNTs-H2O-EtOH and MWCNTs-H2O. The linear dependence between the anodic peak currents and the square root of scan rates over the range of 0.010-0.300 Vs-1 demonstrates that the electro oxidation of APAP occurs under diffusional control. The MWCNT-LGH-EG modified GCE displayed an analytical sensitivity of 10.72 mL µg-1 (r = 0.9994) and a detection limit of 100 ng mL-1 for the selective determination of APAP in urine samples. The proposed electrochemical sensor was successfully applied for quantifying APAP in urine samples in the presence of uric acid. In addition, the accuracy and precision of the method was contrasted against a HPLC reference method.

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.

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.

Achieving the analytical second-order advantage with non-bilinear second-order data.

Multi-way calibration based on second-order data constitutes a revolutionary milestone for analytical applications. However, most classical chemometric models assume that these data fulfil the property of low rank bilinearity, which cannot be accomplished by all instrumental methods. Indeed, various techniques are able to generate non-bilinear data, which are all potentially useful for the development of novel second-order calibration methodologies. However, the achievement of the second-order advantage in these cases may be severely limited, since methods for comprehensive modelling of non-bilinear second-order data remain only partially explored. In this research, the analytical performance of three well-known second-order models, namely non-bilinear rank annihilation (NBRA), unfolded partial least-squares with residual bilinearization (U-PLS-RBL) and multivariate curve resolution - alternating least-squares (MCR-ALS) is systematically assessed through sets of simulated and experimental non-bilinear second-order data, involving one analyte and one interferent. Although it is not possible to establish a single strategy to model any type of non-bilinear second-order data with the studied methods, each approach may lead to successful predictions under certain circumstances. It is shown that the prediction capacity is severely affected by data properties such as the level of instrumental noise, the rank of the response matrices and the signal selectivity pattern of the analyte.

Resolving interactions of miglitol with normal and glycated human serum albumin by multivariate methods.

This article reports results of one of our projects related to the investigation of interactions of miglitol (MIG) with normal human serum albumin (HSA) and glycated HSA (GHSA) with the help of recording spectroscopic and electrochemical data. The experimental data were analyzed by conventional and chemometric methods to extract useful information for comprehensive justifications of the interactions of the MIG with HSA and GHSA. Hard- and soft-modeling chemometric methods were used to extract quantitative and qualitative information. Then, molecular docking techniques were used to further investigation of the binding of the MIG with HSA and GHSA and the extracted results were compatible with those obtained by experimental methods. Finally, according to the binding of the BV with HSA and GHSA, second-order differential pulse voltammetric data were recorded and calibrated with three-way calibration methods for exploiting second-order advantage for determination of the GHSA in the presence of the HSA to develop a novel chemometrics assisted-electroanalytical method for diagnostic and monitoring of diabetic.

Prospective inference of bioprocess cell viability through chemometric modeling of fluorescence multiway data.

In this investigation, the fermentation step of a standard mammalian cell-based industrial bioprocess for the production of a therapeutic protein was studied, with particular emphasis on the evolution of cell viability. This parameter constitutes one of the critical variables for bioprocess monitoring since it can affect downstream operations and the quality of the final product. In addition, when the cells experiment an unpredictable drop in viability, the assessment of this variable through classic off-line methods may not provide information sufficiently in advance to take corrective actions. In this context, Process Analytical Technology (PAT) framework aims to develop novel strategies for more efficient monitoring of critical variables, in order to improve the bioprocess performance. Thus, in this work, a set of chemometric tools were integrated to establish a PAT strategy to monitor cell viability, based on fluorescence multiway data obtained from fermentation samples of a particular bioprocess, in two different scales of operation. The spectral information, together with data regarding process variables, was integrated through chemometric exploratory tools to characterize the bioprocess and stablish novel criteria for the monitoring of cell viability. These findings motivated the development of a multivariate classification model, aiming to obtain predictive tools for the monitoring of future lots of the same bioprocess. The model could be satisfactorily fitted, showing the non-error rate of prediction of 100%.

Interference-free calibration with first-order instrumental data and multivariate curve resolution. When and why?

The possibility of building an interference-free calibration with first-order instrumental data with multivariate curve resolution-alternating least-squares (MCR-ALS) has been a recent topic of interest. When the protocols were successful, MCR-ALS proved to be suitable for the extraction of chemically meaningful information from first-order calibration datasets, even in the presence of unexpected species, i.e., constituents present in the test samples but absent in the calibration set. This may represent an interesting advantage over classical first-order models, e.g. partial least-squares regression (PLS). However, the predictive capacity of MCR-ALS models can be severely affected by rotational ambiguity (RA), which is usually present in first-order datasets when interferents occur, and has not been always characterized in the published analytical protocols. The aim of this report is to discuss important issues regarding MCR-ALS modelling of first-order data for a calibration scenario with a single analyte and one interferent through simulated and experimental data. Specifically, the question of when and why MCR-ALS allows one to build interference-free calibration models with first-order data is studied in terms of signal overlapping, extent of RA, and especially the role of ALS initialization procedures in prediction performance. The aim is to alert analytical chemists that interference-free MCR-ALS with first-order data may not always be successful.

Chemometric modeling of the electrochemical data to investigate proline cis/trans isomeration effect on aggregation of Tau protein.

Tau protein (Tau) is a proline-rich protein and in this work, we have developed a very interesting strategy based on combination of electrochemistry with chemometric methods to investigate proline cis/trans isomeration effect on the Tau aggregation. To achieve this goal, the proline residues at RTPPK motif have been replaced by alanine to generate RTPAK, RTAPK and RTAAK mutants of the Tau. Then, cyclic voltammetric (CV) responses of the Tau and RTPAK, RTAPK and RTAAK as its mutants in the presence of heparin (HEP) as an anionic inducing agent which could trigger aggregation of the Tau were recorded at physiological conditions every hour during 12 h. Therefore, 48 data sets of titrations were obtained which were handled by chemometric methods to extract useful information about aggregation of the Tau. The data were hard-modeled by EQUISPEC, SQUAD, REACTLAB and SPECFIT to extract useful quantitative information. Our results confirmed that the strength of the binding of the HEP with proteins was obeyed from Tau > RTPAK ~ RTAPK > RTAAK which confirmed that the aggregation of the proteins was obeyed from this order as well. Therefore, aggregation of the Tau is decreased by transforming Cis isomer to Trans even in the presence of an anionic inducing agent such as HEP which may have value for the treatment of Alzheimer's disease.

Exploring the potential of combining chemometric approaches to model non-linear multi-way data with quantitative purposes - A case study.

Second-order based calibration methods have been widely investigated capitalizing on the inherent benefits of the data structure and the decomposition models, demonstrating that second-order advantage is a property that conspires to a high likelihood success in the resolution of systems of varying complexity. This work aims to demonstrate the applicability of a combined chemometric strategy to solve non-linear multivariate calibration systems in the presence of non-multilinear multi-way data. The determination of histamine by differential pulse voltammetry at different pH is presented as case study. The experimental system has the outstanding difficulty arisen from the large displacement along the potential axis by the pH, which was successfully overcome by implementation of the presented combined strategy. For data modeling, MCR-ALS, U-PLS/RBL and U-PCA/RBL-RBF were used. MCR-ALS allowed unraveling the non-linear behavior between the signal and the concentration, and extracting the underlying profiles of the constituent. Quantitative analysis was performed through the three models, and a comparative evaluation of the predictive performance was done. The best results were achieved with U-PCA/RBL-RBF (mean recovery = 101%) whereas, MCR-ALS yield the lowest mean recovery for all samples (70%).

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.

Multi-level data fusion strategies for modeling three-way electrophoresis capillary and fluorescence arrays enhancing geographical and grape variety classification of wines.

Capillary electrophoresis with diode array detection (CE-DAD) and multidimensional fluorescence spectroscopy (EEM) second-order data were fused and chemometrically processed for geographical and grape variety classification of wines. Multi-levels data fusion strategies on three-way data were evaluated and compared revealing their advantages/disadvantages in the classification context. Straightforward approaches based on a series of data preprocessing and feature extraction steps were developed for each studied level. Partial least square discriminant analysis (PLS-DA) and its multi-way extension (NPLS-DA) were applied to CE-DAD, EEM and fused data matrices structured as two-way and three-way arrays, respectively. Classification results achieved on each model were evaluated through global indices such as average sensitivity non-error rate and average precision. Different degrees of improvement were observed comparing the fused matrix results with those obtained using a single one, clear benefits have been demonstrated when level of data fusion increases, achieving with the high-level strategy the best classification results.

Sensitivity for Multivariate Calibration Based on Multilayer Perceptron Artificial Neural Networks.

The use of machine learning for multivariate spectroscopic data analysis in applications related to process monitoring has become very popular since non-linearities in the relationship between signal and predicted variables are commonly observed. In this regard, the use of artificial neural networks (ANN) to develop calibration models has demonstrated to be more appropriate and flexible than classical multivariate linear methods. The most frequently reported type of ANN is the so-called multilayer perceptron (MLP). Nevertheless, the latter models still lack a complete statistical characterization in terms of prediction uncertainty, which is an advantage of the parametric counterparts. In the field of analytical calibration, developments regarding the estimation of prediction errors would derive in the calculation of other analytical figures of merit (AFOMs), such as sensitivity, analytical sensitivity, and limits of detection and quantitation. In this work, equations to estimate the sensitivity in MLP-based calibrations were deduced and are here reported for the first time. The reliability of the derived sensitivity parameter was assessed through a set of simulated and experimental data. The results were also applied to a previously reported MLP fluorescence calibration methodology for the biopharmaceutical industry, yielding a value of sensitivity ca. 30 times larger than for the univariate reference method.

Modelling of bioprocess non-linear fluorescence data for at-line prediction of etanercept based on artificial neural networks optimized by response surface methodology.

In the last years, regulatory agencies in biopharmaceutical industry have promoted the design and implementation of Process Analytical Technology (PAT), which aims to develop rapid and high-throughput strategies for real-time monitoring of bioprocesses key variables, in order to improve their quality control lines. In this context, spectroscopic techniques for data generation in combination with chemometrics represent alternative analytical methods for on-line critical process variables prediction. In this work, a novel multivariate calibration strategy for the at-line prediction of etanercept, a recombinant protein produced in a mammalian cells-based perfusion process, is presented. For data generation, samples from etanercept processes were daily obtained, from which fluorescence excitation-emission matrices were generated in the spectral ranges of 225.0 and 495.0 nm and 250.0 and 599.5 nm for excitation and emission modes, respectively. These data were correlated with etanercept concentration in supernatant (measured by an off-line HPLC-based reference univariate technique) by implementing different chemometric strategies, in order to build predictive models. Partial least squares (PLS) regression evidenced a non-linear relation between signal and concentration when observing actual vs. predicted concentrations. Hence, a non-parametric approach was implemented, based on a multilayer perceptron artificial neural network (MLP). The MLP topology was optimized by means of the response surface methodology. The prediction performance of MLP model was superior to PLS, since the first is able to cope with non-linearity in calibration models, reaching percentage mean relative error in predictions of about 7.0% (against 12.6% for PLS). This strategy represents a fast and inexpensive approach for etanercept monitoring, which conforms the principles of PAT.

Coupling of digital image processing and three-way calibration to assist a paper-based sensor for determination of nitrite in food samples.

In this work, a novel and very interesting analytical methodology based on coupling of digital image processing and three-way calibration has been developed for determination of nitrite in food samples. Nitrite in contact with Griess reagent is able to produce a red-colored azo dye whose color intensity is correlated with nitrite concentration and here, a piece of Whatman filter paper impregnated with Griess reagent was used as the platform of the sensor and a SONY Xperia Z5 cell phone was used for image capturing from the sensor surface. To generate second-order data, the F-number of the camera's sensor was changed as an instrumental parameter. Two calibration models were constructed by unfolded partial least squares-residual bilinearization (U-PLS/RBL) and multiway-PLS/RBL (N-PLS/RBL) and then, their performance for prediction of nitrite concentration in test samples was evaluated and the results confirmed a good performance for U-PLS/RBL (REP = 3.25 ppm, RMSEP = 8.82 ppm, RMSEC = 4.62 ppm, Q 2 = 0.99, γ -1 = 0.05 and LOD = 0.1 ppm) which was better than that for N-PLS/RBL (REP = 13.98 ppm, RMSEP = 37.86 ppm, RMSEC = 6.46 ppm, Q 2 = 0.98, γ -1 = 0.07 and LOD = 0.15 ppm) in predicting concentration of nitrite in test samples which motivated us to choose it for the analysis of cabbage, carrot, lettuce, watermelon, onion, potato, kielbasa and sausage as real samples.

Enhancement of multianalyte mass spectrometry detection through response surface optimization by least squares and artificial neural network modelling.

In this work, the use of design of experiments and posterior data modelling by artificial neural network (ANN) and least squares (LS) is presented as a suitable analytical tool for the performance optimization of a tandem mass spectrometric detector coupled to ultra-high performance liquid chromatography for the analysis of seventeen veterinary drugs. Firstly, a central composite design was built considering as factors the cone, capillary, extractor and radio frequency voltages of the mass spectrometer in order to obtain a proper combination to improve the sensitivity of the method. Secondly, a one factor design considering the collision voltage was built to define the adequate voltage for each daughter ion. The response surface methodology (RSM) was then applied, and the prediction capability of ANN and LS were compared. As conclusion, the ANN modelling provided better results than LS, both in terms of the ANOVA and predicted areas results. The accuracy of the model prediction was between 85 and 125%, confirming that the estimates of the model were correct, and endorsing the optimization procedure as a suitable way to gather excellent results. The suitability of the new approach and its implications on the simultaneous analysis of seventeen veterinary drugs by ultra-high liquid chromatography coupled to tandem mass spectrometry detection are discussed.

Developing an elegant and integrated electrochemical-theoretical approach for detection of DNA damage induced by 4-nonylphenol.

In this work, a novel biosensor was fabricated for detection of DNA damage induced by 4-nonylphenol (NP) and also determination of NP. To achieve this goal, a glassy carbon electrode (GCE) was modified with chitosan (Chit), gold nanoparticles (Au NPs) and DNA-multiwalled carbon nanotubes (DNA-MWCNTs). Then, the DNA-MWCNTs/Au NPs/Chit/GCE was incubated with methylene blue (MB) to obtain MB-DNA-MWCNTs/Au NPs/Chit/GCE in which MB was used as the redox indicator. The modifications applied to the GCE were characterized by cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopic (EDS) and theoretical evidence. MB is a derivative of anthraquinone which can intercalate into double helix structure of DNA. By treating MB-DNA-MWCNTs/Au NPs/Chit/GCE with NP, a higher R ct was observed because the insertion of the NP may result in a more negative charge environment on the DNA surface which hinders accessibility of [Fe(CN)6]3-/4- anion to the electrode surface. Change in the EIS response of the biosensor in the presence of NP was used to develop a novel system for monitoring the DNA damage induced by NP. The EIS technique was also used to develop a sensitive electroanalytical method for determination of NP.

Four- and five-way excitation-emission luminescence-based data acquisition and modeling for analytical applications. A review.

The latest advances in both theory and experimental procedures on third-order/four-way and fourth-order/five-way calibration methods are discussed. This report is focused on excitation-emission (fluorescence and phosphorescence) matrices generation, employing different variables as the third data mode (time retention in chromatography, pH gradient, fluorescence/phosphorescence lifetime, kinetics, or other chemical treatments). Fully capitalizing on the second-order advantage, it has been possible to develop appealing analytical applications in spite of the complexity of the data. Extraction of the significant chemical information about the system under study as well as the individual abundance of the contributing constituents after proper higher-order data decomposition has allowed to analytical researchers performing quantitative analysis of complex samples. The experimental works reported up to the present are introduced and discussed in order to illustrate concepts. Throughout this work, the analytical benefits achieved by modeling third- and fourth-order data are exposed, attempting to contribute to the ongoing debate in the chemometric community regarding the existence and the true nature of the third-order advantage.

Matrix augmentation as an efficient method for resolving interaction of bromocriptine with human serum albumin: trouble shooting and simultaneous resolution.

This work reports the results of an interesting study related to the investigation of interactions of bromocriptine (BCP) with human serum albumin (HSA) by mathematicall modelling of voltammetric and spectroscopic data into an augmented data matrix and its resolution by multivariate curve resolution-alternating least squares (MCR-ALS). The quality of the results obtained by MCR-ALS was examined by MCR-BANDS and its outputs confirmed the absence of rotational ambiguities in the MCR-ALS results. BCP-HSA interactions were also modeled by molecular docking methods to verify the results obtained from experimental sections and fortunately, they were compatible. Hard modeling of the experimental data by EQUISPEC helped us to calculate the binding constant of the complex formed from BCP-HSA interactions which was in a good agreement with that of calculated from direct analysis of the experimental data. Finally, with the help of two different amperometric measurements based on BCP-HSA interactions a novel electroanalytical method was developed for biosensing of HSA in serum samples.