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The application of agrochemicals in citrus fruits is widely used to improve the quality of crops, increase production yields, and prolong post-harvest life. However, these substances are potentially toxic for humans and the ecosystem due to their widespread use, high stability, and bioaccumulation. Conventional techniques for determining pesticide residues in citrus fruits are chromatographic methods coupled with different detectors. However, in recent years, the need for analytical strategies that are less polluting for the environment has encouraged the appearance of new alternatives, such as sensors and biosensors, which allow selective and sensitive detection of pesticide residues in real time. A comprehensive overview of the analytical platforms used to determine pesticide residues in citrus fruits and citrus-derived products is presented herein. The review focuses on the evolution of these methods since 2015, their limitations, and possible future perspectives for improving pesticide residue determination and reducing environmental contamination.

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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.

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This study aimed to assess whether perinatal exposure to propiconazole (PRO), glyphosate (GLY) or their mixture (PROGLY) alters key endocrine pathways and the development of the male rat mammary gland. To this end, pregnant rats were orally exposed to vehicle, PRO, GLY, or a mixture of PRO and GLY from gestation day 9 until weaning. Male offspring were euthanized on postnatal day (PND) 21 and PND60. On PND21, GLY-exposed rats showed reduced mammary epithelial cell proliferation, whereas PRO-exposed ones showed increased ductal p-Erk1/2 expression without histomorphological alterations. On PND60, GLY-exposed rats showed reduced mammary gland area and estrogen receptor alpha expression and increased aromatase expression, whereas PRO-exposed ones showed enhanced lobuloalveolar development and increased lobular hyperplasia. However, PROGLY did not modify any of the endpoints evaluated. In summary, PRO and GLY modified the expression of key molecules and the development of the male mammary gland individually but not together.

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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.

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The ecological risks posed by two ß-diketone antibiotics (DKAs, enrofloxacin, ENR and ciprofloxacin, CPX), characterized by their long persistence in aqueous environments and known deleterious effect on model organisms such as zebrafish were analysed using Rhinella arenarum larvae. Sublethal tests were conducted using environmentally relevant concentrations of both ENR and CPX (1-1000µgL-1) under standard laboratory conditions for 96h. Biological endpoints and biomarkers evaluated were body size, shape, development and growth rates, and antioxidant enzymes (glutathione-S-transferase, GST; Catalase, CAT). Risk assessment was analysed based on ration quotients (RQ). The size and shape measurements of the larvae exposed to concentrations greater than 10µgL-1 of CPX were lower compared to controls (Dunnett post hoc p<0.05) and presented signs of emaciation. Concentrations of 1000µgL-1of CPX induced GST activity, in contrast with inhibited GST and CAT of larvae exposed to ENR. Risk assessments indicated that concentrations greater than or equal to10µgL-1 of CPX and ENR are ecotoxic for development, growth, detoxifying, and oxidative stress enzymes. It is suggested that additional risk assessments may provide evidence of bioaccumulation of CPX and ENR in tissues or organs of amphibian larvae by mesocosm sediment test conditions. Finally, intestinal microbiome studies should be considered to establish the mechanisms of action of both antibiotics.

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A method for the detection and quantitation of Hg2+ in aqueous samples by fluorescence spectroscopy is presented. It consists of a turn-on sensor developed by coupling Gold nanoparticles (AuNPs) with the rhodamine 6G derivative FC1, in which the response is generated by a mercury-induced ring-opening reaction. The AuNPs were included in order to improve the sensitivity of the method towards the analyte, maintaining its high selectivity. The method was validated in terms of linearity, precision and accuracy, and applied to the quantitation of Hg2+ in Milli-Q and tap water with and without spiked analyte. The limit of detection and quantitation were 0.15 µg·L-1 and 0.43 µg·L-1, respectively, constituting a substantial improvement of sensitivity in comparison with the previously reported detection of Hg2+ with free FC1.

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A study regarding the acquisition and analytical utilization of four-way data acquired by monitoring excitation-emission fluorescence matrices at different elution time points in a fast HPLC procedure is presented. The data were modeled with three well-known algorithms: PARAFAC, U-PLS/RTL and MCR-ALS, the latter conveniently adapted to model third-order data. The second-order advantage was exploited when analyzing samples containing uncalibrated components. The best results were furnished with the algorithm U-PLS/RTL. This fact is indicative of both no peak time shifts occurrence among samples and high colinearity among spectra. Besides, this latent-variable structured algorithm is capable of better handle the need of achieving high sensitivity for the analysis of one of the analytes. In addition, a significant enhancement in both predictions and analytical figures of merit was observed for carbendazim, thiabendazole, fuberidazole, carbofuran, carbaryl and 1-naphtol, when going from second- to third-order data. LODs obtained were ranged between 0.02 and 2.4µgL(-1).

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The present study reports a sensitive chromatographic method for the analysis of seven fluoroquinolones (FQs) in environmental water samples, by coupling yttrium-analyte complex and three-way chromatographic data modeling. This method based on the use of HPLC-FSFD does not require complex or tedious sample treatments or enrichment processes before the analysis, due to the significant fluorescence increments of the analytes reached by the presence of Y(3+). Enhancement achieved for the FQs signals obtained after Y(3+) addition reaches 103- to 1743-fold. Prediction results corresponding to the application of MCR-ALS to the validation set showed relative error of prediction (REP%) values below 10% in all cases. A recovery study that includes the simultaneous determination of the seven FQs in three different environmental aqueous matrices was conducted. The recovery studies assert the efficiency and the accuracy of the proposed method. The LOD values calculated are in the order of part per trillion (below 0.5 ng mL(-1) for all the FQs, except for enoxacin). It is noteworthy to mention that the method herein proposed, which does not include pre-concentration steps, allows reaching LOD values in the same order of magnitude than those achieved by more sophisticated methods based on SPE and UHPLC-MS/MS.

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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.

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This paper presents the development of a capillary electrophoresis method with diode array detector coupled to multivariate curve resolution-alternating least squares (MCR-ALS) to conduct the resolution and quantitation of a mixture of six quinolones in the presence of several unexpected components. Overlapping of time profiles between analytes and water matrix interferences were mathematically solved by data modeling with the well-known MCR-ALS algorithm. With the aim of overcoming the drawback originated by two compounds with similar spectra, a special strategy was implemented to model the complete electropherogram instead of dividing the data in the region as usually performed in previous works. The method was first applied to quantitate analytes in standard mixtures which were randomly prepared in ultrapure water. Then, tap water samples spiked with several interferences were analyzed. Recoveries between 76.7 and 125 % and limits of detection between 5 and 18 µg L(-1) were achieved.

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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%.

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A methodology based on second-order data (excitation emission matrices) modeling with one of most popular algorithms presenting the second-order advantage, parallel factor analysis (PARAFAC), combined with transference of calibration is proposed to predict the analyte concentration when significant inner filter effects occur, even in the presence of unexpected sample components. The quantitation of phenylephrine hydrochloride (PHE) in water samples (concentrations ranged between 250 and 750 ng mL(-1)) in the presence of ibuprofen, acetyl salicylic acid and paracetamol (which produce inner filter effect across the useful wavelength range) was achieved. The strategy allows reducing the experimental work and increasing the analytical sensitivity in the determination of the analyte of interest in the presence of unexpected compounds and matrix effect caused by inner filter, avoiding the preparation of a large number of solutions and maintaining acceptable figures of merit. Recoveries between 97 and 102% for validation and real spiked water samples, respectively, and a relative prediction error of 5% were achieved. Results were compared with those obtained after the application of the classical standard addition method combined with PARAFAC, carrying out five additions to each sample, in triplicate. The presented methodology constitutes a simple and low-cost method for the determination of PHE in water samples with a considerable reduction in standard handling and time. This methodology can be extended to other systems presenting matrix effect and, consequently, can become in a useful tool to know the amount of pharmaceuticals in the aquatic environment and to evaluate the effect of conventional wastewater treatment plants in the elimination of pharmaceutical compounds.

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The performance of MCR-ALS was studied in the modeling of non-linear kinetic-spectrophotometric data acquired by a stopped-flow system for the quantitation of tartrazine in the presence of brilliant blue and sunset yellow FCF as possible interferents. In the present work, MCR-ALS and U-PCA/RBL were firstly applied to remove the contribution of unexpected components not included in the calibration set. Secondly, a polynomial function was used to model the non-linear data obtained by the implementation of the algorithms. MCR-ALS was the only strategy that allowed the determination of tartrazine in test samples accurately. Therefore, it was applied for the analysis of tartrazine in beverage samples with minimum sample preparation and short analysis time. The proposed method was validated by comparison with a chromatographic procedure published in the literature. Mean recovery values between 98% and 100% and relative errors of prediction values between 4% and 9% were indicative of the good performance of the method.

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Based on green analytical chemistry principles, an efficient approach was applied for the simultaneous determination of galantamine, a widely used cholinesterase inhibitor for the treatment of Alzheimer's disease, and its major metabolites in serum samples. After a simple serum deproteinization step, second-order data were rapidly obtained (less than 6 min) with a chromatographic system operating in the isocratic regime using ammonium acetate/acetonitrile (94:6) as mobile phase. Detection was made with a fast-scanning spectrofluorimeter, which allowed the efficient collection of data to obtain matrices of fluorescence intensity as a function of retention time and emission wavelength. Successful resolution was achieved in the presence of matrix interferences in serum samples using multivariate curve resolution-alternating least-squares (MCR-ALS). The developed approach allows the quantification of the analytes at levels found in treated patients, without the need of applying either preconcentration or extraction steps. Limits of detection in the range between 8 and 11 ng mL(-1), relative prediction errors from 7 to 12% and coefficients of variation from 4 to 7% were achieved.

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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 alpha-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.

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A fast chromatographic methodology is presented for the analysis of three synthetic dyes in non-alcoholic beverages: amaranth (E123), sunset yellow FCF (E110) and tartrazine (E102). Seven soft drinks (purchased from a local supermarket) were homogenized, filtered and injected into the chromatographic system. Second order data were obtained by a rapid LC separation and DAD detection. A comparative study of the performance of two second order algorithms (MCR-ALS and U-PLS/RBL) applied to model the data, is presented. Interestingly, the data present time shift between different chromatograms and cannot be conveniently corrected to determine the above-mentioned dyes in beverage samples. This fact originates the lack of trilinearity that cannot be conveniently pre-processed and can hardly be modelled by using U-PLS/RBL algorithm. On the contrary, MCR-ALS has shown to be an excellent tool for modelling this kind of data allowing to reach acceptable figures of merit. Recovery values ranged between 97% and 105% when analyzing artificial and real samples were indicative of the good performance of the method. In contrast with the complete separation, which consumes 10 mL of methanol and 3 mL of 0.08 mol L(-1) ammonium acetate, the proposed fast chromatography method requires only 0.46 mL of methanol and 1.54 mL of 0.08 mol L(-1) ammonium acetate. Consequently, analysis time could be reduced up to 14.2% of the necessary time to perform the complete separation allowing saving both solvents and time, which are related to a reduction of both the costs per analysis and environmental impact.

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Drug monitoring in serum samples was performed using second-order data generated by CE-DAD, processed with a suitable chemometric strategy. Carbamazepine could be accurately quantitated in the presence of its main metabolite (carbamazepine epoxide), other therapeutic drugs (lamotrigine, phenobarbital, phenytoin, phenylephrine, ibuprofen, acetaminophen, theophylline, caffeine, acetyl salicylic acid), and additional serum endogenous components. The analytical strategy consisted of the following steps: (i) serum sample clean-up to remove matrix interferences, (ii) data pre-processing, in order to reduce the background and to correct for electrophoretic time shifts, and (iii) resolution of fully overlapped CE peaks (corresponding to carbamazepine, its metabolite, lamotrigine and unexpected serum components) by the well-known multivariate curve resolution-alternating least squares algorithm, which extracts quantitative information that can be uniquely ascribed to the analyte of interest. The analyte concentration in serum samples ranged from 2.00 to 8.00 mg/L. Mean recoveries were 102.6% (s=7.7) for binary samples, and 94.8% (s=13.5) for spiked serum samples, while CV (%)=4.0 was computed for five replicate, indicative of the acceptable accuracy and precision of the proposed method.

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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.

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Loratadine (LOR) and pseudoephedrine sulfate (PES) were determined in pharmaceutical samples by using non-linear second-order data generated by a pH-gradient flow injection analysis (FIA) system with diode-array detection. Determination of both analytes was performed on the basis of differences between the acid-base and spectral features of each drug species. Non-linearities were detected by using both qualitative and quantitative tools. As a consequence of the non-linearity, a recently reported algorithm, artificial neural networks followed by residual bilinearization (ANN/RBL), was shown to furnish more satisfactory results. Recoveries of 99.7% (LOR) and 95.6% (PES) were obtained when analyzing a validation set containing unexpected components (the usual excipients found in pharmaceutical preparations). The average value obtained by implementation of the method on four replicates was compared with that obtained by a reference method based on HPLC (difference not significant; p > 0.05).

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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.

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