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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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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.

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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.

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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.

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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.

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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%.

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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.

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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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Higher-order data generation implies some automation challenges, which are mainly related to the hidden programming languages and electronic details of the equipment. When techniques and/or equipment hyphenation are the key to obtaining higher-order data, the required simultaneous control of them demands funds for new hardware, software, and licenses, in addition to very skilled operators. In this work, we present Design of Inputs-Outputs with Sikuli (DIOS), a free and open-source code program that provides a general framework for the design of automated experimental procedures without prior knowledge of programming or electronics. Basically, instruments and devices are considered as nodes in a network, and every node is associated both with physical and virtual inputs and outputs. Virtual components, such as graphical user interfaces (GUIs) of equipment, are handled by means of image recognition tools provided by Sikuli scripting language, while handling of their physical counterparts is achieved using an adapted open-source three-dimensional (3D) printer. Two previously reported experiments of our research group, related to fluorescence matrices derived from kinetics and high-performance liquid chromatography, were adapted to be carried out in a more automated fashion. Satisfactory results, in terms of analytical performance, were obtained. Similarly, advantages derived from open-source tools assistance could be appreciated, mainly in terms of lesser intervention of operators and cost savings.

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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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