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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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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.

Assuntos

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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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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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Temperature-induced conformational transitions of poly-l-lysine were monitored with Fourier-transform infrared (FT-IR) spectroscopy between 10°C and 70°C. Chemometric analysis of dynamic IR spectra was performed by multivariate curve analysis-alternating least squares (MCR-ALS) of the amide I' and amide II' spectral region. With this approach, the pure spectral and concentration profiles of the conformational transition were obtained. Beside the initial α-helical, the intermediate random coil/extended helices and the final ß-sheet structure, an additional intermediate PLL conformation was identified and attributed to a transient ß-sheet structure.

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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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This paper reports the modeling of excitation-emission matrices for classification of Argentinean white wines according to the grape variety employing chemometric tools for pattern recognition. The discriminative power of the data was first investigated using Principal Component Analysis (PCA) and Parallel Factor Analysis (PARAFAC). The score plots showed strong overlapping between classes. A forty-one samples set was partitioned into training and test sets by the Kennard-Stone algorithm. The algorithms evaluated were SIMCA, N- and U-PLS-DA and SPA-LDA. The fit of the implemented models was assessed by mean of accuracy, sensitivity and specificity. These models were then used to assign the type of grape of the wines corresponding to the twenty samples test set. The best results were obtained for U-PLS-DA and SPA-LDA with 76% and 80% accuracy.

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Matrix augmentation is regularly employed in extended multivariate curve resolution-alternating least-squares (MCR-ALS), as applied to analytical calibration based on second- and third-order data. However, this highly useful concept has almost no correspondence in parallel factor analysis (PARAFAC) of third-order data. In the present work, we propose a strategy to process third-order chromatographic data with matrix fluorescence detection, based on an Augmented PARAFAC model. The latter involves decomposition of a three-way data array augmented along the elution time mode with data for the calibration samples and for each of the test samples. A set of excitation-emission fluorescence matrices, measured at different chromatographic elution times for drinking water samples, containing three fluoroquinolones and uncalibrated interferences, were evaluated using this approach. Augmented PARAFAC exploits the second-order advantage, even in the presence of significant changes in chromatographic profiles from run to run. The obtained relative errors of prediction were ca. 10 % for ofloxacin, ciprofloxacin, and danofloxacin, with a significant enhancement in analytical figures of merit in comparison with previous reports. The results are compared with those furnished by MCR-ALS.

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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.

Assuntos