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

A micellar-enhanced fluorescence photoinduced four-way calibration method for the determination of multiclass pesticides in lemon juice

In this work, a four-way multivariate calibration method for the simultaneous determination of four pesticides - carbendazim (CBZ), thiabendazole (TBZ), pirimiphos-methyl (PMM), and clothianidin (CLT) - in lemon juice is presented. Third-order data were acquired by registering the photoinduced fluorescence of the analytes as excitation-emission matrices at different times of UV-light irradiation, in the presence of organized media (direct micelles) as fluorescence enhancers. The optimal experimental conditions (pH 11.5 and 32 mmol L-1 hexadecyltrimethylammonium chloride surfactant) were determined through a central composite design using the response surface methodology. The analytes were individually calibrated, except for TBZ and CBZ due to the inner filter effect of TBZ on CBZ. Test samples containing all analytes and imidacloprid (as potential interference) were analysed. PARAFAC was utilized to evaluate both the trilinearity and quadrilinearity of the third-order data and four-way arrays, respectively. PMM was successfully determined with quadrilinear PARAFAC decomposition, whereas CLT, TBZ, and CBZ were satisfactorily modelled using U-PLS/RTL due to the loss of quadrilinearity caused by different phenomena. The profitable applicability of the analytical method in the CBZ, TBZ, PMM, and CLT determination in lemon juice samples was demonstrated, achieving limits of detection below the maximum residue levels reported by the European Commission, and mean recoveries at 90 ± 5%.

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.

Variable selection in the chemometric treatment of food data: A tutorial review

Food analysis covers aspects of quality and detection of possible frauds to ensure the integrity of the food. The arsenal of analytical instruments available for food analysis is broad and allows the generation of a large volume of information per sample. But this instrumental information may not yet give the desired answer; it must be processed to provide a final answer for decision making. The possibility of discarding non-informative and/or redundant signals can lead to models of better accuracy, robustness, and chemical interpretability, in line with the principle of parsimony. Thus, in this tutorial review, we cover aspects of variable selection in food analysis, including definitions, theoretical aspects of variable selection, and case studies showing the advantages of variable selection-based models concerning the use of a wide range of non-informative and redundant instrumental information in the analysis of food matrices.

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.

An integrated giant polyoxometalate complex for photothermally enhanced catalytic oxidation

A strategy integrating near infrared (NIR) photothermal and catalytic effects within one active center beyond ultraviolet and visible light is proposed without the combination of separated photothermal transformation components. A giant polyoxomolybdate, which has high NIR photothermal conversion efficiency, is selected as the model catalyst, while a cationic β-cyclodextrin is used to cover its negatively charged surface electrostatically. Under NIR light radiation, the designed catalyst increases catalytic activity of cyclohexene oxidation under O2 atmosphere in water. The conversion reaches about pentaploid of the reaction without NIR radiation. By excluding heating effect from the external heater at the same temperature, about twice as much enhancement, which can be attributed to the sole photothermal action, is still observed. While the catalytic center is shielded by the organic porous layer, the surface cavity allows the integrated catalyst to conduct a selective catalysis by screening the molecules in size over the surface channel.