Highly sensitive quantitation of pesticides in fruit juice samples by modeling four-way data gathered with high-performance liquid chromatography with fluorescence excitation-emission detection

Functional data analysis, a comprehensive framework for processing non-quadrilinear and low-selective data provided by four-way liquid chromatography analysis

The chemometric treatment of higher-order chromatographic (LC) data often crashes due to two main effects: the chromatographic band shifts/warpings across samples and quasi-full overlaps between component signals, affecting their analytical selectivities. From a chemometric point of view, these phenomena have independent effects, although they can jointly contribute to the failure of the algorithms: 1) data multilinearity breaking, leading to the poor performance of multilinear decomposition algorithms, and 2) linear dependence between the analytes signals, causing the failure of folded models. Under this scenario, making chemometric processing feasible involves defining specific experimental conditions that minimize these effects or increasing the number of instrumental ways to deal with selectivity lost. This work presents the Functional Aligned of Pure Vectors (FAPV) algorithm for restoring four-way chromatographic data multilinearity and bearing the spectral overlap trouble. Simulated and experimental four-way data were used to test the FAPV analytical efficiency, covering a wide range of chromatographic artifacts. Based on a multi-injection procedure, the experimental case implied the chromatographic determination of two analytes with uncalibrated interferents in aqueous samples. Both data systems were subjected to FAPV and then processed by PARAFAC. Therefore, a comprehensive comparison was made with the most widely used chemometric models for non-multilinear chromatographic data (MCR-ALS and PARAFAC2). Moreover, the performance of the FAPV approach was compared with commonly used alignment procedures, e.g., correlation-optimized warping. The results (c.a. REPs of 10 % in both analytes from the experimental case) show the efficiency of the FAPV algorithm in solving the troubles observed in chromatographic/spectral data.

Pesticides monitoring in biological fluids: Mapping the gaps in analytical strategies

Pesticides play a key-role in the development of the agrifood sector allowing controlling pest growth and, thus, improving the production rates. Pesticides chemical stability is responsible of their persistency in environmental matrices leading to bioaccumulation in animal tissues and hazardous several effects on living organisms. The studies regarding long-term effects of pesticides exposure and their toxicity are still limited to few studies focusing on over-exposed populations, but no extensive dataset is currently available. Pesticides biomonitoring relies mainly on chromatographic techniques coupled with mass spectrometry, whose large-scale application is often limited by feasibility constraints (costs, time, etc.). On the contrary, chemical sensors allow rapid, in-situ screening. Several sensors were designed for the detection of pesticides in environmental matrices, but their application in biological fluids needs to be further explored. Aiming at contributing to the implementation of pesticides biomonitoring methods, we mapped the main gaps between screening and chromatographic methods. Our overview focuses on the recent advances (2016-2021) in analytical methods for the determination of commercial pesticides in human biological fluids and provides guidelines for their application.

Layered filter paper-silver nanoparticle-ZIF-8 composite for efficient multi-mode enrichment and sensitive SERS detection of thiram

A SERS substrate FP/Ag/ZIF-8 composed of filter paper (FP), silver nanoparticles (AgNPs) and zeolitic imidazolate framework (ZIF-8) film arranged in a layered structure was developed for sensitive detection of pesticide thiram in various samples. Roles of these components in analyte adsorption and Raman signal enhancement were studied using a pesticide intermediate 4-Aminothiophenol (4-ATP) as the probe. The substrate showed high adsorption and optimized SERS response with thick metal organic framework (MOF) coating (125 nm), which is different from previous reported plasmonic particle-MOF composite substrate, where thinnest MOF coating produced the strongest SERS signal. Detection limit for 4-ATP improved 1000-fold on FP/Ag/ZIF-8 (3 pM) compared with that on FP/Ag (3 nM). Importantly, the FP/Ag/ZIF-8 with porous and flexible property can efficiently capture pesticide thiram in different real samples using soaking, filtration or swabbing operation. The subsequent SERS detection of thiram showed advantages of low detection limit (soaking, LOD: 0.04 nM in lake water), fast detection (filtration, within 1 min in peach juice) and suitable for curve surface analysis (swabbing, LOD: 0.1 ng/cm² on apple peel), respectively. The substrate also displayed good reproducibility, high stability and size-selective response for thiram detection. Such a layered plasmonic particle/MOF hybrid may hold great promise for toxicant analysis in environment and food.

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.

Porous polymers from octa(amino-phenyl)silsesquioxane and metalloporphyrin as peroxidase-mimicking enzyme for malathion colorimetric sensor

Rapid and efficient pesticide detection methods are particularly important due to the growing problems of pesticide residues. Here, a new azo-based porous organic polymer, Azo(Fe)PPOP, was prepared from octa(amino-phenyl)silsesquioxane (OAPS) and iron(III) 5,10,15,20-tetrakis(4-nitrophenyl)porphyrin (FeTPP(NO₂)₄) via a simple coupling reaction without the participation of metal catalysts. The inorganic cage units of OAPS endowed Azo(Fe)PPOP a porous framework, high surface area, favorably thermal and chemical stability. In Azo(Fe)PPOP, iron(III) porphyrin units were individually isolated in a fixed location, which could effectively avoid dimerization or self-oxidation as happens as in the case of porphyrin monomers. Such a unique structure made Azo(Fe)PPOP exhibit an excellent peroxidase-like catalytic performance in the presence of H₂O₂ and 3,3',5,5'-tetramethylbenzidine (TMB). Because of these advantages, we established a selective, facile, and sensitive colorimetric platform for direct detection of malathion within a very short time (3 min) with a low detection limit (8.5 nM). In addition, the recognition mechanism between Azo(Fe)PPOP and malathion was verified using X-ray photoelectron spectroscopy spectra. The practicality of the constructed platform was further executed by the detection of the pesticide in soil and food samples.

Multilinear Mathematical Separation in Chromatography

Chromatography is a powerful and generally applicable method for the analytical separation and quantification of the chemical constituents in complex mixtures because chromatographic separation can provide high selectivity by isolating all analytes from interferences. Multiway analysis based on the multilinear model is an increasingly widely used method for interference-free and fast determination of the chemical constituents also in complex mixtures because multilinear mathematical separation can provide high selectivity by extracting the pure signal of the analyte from the mixed signal of a real sample. By combining chromatographic separation with mathematical separation, multiway calibration method, multiway standard additions method, and multiway internal standard method can be established. Chromatography assisted by multiway analysis can reduce the requirements for complete chromatographic separation, save elution time, and decrease the consumption of the mobile phase, particularly when the peak coelution problem is difficult to solve. This review presents the fundamentals and analytical applications of multilinear mathematical separation in chromatography.

Chromatographic Methods for Detection and Quantification of Carbendazim in Food

Carbendazim (CBZ), which is a fungicide widely used for the management of plant diseases, has been detected in a number of food products. The negative effects of CBZ to human health have stimulated the reduction of the maximum residue limits (MRLs), and subsequently the development of reliable and sensitive detection methods. Here, we are reviewing for the first time all reported chromatographic methods for the detection and quantification of CBZ in food. Several techniques, including liquid chromatography (LC), thin layer chromatography (TLC), micellar electrokinetic chromatography (MEKC), and supercritical fluid chromatography (SFC), were used for the separation and detection of CBZ, showing diverse characteristics and sensitivity. Some methods allowed the specific determination of CBZ, whereas other methods were successfully applied for the simultaneous quantification of a huge number of pesticides. Most reported methods showed limits of detection (LOD) and quantification (LOQ) lower than the MRLs. Relevant efforts in the field have been directed toward the simplification and optimization of the extraction steps prior to the chromatographic separation to increase the recovery and reduce the matrix effects. In this Review, the matrices, extraction procedures, and separation and detection parameters are detailed and compared in order to provide new insights on the development of new reliable methods for the detection of CBZ in food.

Novel Approach to the Analysis of Chemical Third-Order Data

For the more complex samples, chemical higher-order data can be collected from various information sources, which become the necessary foundation of accurate analysis. In this article, the Tchebichef cubic moment (TCM) was developed for the analysis of chemical third-order data for the first time. Then, the proposed TCM approach was applied to the fluorescence excitation-emission time data for the analysis of adrenaline and noradrenaline in urinary samples (Data I) and the data fusion of the excitation-emission matrix (EEM), NMR, and liquid chromatography-mass spectrometry (LC-MS) spectra for the determination of the five target components (Data II). For Data I, all of the cross-validation correlation coefficients (R_cv²) of the obtained linear models on the calibration set were more than 0.9937 and the prediction root-mean-square errors (RMSE_p) of the external independent test samples were less than 0.0250 μM. For Data II, all of the R_cv² were higher than 0.9846 and RMSE_p were less than 0.2267 μM. Compared with several conventional methods, the proposed method was more convenient and accurate. This study provides another effective approach to the analysis of complex samples based on their chemical third-order data.

Multiblock modelling on the study of the kinetic degradation of rosuvastatin calcium in the presence of retention time shifts and rank deficiency

In pharmaceutical development, forced degradation studies are mandatory before the commercialization of any drug product. They aim at identifying the possible degradation routes and the potential products that may be formed during drug product shelf life. The most widely used techniques for monitoring this in the pharmaceutical industry are hyphenated techniques such as Liquid Chromatography coupled to ultraviolet diode array detector (LC-DAD). There are however some drawbacks, such as long analysis times required for the elution of all compounds and coelution, which is not easily detected since degradation products usually have spectra very similar to that of the drug. Chemometrics methods applied to LC-DAD data are capable of solving this issue, but the approaches described in the literature first require peak alignment to solve the rank deficiency problem, which is a delicate preprocessing method for high order data. The present work describes another approach where extra information - the kinetic degradation profiles - is included for the modelling, generating a third-order data set for each sample, resulting in a four-way array (sample x retention times x spectra x degradation profile). This approach has the advantage of using the information in the third mode to solve the peak co-elution problem without the need for peak alignment among samples. With the proposed approach, it was possible to study the degradation of calcium rosuvastatin, a modern cholesterol lowering drug, using a 2 min-run, despite all the challenges in the modelling of this data. The proposed strategy was compared to an approach based on augmenting the matrix in the spectral/kinetic modes (second order modelling strategy).

Nanozyme Sensor Arrays Based on Heteroatom-Doped Graphene for Detecting Pesticides

Pesticides, widely used for pest control and plant growth regulation, have posed a threat to the environment and human health. Conventional methods to analyze pesticide residues are not applied to resource-limited areas because of their high cost, complexity, and requirements for expensive instruments (such as GC/MS and LC/MS). To address these challenges, herein we fabricated colorimetric nanozyme sensor arrays based on heteroatom-doped graphene for detection of aromatic pesticides. The active sites of nanozymes could be differentially masked when different pesticides were adsorbed on the graphene, which in turn resulted in the decrease of their peroxidase-mimicking activities. On the basis of this principle, five pesticides (i.e., lactofen, fluoroxypyr-meptyl, bensulfuron-methyl, fomesafen, and diafenthiuron) from 5 to 500 μM were successfully discriminated by the sensor arrays. In addition, discrimination for different concentrations of each pesticide and different ratios of two mixed pesticides were also demonstrated. The practical application of the sensor arrays was further validated by successfully discriminating the pesticides in soil samples. This work not only provides a facile and cost-effective method to detect pesticides but also makes a positive contribution to food safety and environmental protection.

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.

Aptasensors for pesticide detection

Pesticide contamination has become one of the most serious problems of public health in the world, due to their wide application in agriculture industry to guarantee the crop yield and quality. The detection of pesticide residues plays an important role in food safety management and environment protection. However, the conventional detection methodologies cannot realize highly sensitive, selective and on-site detection, which limits their applications. Aptamers are short single-stranded oligonucleotides (RNA or DNA) selected by SELEX method, which can selectively bind to their targets with high affinity. Compared with the commonly used antibodies or enzymes in designing biosensors, aptamers exhibit better stability, low molecular weight, easy modification and low cost, and were regarded as excellent candidates for developing aptasensors for pesticide detection. In this review, application of aptamers for pesticide detection was reviewed. Firstly, aptamers specifically bind to various pesticides were first summarized. Secondly, the progresses and highlights of developing aptasensors for highly-sensitive and selective detection of pesticide residues were systematically provided. Finally, the present challenges and future perspectives for developing novel highly-effective aptasensor for the detection of pesticide residues were discussed.

A colorimetric sensor array based on sulfuric acid assisted KMnO4 fading for the detection and identification of pesticides

Pesticides play a critical role in improving crop yield in modern agriculture, but their residues significantly harm the environment and human health. Herein, a novel and simple colorimetric sensor array built on sulfuric acid assisted KMnO₄ fading strategy has been developed for pesticides detection and discrimination. This sensor array is facilely fabricated by KMnO₄ and sulfuric acid through simply adjusting their concentrations and ratios. Hierarchical clustering analysis (HCA) demonstrates that the as-fabricated colorimetric sensor array has a high dimensionality, and shows excellent capability to recognize common kinds of pesticides from potential interferants. Semi-quantitative detection was achieved through combining HCA and corresponding fitting curves. Moreover, the proposed sensor array was successfully applied to detect pesticide residues (e.g. carbaryl) in real samples. The strategy described herein will not only "maximally" simplify the design and fabrication approach, but expand the application fields of colorimetric sensor array methodology towards weak-reactive analytes.

A road map for multi-way calibration models

A road map is proposed for the selection of a multi-way calibration model according to the data properties.