Analytical chemistry assisted by multi-way calibration: A contribution to green chemistry

A systematic review of polycyclic aromatic hydrocarbon pollution: A combined bibliometric and mechanistic analysis of research trend toward an environmentally friendly solution

Pollution caused by polycyclic aromatic hydrocarbons (PAHs) is a significant concern. This concern has become more problematic given the rapid modification of PAHs in the environment during co-contamination to form substituted PAHs. This review aims to integrate bibliometric analysis with a rigorous study of mechanistic insights, resulting in a more comprehensive knowledge of evolving research trends on PAH remediation. The results show that research in this field has progressed over the years and peaked in 2022, potentially due to the redirection of resources toward emerging pollutants, hinting at the dynamic nature of environmental research priorities. During this year, 158,147 documents were published, representing 7 % of the total publications in the field between 2000 and 2023. The different remediation methods used for PAH remediation were identified and compared. Bioremediation, having >90 % removal efficiency, has been revealed to be the best technique because it is cost-effective and easy to operate at large scale in situ and ex-situ. The current challenges in PAH remediation have been detailed and discussed. Implementing innovative and sustainable technologies that target pollutant removal and valuable compound recovery is necessary to build a more robust future for water management.

Aqueous two-phase systems coupled with chemometrics-enhanced HPLC-DAD for simultaneous extraction and determination of flavonoids in honey

In this study, an accurate, rapid, green, and environment friendly method for the extraction and quantitative analysis of flavonoids in honey was established by using the aqueous two-phase extraction combined with the chemometrics-assisted HPLC-DAD. The first purpose of this study was to extract seven flavonoids in five different types of honey using alcohol/salt aqueous two-phase system (ATPS). The system with 2.82 mL sodium citrate (30%), 1.58 mL water, and 3.10 mL isopropanol, showed the highest flavonoids extraction yields in the top phase (87.66-101.50%). Additionally, the three-way array of honey samples based on HPLC-DAD was decomposed mathematically by the alternating trilinear decomposition (ATLD) algorithm to obtain reasonable chromatograms, spectra, and concentration profiles for each analyte. Compared with the traditional solid-phase extraction method, the ATPS-ATLD-based method showed satisfactory spiked recoveries, lower limit of detection, and higher sensitivity, further verifying its accuracy and stability.

Kinetic Determination of Acetylsalicylic Acid Using a CdTe/AgInS2 Photoluminescence Probe and Different Chemometric Models

The combination of multiple quantum dots (QDs) in a multi-emitter nanoprobe can be envisaged as a promising sensing scheme, as it enables obtaining a collective response of individual emitters towards a given analyte and allows for achieving specific analyte-response profiles. The processing of these profiles using adequate chemometric methods empowers a more sensitive, reliable and selective determination of the target analyte. In this work, we developed a kinetic fluorometric method consisting of a dual CdTe/AgInS₂ quantum dots photoluminescence probe for the determination of acetylsalicylic acid (ASA). The fluorometric response was acquired as second-order time-based excitation/emission matrices that were subsequently processed using chemometric methods seeking to assure the second-order advantage. The data obtained in this work are considered second-order data as they have a three-dimensional size, I × J × K (where I represents the samples' number, J the fluorescence emission wavelength while K represents the time). In order to select the most adequate chemometric method regarding the obtained data structure, different chemometric models were tested, namely unfolded partial least squares (U-PLS), N-way partial least squares (N-PLS), multilayer feed-forward neural networks (MLF-NNs) and radial basis function neural networks (RBF-NNs).

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.

Restoring trilinearity with the purpose of advanced modeling: towards a more effective analysis of Pericarpium Citri Reticulatae during storage periods

To accurately quantify Pericarpium Citri Reticulatae samples, trilinear structure was restored in the stacked fingerprints for more robust modeling. Initially, liquid chromatography - diode array detector - mass spectrometry (LC-DAD-MS) and head space-solid phase micro extraction coupled to gas chromatography - mass spectrometry (HS-SPME/GC-MS) were utilized to analyze Pericarpium Citri Reticulatae. Faced with the time-shifts in two-dimensional (2D) matrices across different samples, three algorithms were developed to synchronize them. Furthermore, bilinear and trilinear models were used to realize the quantifications with different principles. Through real cases based on LC-DAD, the advantages and disadvantages of trilinear decomposition over multivariate curve resolution-alternating least-squares can be clarified in the quantification of raw or synchronized fingerprints. Also in the data processing, a modification version of multi-scale peak alignment (^mMSPA) was proved to be more suitable for trilinearity restoring than the other two algorithms. Recognizing these facts, restoring trilinearity were developed for more robust modeling in the application of the Pericarpium Citri Reticulatae fingerprints from different storage periods. After effective analysis, the upward/downward trend of 13 flavonoids were drawn accurately; and several flavour components having the highest contribution rate during storage were outlined reasonably. In conclusion, more robust modeling can be realized in trilinear data synchronized by appropriate algorithms, leading to an accurate quantification in herbal quality researches.

Second-order electrochemical data generation to quantify carvacrol in oregano essential oils

A novel analytical method using voltammetric second-order modeling based on multivariate curve resolution-alternating least-square (MCR-ALS) is presented for the first time for the quantitation of carvacrol (CAR) in oregano essential oils (OEO). The second-order cyclic voltammetry data were generated on the basis that CAR shows a diffusional system. Thus, the scan rate (v) was used as a second instrumental mode and cyclic voltammograms at different v were acquired for a single sample, generating the second-order data. CAR determination was performed in presence of thymol, included as a potential interferent. Results demonstrated that MCR-ALS successfully exploited the second-order advantage and the recoveries were not statistically different than 100%. The limits of detection and quantitation were estimated using the MCR-ALS which were 6.27 × 10^-5°mol°L^-1°and 1.90 × 10^-4°mol L^-1, respectively. Finally, the developed methodology was implemented to quantify of CAR in OEO samples.

Achieving the analytical second-order advantage with non-bilinear second-order data

Multi-way calibration based on second-order data constitutes a revolutionary milestone for analytical applications. However, most classical chemometric models assume that these data fulfil the property of low rank bilinearity, which cannot be accomplished by all instrumental methods. Indeed, various techniques are able to generate non-bilinear data, which are all potentially useful for the development of novel second-order calibration methodologies. However, the achievement of the second-order advantage in these cases may be severely limited, since methods for comprehensive modelling of non-bilinear second-order data remain only partially explored. In this research, the analytical performance of three well-known second-order models, namely non-bilinear rank annihilation (NBRA), unfolded partial least-squares with residual bilinearization (U-PLS-RBL) and multivariate curve resolution - alternating least-squares (MCR-ALS) is systematically assessed through sets of simulated and experimental non-bilinear second-order data, involving one analyte and one interferent. Although it is not possible to establish a single strategy to model any type of non-bilinear second-order data with the studied methods, each approach may lead to successful predictions under certain circumstances. It is shown that the prediction capacity is severely affected by data properties such as the level of instrumental noise, the rank of the response matrices and the signal selectivity pattern of the analyte.

Analytical determinations of luteolin

Plants, through the photosynthesis process, produce the substances necessary for all the life cycles of nature, which are called "primary metabolites." Moreover, there are some plants that synthesize, in addition to these, other substances with more specific functions, which are known as "secondary metabolites." It is inside this group that flavonoids are located, whose main function is to protect organisms from damage caused by different oxidizing agents. Luteolin (3,4,5,7-tetrahydroxy-flavone) belongs to the sub-class of flavonoids known as flavones and is one of 10,000 flavonoids currently known, being one of the most bio-active flavonoids. Its various beneficial properties for health, together with the increasing reduction in the use of synthetic antioxidants, make the study of luteolin a very active field. Within this, the quantification of this molecule has become a subject of very special interest given that it is transversal to all fields. In this review article, we aim to give the reader a broad and deep vision of this topic, focusing on the events reported in the last 5 years and covering all possible techniques related to analytical determinations. We will discuss in terms of advantages and disadvantages between techniques, selectivity, sensitivity, costs, time consumption, and reagents as well as in the complexity of operations.

Herbal Component Correlation and Matrix-based Resolution in Comprehensive two-dimensional Gas Chromatography - Mass Spectrometry data via Intelligent Clustering of Modulation Peaks

In order to facilitate correlation calculation and matrix-based resolution in comprehensive two-dimensional gas chromatography - mass spectrometry (GC × GC-MS) data-set, an intelligent clustering of modulation peaks (ICMP) algorithm was developed in this paper. ICMP is start with the second -dimension (²D) peak restriction, then conducting the peak shape restriction in the first dimension (¹D), finally end with the eigenvalues calculation against mass spectra in moving sub-windows. After this three-tier restriction, multi-component spectral correlative chromatography (MSCC) was applied in peak clustering result from a row-wise augmented "two-dimension (2D) slice" set. Then the component similarities and differences were distinguished rapidly/ accurately in chemical fingerprints from ChaiHu Shugan San and Cyperus rotundus. Faced with co-eluted phenomenon, matrix-based resolution was made in the representative sub-matrices that have been locked in ICMP procedure. From the example data shows that ICMP- multivariate curve resolution (MCR) can served as a good complement to (non) trilinear decomposition. To summarize, the GC × GC data-structure can be simplified to facilitate MSCC or MCR operation in fingerprints from herbal or biological samples.

Study of photodegradation kinetics of aflatoxins in cereals using trilinear component modeling of excitation-emission matrix fluorescence data

In this work, a smart analytical strategy that combines excitation-emission matrix (EEM) fluorescence detection with alternating trilinear decomposition (ATLD) algorithm was developed for fast, on-line and interference-free study on the photodegradation kinetics of aflatoxin B₁ (AFB₁) and aflatoxin G₁ (AFG₁) in rice and wheat under UV-Vis light (λ = 250-500 nm) treatment. With the aid of prominent "second-order advantage" of ATLD method, pure fluorescence signals of two targeted analytes can be directly resolved out from heavily overlapping spectral environment and accurately quantified even in the presence of unknown matrix interferences. Cereal samples in kinetic processing of photodegradation were detected without complex pretreatment steps except for a simple extraction using methanol/water solution (4:1, v/v), which solves the problem facing varied matrix interferences in the case of on-line monitoring of aflatoxins. The kinetic signals of analytes of interest were directly extracted regardless of varied matrix backgrounds of various cereals. The kinetic curves and degradation speeds of AFB₁ and AFG₁ can be estimated by resolved quantitative data, optimal radiation conditions including 365 nm wavelength and 35 J m^-2 density were discussed for high-efficiency detoxification control of aflatoxins in rice and wheat. This strategy was promising to be as an alternative tool for eco-friendly photodegradation kinetic study of mycotoxins or other hazards in complex foodstuff matrixes.

Impact of diverse background interferences on the alternating trilinear decomposition modeling of excitation-emission matrix fluorescence data acquired from different sample sources

Alternating trilinear decomposition (ATLD) method enables the qualitative and quantitative analysis of excitation-emission matrix fluorescence (EEMF) data acquired from complex samples. However, the impact of diverse background interferences from different sample sources on the performances of ATLD method has never been lucubrated. In this work, simulated and real EEMF data sets from different sample sources with diverse background interferences were collected and subjected to ATLD analysis. The performances of ATLD modeling individual and global EEMF data sets were comprehensively compared in terms of the resolved spectral profiles and quantitative results. It was found that ATLD method can use the same set of calibration samples to resolve and quantify multiple components of interest in multiple complex systems with diverse background interferences, regardless of individual or global modeling. The results revealed that the qualitative and quantitative results provided by ATLD method were affected neither by diversity of background interferences nor by data merging as long as the acquired EEMF data sets conform to the trilinear component model. This property of ATLD method can enrich the "second-order advantage", i.e. the term "unknown interferences" in the concept of "second-order advantage" refers to not only constant background interferences but also diverse background interferences, which will be certain to further expand the practicality of ATLD method in complex sample analysis, especially in the field of fluorescence spectroscopy.

Why should the pharmaceutical industry claim for the implementation of second-order chemometric models-A critical review

Today, pharmaceutical products are submitted to a large number of analytical tests, planned to either ensure or construct their quality. The official methods of analysis used to perform these determinations are very different in nature, but almost all demand the intensive use of reagents and manpower as major drawbacks. Thus, analytical development is continuously evolving to find fast and smart approaches. First-order chemometric models are well-known in the pharmaceutical industry, and are extensively used in many fields. Such is the impact of chemometric models that regulatory agencies include them in guidelines and compendia. However, the mention or practical application of higher-order models in the pharmaceutical industry is rather scarce. Herein, we try to bring a brief introduction to chemometric models and useful literature references, focusing on higher-order chemometric models (HOCM) applied to reduce manpower, reagent consumption, and time of analysis, without sacrificing accuracy or precision, while gaining selectivity and sensitivity. The advantages and drawbacks of HOCM are also discussed, and the comparison to first-order chemometric models is also analyzed. Along the work, HOCM are evidenced as a powerful tool for the pharmaceutical industry; moreover, its implementation is shown during several steps of production, such as identification, purity test and assay, and other applications as homogeneity of API distribution, Process Analytical Technology (PAT), Quality by Design (QbD) or natural product fingerprinting. Among these topics, qualitative and quantitative applications were covered. Experimental approaches of chemometrics coupled to several analytical techniques such as UV-vis, fluorescence and vibrational spectroscopies (NIR, MIR and Raman), and other techniques as hyphenated-chromatography and electrochemical techniques applied to production and analysis are discussed throughout this work.