Multi-way analysis has become one of the most powerful and versatile chemometric approaches for dealing with the increasing complexity of data generated in modern analytical chemistry. Advances in instrumentation, the widespread use of hyphenated techniques, and the inherently multidimensional nature of many experimental designs require methods capable of preserving structural relationships within datasets. In this context, multi-way tools such as Tucker 3, PARAFAC, or other supervised variants provide rigorous and interpretable descriptions of variability across multiple modes (samples, variables, conditions), enabling the extraction of meaningful patterns, improved noise handling, and enhanced robustness, compared with traditional bilinear approaches. This review offers a critical overview of the most commonly applied multi-way algorithms and their practical use in fields such as environmental chemistry, food science, clinical diagnostics, industrial process monitoring, and pharmaceutical analysis. The essential steps of the workflow, from data acquisition and preprocessing to model selection and interpretation, are discussed, highlighting their impact on model reliability. A dedicated section summarizes the software environments available for performing multi-way analyses, guiding readers in selecting the most suitable tools for their needs. Overall, this review emphasizes how multi-way chemometrics is becoming increasingly crucial for converting complex, high-dimensional data into reliable and actionable chemical knowledge.

Nickel release from metallic items is the leading cause of allergic contact dermatitis, and preventive strategies require both reliable detection tools and materials capable of limiting skin exposure. In this work, we propose dual-function bioplastic coatings based on starch, glycerol, and cellulose derivatives incorporating dimethylglyoxime (DMG) and a pH-10 borate buffer to enable colorimetric nickel sensing directly in the solid state. The Ni–DMG assay was first optimized in solution through UV-Vis spectroscopy and a Central Composite Face-Centered Design, identifying reagent concentrations that maximize linearity while minimizing detection limits. These conditions were transferred to bioplastic films prepared using carboxymethyl cellulose (CMC) or quaternized hydroxyethyl cellulose ethoxylate (QHECE). The materials were characterized by FT-IR spectroscopy and Principal Component Analysis, while gravimetric tests assessed hydrophilicity. Both bioplastics showed clear and reproducible colorimetric responses upon nickel exposure, and multivariate models built from RGB values and UV-Vis spectra enabled quantitative prediction of Ni2+ content. However, the proof-of-concept experiment revealed insufficient resistance to prolonged moisture, with films softening and partially losing cohesion under conditions mimicking skin perspiration. These results demonstrate that the sensing mechanism is robust, but the current bioplastic formulation requires improved water resistance before practical deployment as protective coatings for jewelry.

A chemometric-assisted strategy was developed for accurate, online tap water conductivity monitoring using electrical impedance spectroscopy in a low-cost Internet-of-Things device (NEMO). The study focused on optimizing instrumental settings and quantification models to achieve reliable conductivity measurements in the 15-750 & micro;S/cm range, representative of real tap water conditions. Design of Experiments (DoE) was applied to evaluate the influence of key measurement parameters, while Principal Component Analysis (PCA) guided the selection of representative water samples, based on the chemical variability of tap waters in the Lombardia region (Italy). Partial Least Squares (PLS) regression was then used to model the full impedance spectra (2-150 kHz), achieving accurate conductivity predictions directly from raw data. The optimized model, built with data from 144 real water samples and validated on independent test sets, demonstrated excellent analytical performance (RMSECV = 35 & micro;S/cm, RMSEP = 32 & micro;S/cm) and high robustness under simulated distribution system conditions (30 L/h flow rate, 22 degrees C). Analytical figures of merit, including limit of detection (33-51 & micro;S/cm) and quantification (98-154 & micro;S/cm), were calculated following IUPAC-consistent chemometric procedures. This approach enables high-performance conductivity monitoring using compact and energy-efficient sensors, bridging the gap between device simplicity and analytical accuracy for online water quality monitoring systems.

Albumin is one of the most common proteins in human fluids, making it one of the most studied and quantified; it is also used as a biological marker for a wide range of pathologies, such as diabetes mellitus, renal diseases, and liver malfunctions. Clinically, it is currently determined using UV-Vis spectroscopy with sulfonephthalein indicators such as Bromophenol Blue (BPB), Bromocresol Green (BCG), and Bromocresol Purple (BCP) at acidic pH levels between 3.5 and 5. These procedures, generally referred to as dye-binding methods, are favored for their rapidity, simplicity, low cost of reagents and instrumentation, and potential for automation. However, they have some disadvantages, including the risk of contamination, the need for skilled operators in manual analysis, low selectivity, matrix effects, time sensitivity, inaccuracy, and low robustness towards pH and ionic strength (I) variations. In this study, new alternative dye-binding methods are proposed using Bovine Serum Albumin (BSA) as model albumin and five different sulfonephthalein dyes, the three aforementioned dyes, Chlorophenol Red (CPR), and Bromothymol Blue (BTB). The successful application of Partial Least Squares regression allows quantifying the protein using each dye and in a range of pH between 3.5 and 9 and of I between around 0.01 and 0.5 M. In addition, most of the multivariate quantification models show an interesting robustness towards both pH and I since the same model can be applied to quantify BSA in samples with much different pH (over 3 log units) and I values.

The detection of human serum albumin (HSA) in urine is crucial for the early diagnosis of nephrotic syndromes and diabetic nephropathy. In this study, we developed a cost-effective, colorimetric sensor based on Bromocresol Green (BCG) sorbed on Color Catcher (R) (CC) sheets for albumin detection. The sensor undergoes a visible color change from yellow to blue upon interaction with albumin at acidic pH, enabling qualitative detection. A Design of Experiment (DoE) approach was applied to optimize sensor preparation and application and to control experimental variability within the lab-scale preparation procedure, ensuring enhanced sensitivity and robustness. Several multivariate data analysis tools, including Principal Component Analysis (PCA) and Discriminant Analysis (LDA and QDA), were merged to describe the samples, develop robust and predictive models and assess detection performance. The optimized sensor proved a detection limit as low as 0.5 mu M for albumin, making it a promising candidate for rapid, low-cost, and user-friendly point-of-care (PoC) applications.

The acid-catalyzed conversion of cannabidiol (CBD) to tetrahydrocannabinol and iso-tetrahydrocannabinol derivatives is a well-established synthetic strategy. However, since the reaction outcome is strongly dependent on the conditions, a careful investigation is always required to achieve the optimal chemoselectivity. Chemometrics recently emerged as an effective approach for improving synthetic methods, especially when multiple parameters are involved. The present paper aims to apply chemometrics tools to the optimization of the procedures for the preparation of Δ⁹-THC, Δ⁸-THC, Δ⁸-iso-THC, Δ⁴-iso-THC, and Δ⁴(⁸)-iso-THC. All the reactions have been performed at room temperature by tuning the initial concentration of CBD, the equivalents of the model Lewis acid considered (BF3·OEt2), the reaction time, and the nature of the media to achieve the desired products. The kinetics of the process, followed over the course of 1 to 48 hours, were analyzed by means of Principal Component Analysis (PCA) to initially simplify the multidimensional dataset and help identify the best media for Δ⁹-THC and Δ⁸-THC. In order to optimize the conditions needed for Δ⁴-iso-THC, Δ⁴(⁸)-iso-THC, and Δ⁸-iso-THC, multiple Design of Experiment (DoE) were employed, leading to the successful isolation of the target compounds. During the investigation, cannabinoid derivatives incorporating a portion of the reaction medium were also identified.

The acid‐catalyzed conversion of cannabidiol (CBD) to tetrahydrocannabinol and iso‐tetrahydrocannabinol derivatives is a well‐established synthetic strategy. However, since the reaction outcome is strongly dependent on the conditions, a careful investigation is always required to achieve the optimal chemoselectivity. Chemometrics recently emerged as an effective approach for improving synthetic methods, especially when multiple parameters are involved. The present paper aims to apply chemometrics tools to the optimization of the procedures for the preparation of Δ⁹‐THC, Δ⁸‐THC, Δ⁸‐ iso ‐THC, Δ⁴‐ iso ‐THC, and Δ⁴ ( ⁸ ) ‐ iso ‐THC. All the reactions have been performed at room temperature by tuning the initial concentration of CBD, the equivalents of the model Lewis acid considered (BF 3 ·OEt 2 ), the reaction time, and the nature of the media to achieve the desired products. The kinetics of the process, followed over the course of 1 to 48 hours, were analyzed by means of Principal Component Analysis (PCA) to initially simplify the multidimensional dataset and help identify the best media for Δ⁹‐THC and Δ⁸‐THC. In order to optimize the conditions needed for Δ⁴‐ iso ‐THC, Δ⁴(⁸)‐ iso ‐THC, and Δ⁸‐ iso ‐THC, multiple Design of Experiment (DoE) were employed, leading to the successful isolation of the target compounds. During the investigation, cannabinoid derivatives incorporating a portion of the reaction medium were also identified.

A combined statistical approach significantly improves the reliability of gravimetric assessments of hydrophilicity in starch/glycerol/carboxymethylcellulose biofilms. This study reveals that traditional methods for evaluating hydrophilicity-related properties are limited by inconsistent experimental conditions and inadequate statistical design. By systematically varying film composition within a pseudocomponent domain and applying ANOVA, Mixture Design, and Principal Components Analysis, we demonstrate that composition-related effects far exceed intrinsic method variability. The results underscore the influence of components on hydrophilic behavior, offering robust and predictive models across the experimental domain. This findings-oriented approach provides a rational framework to optimize formulations, ensuring reproducible and meaningful assessments of the hydrophilicity.

Nowadays, the existence of high entropy perovskite (HEPs) oxides is well established and, since their discovery, have found application in several technological fields. The main advantage provided by these materials,...

This paper presents a comprehensive optimization strategy for the synthesis of new black ceramic pigments with low cobalt content while maintaining a single-phase spinel structure. The aim is to achieve comparable hues to those of an industrial benchmark containing five transition metals while minimizing the environmental impact. Exploring all possible compositions deriving from a five-component system (Cr, Mn, Fe, Co and Ni), through traditional methods would be extremely time consuming to guarantee an efficient sampling, requiring high experimental efforts. Hence, to identify the best black compositions, we employed a chemometric approach, the Design of Experiments, aiming to investigate the compositional domain derived by varying the metals stoichiometry within fixed boundaries to identify optimal pigment compositions. The resulting pigments (comprising Cr, Fe and Co), despite the lower cobalt content, exhibited optimal colorimetric properties comparable to the standard benchmark, with experimental Delta E values comparable to the ones predicted by the model. In particular, the pure spinels Cr1.05Fe1.05Co0.9O4 and Cr1.2Fe1.05Co0.75O4 displayed low lightness and chroma (L* = 2.4; C* = 5 and L* = 1.12; C* = 1.1, respectively), providing deep and dark black tonality. These compositions exhibit promising colorimetric performance and chemical stability, offering potential benefits for industrial applications.

The paper describes the development and application of a screen-printed electrode cell with a graphite-ink working electrode modified by a molecularly imprinted electropolymerized polypyrrole for the voltammetric determination of the herbicide 4-chloro-2-methylphenoxyacetic acid (MCPA). The method exploits the direct measurement of the analyte by applying the differential pulse voltammetry (DPV) technique, taking advantage of the irreversible oxidation peak at about +1.0 V vs. Ag/AgCl pseudo reference electrode. The presence of the molecularly imprinted polypyrrole enhances the sensor's selectivity and sensitivity. A chemometric approach has been crucial for quantitative analysis because of the peak's broad and not well-defined shape. Firstly, a proper pretreatment of the voltammetric signals is identified, proving the most effective is the first-derivative function transformation of the signal. The Partial Least Square regression (PLS) is the tool applied for MCPA quantification. A preliminary PLS model has been developed and validated in dihydrogen phosphate solution at pH 5.5, aiming to optimize the data treatment approach. Then, the same approach is used to develop a PLS model analyzing tap water samples fortified with MCPA and other pesticides as possible interferents to simulate contaminated natural waters. The model correctly predicted the analyte concentration in the range of 2.5-75 μM, assuring the reliability and robustness of the sensor for the possible quantification of MCPA in wastewater samples.

In this study, we aimed to explore the phase stability of high-entropy oxides (HEOs) beyond their conventional equimolar composition, which presents the maximum configurational entropy. This task is challenging due to the large number of compositional parameters involved. We used the design of experiments as a strategy to investigate the compositional range of stability of the rock salt (RS) structure in the (Mg,Ni,Co,Zn,Cu)O quinary system, featuring the prototypical HEO Mg0.2Ni0.2Co0.2Zn0.2Cu0.2O. Our study revealed that the chemical nature of the RS-native oxides (NiO, MgO, and CoO) significantly affects the phase stability of the RS-HEO, suggesting that the HEO stability is not solely governed by the balance of configurational entropy and enthalpy of mixing. In addition, a single high-entropy phase can be achieved on a wide out-of-equimolar set of compositions, thereby broadening the compositional range that should be explored in the search for innovative materials with unique properties and applications.

Green silver nanoparticles (AgNPs@OPE) were obtained by using orange (citrus sinensis) peel water extract (OPE) that acts as a reducing and capping agent. This procedure permits the valorisation of waste as orange peel, and lowers the environmental impact of the process, with respect to the conventional synthetic procedure. The OPE extract reduced Ag(I) to Ag(0) in alkaline conditions, and stabilised the produced nanoparticles as a capping agent. The AgNPs@OPE were deeply characterized by UV-Vis spectroscopy, FT-IR, SEM analysis and DLS analysis and successively used as colorimetric sensors for different metals in aqueous solution. The colourimetric assay showed that AgNPs@OPE were able to detect Pb2+ 2 + and Cd2+, 2 + , as demonstrated by the splits of surface plasmon resonance (SPR) band accompanied by the formation of a second new band; these spectral modification resulted in a colour change, from pristine nanoparticles' yellow to brown, due to the aggregation process. For the quantification of each of the two target cations, a calibration was performed by using the univariate linear regression, within the linearity ranges, exploiting the absorbance ratio between the main SPR band and the new band relative to the aggregate formation. Then a multivariate approach was followed to perform both Cd2+ 2 + and Pb2+ 2 + quantification by means of Partial Least Square regression (PLS) and target cations distinction by Linear Discriminant Analysis (LDA) applied on Principal Components Analysis (PCA) outputs, in both cases using the entire UV-Vis spectra (350-800 nm) as input data. Finally, the ability to quantify and distinguish between Cd2+ 2 + and Pb2+ 2 + was tested in tap water samples spiked with the two cations in order to confirm the application of the AgNPs@OPE as selective sensor in real samples.

Neuromelanin (NM) plays a well-established role in neurological disorders pathogenesis; the mechanism of action is still discussed and the investigations in this field are limited by NM's complex and heterogeneous composition, insolubility, and low availability from human brains. An alternative can be offered by synthetic NM obtained from dopamine (DA) oxidative polymerization; however, a deep knowledge of the influence of both physicochemical parameters (T, pH, ionic strength) and other compounds in the reaction media (buffer, metal ions, other catecholamines) on DA oxidation process and, consequently, on synthetic NM features is mandatory to develop reliable NM preparation methodologies. To partially fulfill this aim, the present work focuses on defining the role of temperature, buffer and metal ions on both DA oxidation rate and DA oligomer size. DA oxidation in the specific conditions is monitored by UV-Vis spectroscopy and Principal Component Analysis (PCA) is run either on the raw spectra to model the background absorption increase, related to small DA oligomers formation, or on their first derivative to rationalize DA consumption. After having studied three case studies, 3-Way PCA is applied to directly evaluate the effect of temperature and buffer type on DA oxidation in the presence of different metal ions. Despite the proof-of-concept nature of the work and the number of compounds still to be included in the investigation, the preliminary results and the possibility to further expand the chemometric approach represent an interesting contribution to the field of in vitro simulation of NM synthesis.

Albumin is undoubtedly the most studied protein thanks to its widespread diffusion and biochemistry; despite its binding ability towards different dyes, provoking dye's colour change, has been exploited for decades for quantification purposes, the joint effect of working pH, ionic strength, and dye's pKa still remains only sporadically discussed. In the present study, the interaction of Bovine Serum Albumin (BSA) with five dyes belonging to the sulfonephthalein group, Bromophenol Blue (BPB, pKa = 3.75), Bromocresol Green (BCG, pKa = 4.42), Chlorophenol Red (CPR, pKa = 5.74), Bromocresol Purple (BCP, pKa = 6.05) and Bromothymol Blue (BTB, pKa = 6.72), is investigated at four working pH values (3.5, 6.0, 7.5 and 9.0) and two ionic strength conditions by UV-Vis spectroscopy. Principal Component Analysis is then applied to rationalize dye behavior upon BSA addition at each pH value and to summarize the protein effect on dyes' spectral features, identifying three general behaviors. The most relevant systems are then submitted to further characterization involving a solution equilibria study aimed at determining conditional binding constants for the selected DSA-dye adducts and fluorescence, CD, and 1 H NMR spectroscopy to evaluate the binding effect on the species involved.

Human apolipoprotein E (APOE) is a crucial lipid transport glycoprotein involved in various biological processes, including lipid metabolism, immune response, and neurodegeneration. Elevated APOE levels are linked to poor prognosis in several cancers and increased risk of Alzheimer's disease (AD). Therefore, modulating APOE expression presents a promising therapeutic strategy for both cancer and AD. Considering the pivotal role of G-quadruplex (G4) structures in medicinal chemistry as modulators of gene expression, here, we present a newly discovered G-quadruplex (G4) structure within the ApoE gene promoter. Bioinformatic analysis identified 21 potential G4-forming sequences in the ApoE promoter, with the more proximal to the transcription start site, pApoE, showing the highest G-score. Biophysical studies confirmed the folding of pApoE into a stable parallel G4 under physiological conditions, supported by circular dichroism, NMR spectroscopy, UV-melting, and a quantitative PCR stop assay. Moreover, the ability to modulate pApoE-G4 folding was demonstrated by using G4-stabilizing ligands (HPHAM, Braco19, and PDS), which increased the thermal stability of pApoE-G4. In contrast, peptide nucleic acid conjugates were synthesized to disrupt G4 formation, effectively hybridizing with pApoE sequences, and confirming the potential to unfold G4 structures. Overall, our findings provide a mainstay for future therapeutic approaches targeting ApoE-G4s to regulate APOE expression, offering potential advancements in cancer and AD treatment.

The role of dopamine (DA) oxidation in Parkinson’s disease makes to investigate the chemical aspects behind this process: in this paper, we applied Design of Experiments and Principal Component Analysis, to model the DA oxidative polymerization, unveiling the effect exerted by pH, ionic strength (I), temperature, and metal ions. Higher pH and T, lower I and Cu over Fe were found to result in faster DA consumption and smaller oligomers formation. We demonstrated that soft modelling strategies provide a valuable approach to deal with complex biochemical phenomena, regardless system complexity.