Multivariate Curve Resolution With Alternating Least Squares Research Articles

Evaluation of the influence of temperature and time exposure on Peruvian Maca (Lepidium meyenii) flour samples through NIR spectroscopy and chemometrics

In this study, the three most known varieties of the Peruvian Maca (Lepidium meyenii), yellow, red, and black, were fractionated in multiple sub-samples and submitted to heating (from 25 to 200 °C) during one hour at 10-minute intervals to be measured using the near-infrared (NIR) spectroscopy after 24 h resting in a desiccator. The study focused on changes related to heating for considering new applications on Maca-enriched food products such as bread, cakes, and cookies, which are prepared over high temperatures during different time exposures. To perform the experiment, 9 portions of 1 g of each sample remained in an oven at each temperature (25, 50, 100, 150, 160, 170, 180, 190, and 200 °C) for different times (10, 20, 30, 40, 50, and 60 min). The principal component analysis (PCA) and the multivariate curve resolution with alternating least squares (MCR-ALS) were applied to interpret the results. In general, it was observed that spectral variations start to occur from heating samples from above 100 °C. The recovered signals indicated that most spectral variations occurred in regions assigned to NH vibrations, suggesting that heating influences the protein and/or macamides structures, which are responsible for most of the properties attributed to the Peruvian Maca. The yellow Maca phenotype was the most susceptible to spectral variations after heating, while the black Maca displayed the slowest spectral transformations. This study may encourage deeper investigations into the use of Maca-derived foods regarding their health-beneficial effects after heating to indicate the limitations of Maca applications.

Applying multivariate curve resolution modelling combined with discriminant tools on near-infrared spectra for distinguishing between cheese varieties and stages of ripening

In this study, near-infrared (NIR) spectra were employed to monitor the ripening process of two kinds of soft cheese produced in the Extremadura region of Spain, manufactured by two different producers, “Torta del Casar” and “Queso de la Serena”. Spectra were collected from the interior of the cheeses and the rind and analysed using appropriate chemometric techniques to distinguish between the two varieties and among different weeks of the maturation process. Different chemometric tools, including multivariate curve resolution with alternating least-squares (MCR-ALS), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and feed-forward artificial neural networks (FF-ANN), were utilised, resulting in outstanding discrimination outcomes with sensitivity, precision, specificity, and accuracy reaching values c.a. 1.00 in optimal scenarios. More comprehensive information was acquired from the rind spectra analysis, indicating that the sampling process can be performed without disturbing the cheese in a non-destructive way. Remarkably, the capability to distinguish between various weeks of ripening for both cheeses could enable manufacturers to produce market-ready products earlier than the typically established timeline.

Multivariate curve resolution-based data fusion approaches applied in 1H NMR metabolomic analysis of healthy cohorts

BackgroundMetabolomics plays a critical role in deciphering metabolic alterations within individuals, demanding the use of sophisticated analytical methodologies to navigate its intricate complexity. While many studies focus on single biofluid types, simultaneous analysis of multiple matrices enhances understanding of complex biological mechanisms. Consequently, the development of data fusion methods enabling multiblock analysis becomes essential for comprehensive insights into metabolic dynamics. ResultsThis study introduces a novel guideline for jointly analyzing diverse metabolomic datasets (serum, urine, metadata) with a focus on metabolic differences between groups within a healthy cohort. The guideline presents two fusion strategies, ‘Low-Level data fusion’ (LLDF) and ‘Mid-Level data fusion’ (MLDF), employing a sequential application of Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS), linking the outcomes of successive analyses. MCR-ALS is a versatile method for analyzing mixed data, adaptable at various stages of data processing—encompassing resonance integration, data compression, and exploratory analysis. The LLDF and MLDF strategies were applied to 1H NMR spectral data extracted from urine and serum samples, coupled with biochemical metadata sourced from 145 healthy volunteers. SignificanceBoth methodologies effectively integrated and analysed multiblock datasets, unveiling the inherent data structure and variables associated with discernible factors among healthy cohorts. While both approaches successfully detected sex-related differences, the MLDF strategy uniquely revealed components linked to age. By applying this analysis, we aim to enhance the interpretation of intricate biological mechanisms and uncover variations that may not be easily discernible through individual data analysis.

Mercury (II) sensing using a simple turn-on fluorescent graphene oxide based aptasensor in serum and water samples

A simple, highly sensitive, and selective fluorometric aptasensing platform based on aptamer and graphene oxide (GO) is proposed for the determination of mercury (II) ion (Hg2+). In the designed assay, two aptamer probes, a carboxy-fluorescein (FAM) labeled aptamer (aptamer A) and its complementary (aptamer B) with partial complement containing several mismatches and GO as the quencher were used. In the absence of Hg2+, both A and B aptamers were adsorbed on the surface of GO by π-π-stacking, leading to fluorescence quenching of FAM due to fluorescence resonance energy transfer (FRET). Upon exposure to Hg2+, the A and B aptamer strands bind Hg2+ and form T-Hg2+-T complexes, leading to the formation of a stable double-stranded aptamer. The double-stranded aptamer is detached from the GO surface, resulting in the recovery of FAM fluorescence. The fluorescence intensity (FI) of the developed sensor was correlated with the Hg2+ concentration under optimized experimental conditions in two wide linear ranges, even in the presence of 10 divalent cations as interferences. The linear ranges were obtained from 200.0 to 900.0 fM and 5.0 to 33.0 pM, a limit of detection (LOD) of 106.0 fM, and a limit of quantification (LOQ) of 321.3 fM. The concentration of Hg2+ was determined in five real samples containing three water and two serum samples, using spiking and standard addition methods and the results were compared with the spiked amounts and atomic absorption (AAS) as standard method respectively, with acceptable recoveries. Furthermore, in the standard addition method, to overcome the effects of matrix influence of real samples in quantitative predictions, the excitation-emission matrix (EEM) data for samples was simultaneously analyzed by multivariate curve resolution with alternating least squares (MCR-ALS) as a second-order standard addition method (SOSAM).

Uv–Vis molecular spectral resolution of multi-level mixture design-fingerprints of yerba mate leaves by chemometric methods

This study proposes the use of multivariate curve resolution with alternating least squares (MCR-ALS) to comprehensively analyze mixture design-fingerprints in yerba mate leaves, investigating the influence of multi-level factors such as sexual dimorphism (male and female plants) and cultivation systems (monoculture and agroforestry). Plant extracts were obtained according to a mixture design with ethanol, dichloromethane, and hexane and their binary and ternary combinations. Through simultaneous resolution of the diverse profiles obtained using these different solvents, seven components were successfully identified, collectively explaining 98.57% of the data variance. Each component was associated with distinct absorption bands indicative of their chemical composition with compounds such as chlorogenic acids, xanthophylls, catechin, caffeine, kaempferol, lycopene, carotene, and chlorophyll. The MCR-ALS resolved profiles revealed significant variations in component concentrations across experimental conditions, facilitating the determination of the effects of sexual dimorphism and cultivation systems on yerba mate metabolism. The ANOVA-simultaneous component analysis (ASCA) highlighted the chemical differences of yerba mate leaves due to the cultivation system. Binary and ternary mixture extractants resulted to be more effective than pure solvents in determining the significance of effects on the chemical composition of yerba mate. Partial least squares discriminant analysis (PLS-DA) demonstrated the most distinct metabolic profiles of yerba mate leaves under monoculture and agroforestry conditions, with specific components playing crucial roles in their discrimination. This study shows the robustness of the MCR-ALS method in resolving complex chemical data, offering valuable insights into the diverse chemical composition of yerba mate under different experimental conditions. The congruence of results across various analyses reinforced the reliability and validity of the methodology, emphasizing its potential use for exploring the composition and properties of natural products.

Excitation-emission fluorescence spectroscopy coupled with PARAFAC and MCR-ALS with area correlation for investigation of jet fuel contamination

The contamination of jet fuel has gained attention in the past years as a notable factor in aircraft accidents. Identifying the contamination sources is still a challenge, especially when they have a similar composition to the fuel, such as kerosene solvent (KS). A novel analytical methodology was developed by combining a set of excitation-emission matrix (EEM) fluorescence to area constrained multivariate curve resolution with alternating least-squares (MCR-ALS) and PARAllel FACtor (PARAFAC) analysis, in order to identify KS in blends with JET-A1. For this purpose, a dataset with 50 samples (KS and JET-A1 blends, 2.0–100% v/v) was used to build the multivariate models. Both PARAFAC and MCR-ALS allowed fuel quantification with 4.64% and 3.46% RMSEP, respectively; both models (PARAFAC and MCR-ALS) could quantify KS with high accuracy (RMSEP <5.36%). In addition, MCR-ALS model was able to recover the pure spectral profiles of KS, JET-A1 and interferers. GC-MS data of the samples proved the composition similarities between both petroleum distillates, thus being inefficient for identifying the contamination. These results indicate that the development of multivariate models using EEM was the key for contributing with a new low-cost and accurate method for on-line screening of jet fuel contamination.

A conventional and chemometric analytical approach to solving urea determination with accuracy and precision.

Urea is an essential molecule usually detected using spectroscopy, particularly ultraviolet and visible spectroscopy (UV-vis). However, its detection represents a not always fully acknowledged issue. Its concentration dependency has raised questions about the reliability of the UV-vis results. Derivatization reactions, common alternatives to achieve accuracy and precision with UV-vis measurements, still represent an additional step in the measurement process. Besides the problems mentioned earlier, urea forms complex mixtures in aqueous mediums. Therefore, this work proposes to investigate the accuracy and precision of urea determination by UV-vis spectroscopy in the pure form and derivatized with para-dimethylaminobenzaldehyde. The results show that UV-vis spectroscopy could not quantify urea in both forms with precision and accuracy. On the other hand, when applying multivariate curve resolution with alternating least squares (MCR-ALS) to the UV-vis data, the pure urea analytical signal is mathematically separated. Then, those parameters of merit were successfully achieved.

Crystallization Kinetics in an Immiscible Polyolefin Blend.

Motivated by the problem of brittle mechanical behavior in recycled blends of high density polyethylene (HDPE) and isotactic polypropylene (iPP), we employ optical microscopy, rheo-Raman, and differential scanning calorimetry (DSC) to measure the composition dependence of their crystallization kinetics. Raman spectra are analyzed via multivariate curve resolution with alternating least-squares (MCR-ALS) to provide component crystallization values. We find that iPP crystallization behavior varies strongly with blend composition. Optical microscopy shows that three crystallization kinetic regimes correspond to three underlying two-phase morphologies: HDPE droplets in iPP, the inverse, and cocontinuous structures. In the HDPE droplet regime, iPP crystallization temperature decreases sharply with increasing HDPE composition. For cocontinuous morphologies, iPP crystallization is delayed, but the onset temperature changes little with the exact blend composition. In the iPP droplet regime, the two components crystallize nearly concurrently. Rheological measurements are consistent with these observations. DSC indicates that the enthalpy of crystallization of the blends is less than the weighted values of the individual components, providing a possible clue for the decreased iPP crystallization temperatures.

A study of the heat-mediated phase transformations of praziquantel hydrates. Evaluation of their impact on the dissolution rate

Comprehensive knowledge of the critical properties of the active pharmaceutical ingredients is a requirement within the modern concept of quality. Praziquantel hemihydrate (HH) and monohydrate (MH) are new solid forms of this antihelmintic agent, which have better solubility properties than the commercial anhydrous solid form (polymorph A). The thermal stability of the hydrates was evaluated, aiming to understand any possible transformation (amorphization, change to a less soluble form).Therefore, HH and MH were prepared along with the related anhydrous solid forms A and B, and characterized employing solid-state nuclear magnetic resonance, powder X-ray diffraction, mid and near infrared spectroscopy, thermal methods and the intrinsic dissolution rate.The transformations of HH and MH under thermal stress conditions were monitored through a variable temperature infrared spectroscopy approach, assisted by multivariate curve resolution with alternating least squares (MCR-ALS), finding that HH undergoes a two-step transformation (HH→B→A) to form A, whereas MH dehydrates directly into form A. This was further confirmed by conventional calorimetric methods (differential scanning calorimetry and thermogravimetry) and powder X-ray diffractometry.The impact of changes in the stressed solid forms and their dissolution rates was also assessed. Significant differences in dissolution performance were found regarding the solid forms produced as a consequence of thermally-induced dehydration.

Multivariate strategy for identifying and quantifying jet fuel contaminants by MCR-ALS/PLS models coupled to combined MIR/NIR spectra.

The investigation and control of jet fuel contamination for private aircrafts has gained attention due to the softer monitoring in comparison to commercial aviation. The possible contamination with kerosene solvent (KS) makes this investigation more challenging, since it has physicochemical similarities with jet fuel.To help solve this problem, a chemometric methodology was applied in this researchcombining multivariate curve resolution with alternating least squares (MCR-ALS) and partial least squares (PLS) models coupled to near- and mid-infrared spectroscopies (MIR/NIR) in order to detect and quantify KS in blends with JET-A1 using 23 samples (5-60% v/v). Additionally, 98 samples were stored for 60days, and principal component analysis, genetic algorithm, and successive projections algorithm were coupled to linear discriminant analysis (PCA-LDA, GA-LDA, and SPA-LDA) in order to classify the blends according to the bands assigned to oxidation products, such as phenols and carboxylic acids. GA-LDA and SPA-LDA models were accurate and reached 100% sensitivity and specificity. Physicochemical analysis was not able to detect the presence of KS in contaminated jet fuel samples, even in high concentrations. The use of MIR-NIR combined spectra improved the quantification results, thus decreasing the experimental error from 5.22% (using only NIR) to 1.64%. PLS regression quantified the content of KS with high accuracy (RMSEP < 1.64%, R2 > 0.995). The MCR-ALS model stood out for recovering the spectral profile of kerosene solvent by segregating it from jet fuel spectra. The development of models using chemometric tools contributed to a fast, low-cost, and efficient process for quality control that can be applied in the fuel industry.

Raman spectra and structure of hydrogen‐bonded water oligomers in tetrahydrofuran–H2O binary solutions

AbstractRaman spectra of hydrogen‐bonded water oligomers have been resolved from concentration‐dependent Raman spectra of tetrahydrofuran (THF)–water binary solutions with the use of multivariate curve resolution with alternating least‐squares (MCR‐ALS) analysis, assisted by hypothetical addition multivariate analysis with numerical differentiation (HAMAND) procedure. A total of 38 Raman spectra acquired in the broad spectral range of 700–3810 cm−1were analyzed in a multistep procedure to explain the spectral variations in terms of four components: pure THF, Spectra A, Spectra B, and Spectra C. A four‐component MCR‐ALS analysis was performed with initial guesses by HAMAND analysis to refine the spectral components and determine their concentration dependence. Of the MCR‐ALS resolved three spectra, A was identified as THF(H2O), B as THF(H2O)2with two hydrogens attached to THF, and C as THF(H2O)nhaving two or more linearly hydrogen‐bonded oligomers of water (n = 2–4). Observed spectral changes of the OH stretch vibrations of water and those of the C–C/C–O stretches of THF were well reproduced by quantum chemical calculations. Formation of hydrogen‐bonded water oligomers in THF–water complexes has thus been revealed. The present study provides a new insight into the basics of hydrogen bonding formation in aqueous systems. It also provides experimental methodology for extracting concentration‐dependent minor spectral components contained in complex condensed‐phase Raman spectra.

Multivariate assessment for predicting antioxidant activity from clove and pomegranate extracts by MCR-ALS and PLS models combined to IR spectroscopy

This study evaluated the feasibility of infrared (MIR/NIR) spectroscopy coupled to chemometrics as an alternative method for determining the antioxidant activity (AA%) of pomegranate (Punica granatum) and clove (Syzygium aromaticum) alcoholic extracts versus the conventional DPPH method. Multivariate curve resolution with alternating least squares (MCR-ALS) and Partial least squares (PLS) regression were efficient to predict the AA%, thus providing good accuracy and low residuals compared to the standard method. The MCR-ALS combined with NIR data stood out among the other models with R2 ≥ 0.962 and RMSEP ≤ 3.38 %; furthermore, this technique presents the great feature of recovering the pure spectral profile of the analytes and identifying interferents in the sample. The application of chemometrics tools to predict the antioxidant activity of natural extracts resulted in a greener, low-cost and efficient process for the food industry.

Distributional homogeneity and penetration depth assessment of antibiotic added by surface coating to pellets with mid Infrared imaging and multivariate curve resolution

Fourier Transform Mid Infrared with Attenuated Total Reflection Imaging (FTIR-ATR imaging) and Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS) were used in a multiblock fashion to study the presence, distribution and penetration depth of very low concentrations of florfenicol (FF) in a complex matrix like feed pellets for salmonids. Images from the surface, at 150 µm deep and 200 µm deep from the surface were analyzed to certify the penetration power of FF added by surface coating methodology. Besides, the unique homogeneity index was calculated in order to evaluate the distributional homogeneity of each component. The results demonstrated the reliability of MCR-ALS in studying the distributional homogeneity of FF. It was demonstrated that FF remains mostly on the surface of the pellets with almost no penetration. The rest of the components of the pellets (oil, protein and carbohydrates) were also analyzed. These three nutrients are distributed on the three layers analyzed with a relatively homogeneous location, being carbohydrates (%H = 51 ± 3) the component with the best homogeneous distribution, unlike protein (%H = 45 ± 5), and oil (%H = 40 ± 7). This is the first publication where the penetration of an antibiotic, added with surface-coating to feed pellets, was analyzed with FTIR-ATR imaging and multivariate analysis, showing the contribution these analytical tools can make to the medicated feed industry.

Digital images and independent components analysis in the determination of bioactive compounds from grape juice

Analytical methods based on digital images coupled to chemometric tools are promising alternatives for developing analytical methodologies in determining quality parameters in foods. The total phenolics, monomeric anthocyanins, and tannin content were quantified based on digital images and pseudo-univariate calibration performed from Independent Components Analysis (ICA). Differences between ICA, Principal Component Analysis (PCA), and Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS), highlighting the chosen ICA for this proposal, were explained. In all pseudo-univariate calibration curves, correlation coefficients higher than 0.9 were achieved. The results suggest that digital images coupled with ICA can give applicable pseudo-univariate calibration models to determine bioactive compounds. Even more, the device to obtain an image is cheaper than most analytical instruments. Digital images may have a promising future in developing analytical methodologies for complex samples such as foods from a pseudo-univariate calibration perspective (where a small data set is needed in the calibration step).

Insights into the Structural Dynamics of Pt/CeO2 Single-Site Catalysts during CO Oxidation

Despite their high atomic dispersion, single site catalysts with Pt supported on CeO2 were found to have a low activity during oxidation reactions. In this study, we report the behavior of Pt/CeO2 single site catalyst under more complex gas mixtures, including CO, C3H6 and CO/C3H6 oxidation in the absence or presence of water. Our systematic operando high-energy resolution-fluorescence-detected X-ray absorption near-edge structure (HERFD-XANES) spectroscopic study combined with multivariate curve resolution with alternating least squares (MCR-ALS) analysis identified five distinct states in the Pt single site structure during CO oxidation light-off. After desorption of oxygen and autoreduction of Pt4+ to Pt2+ due to the increase of temperature, CO adsorbs and reduces Pt2+ to Ptδ+ and assists its migration with final formation of PtxΔ+ clusters. The derived structure–activity relationships indicate that partial reduction of Pt single sites is not sufficient to initiate the conversion of CO. The reaction proceeds only after the regrouping of several noble metal atoms in small clusters, as these entities are probably able to influence the mobility of the oxygen at the interface with ceria.

Cocrystal Formation through Solid-State Reaction between Ibuprofen and Nicotinamide Revealed Using THz and IR Spectroscopy with Multivariate Analysis

Cocrystallisation can enhance the solubility and bioavailability of active pharmaceutical ingredients (APIs); this method may be applied to improve the availability of materials that were previously considered unsuitable. Terahertz (THz) spectroscopy provides clear, substance-specific fingerprint spectra; the transparency of the THz wave allows us to probe inside a sample to identify medicinal materials. In this study, THz and infrared (IR) spectroscopy were used to characterise cocrystallisation in solid-phase reactions between ibuprofen and nicotinamide. Multivariate curve resolution with alternating least squares (MCR-ALS) was applied to both time-dependent THz and IR spectra to identify the intermolecular interactions between these cocrystallising species. The analytical results revealed cocrystal formation through a two-step reaction, in which the steps were dominated by thermal energy and water vapour, respectively. We infer that the presence of water molecules significantly lowered the activation energy of cocrystal formation.

Evaluation of the acidic strengths on electronic ground and excited states of proton transfer dye using Excitation-Emission fluorescence matrix (EEM) and Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS)

Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS) was for the first time successfully used to evaluate an intricate photophysical behavior, where deprotonation on the electronic ground state (S0), intra and intermolecular proton transfer processes (ESPT and ESIPT) on the electronic excited state (S1) can simultaneously be presented. In this sense, the organic dye 2-(2′-hydroxyphenyl)benzothiazole (HBT) was used as a proof-of-concept model, where MCR-ALS showed to be a powerful tool for discriminate chemical reactions that occur concomitantly on different potential energy surfaces, which include photochemical reactions. As a result, the chemometric method showed to be a straightforward approach for the determination of the acidic strengths of those equilibria were estimated as 8.61 and 1.11 to hydroxyl deprotonation on electronic ground and excited states, respectively.

Estrogens determination through disposable pipette extraction coupled to ultraviolet spectroscopy and nonlinear pseudo‐univariate calibration: Solving rank deficiency problems

AbstractIn this study, the disposable pipette extraction (DPX) approach was employed to prepare water samples containing the hormones 17‐β‐estradiol (beta‐EST), estrone (ETR), estriol (EST), and 17‐α‐ethinylestradiol (17‐EE). Both multivariate and univariate experimental designs were used to optimize the DPX procedure through ultraviolet (UV) and high‐performance liquid chromatography (HPLC). The determination of estrogens in water through UV spectroscopy exhibited rank deficiency, and after trying several strategies, it was possible to suggest that the rank deficiency in the spectra was solved by column augmented matrix strategy afterward complexing the hormones with β‐cyclodextrin, in which the relative concentration recovered by the multivariate curve resolution with alternating least squares (MCR‐ALS) to each hormone was employed in the development of nonlinear pseudo‐univariate calibration models based on support vector machine (SVM) approach. The models were validated through parameters of merit determination and by comparing the models results with those obtained by HPLC. The alternative methodology for the determination of estrogens in water through UV spectroscopy is an environmentally friendly analytical methodology employing less solvent and with high throughput. Moreover, it is not possible to determinate these hormones without using the chemometric approaches, mainly due to the rank deficiency, which needs to be broken to allow for the differentiation between the substances and later modeling.

Soy Protein Isolate Films Incorporated with Pinhão (Araucaria angustifolia (Bertol.) Kuntze) Extract for Potential Use as Edible Oil Active Packaging

One of the traditional residues obtained from pinhao seeds (Araucaria angustifolia (Bertol.) Kuntze) consists of an aqueous extract produced from the cooking process, which presents a significant concentration of phenolic compounds with antioxidant properties. In this work, soy protein isolate (SPI) films with different concentrations of pinhao cooking water extract (EP, 0.5, 1, and 2% wt/wt) were produced and physical properties, microstructure, and antioxidant capacity were investigated. The films were applied as packaging (as sachet-type) for linseed oil, and the oil oxidative stability was evaluated during 10 days under accelerated storage condition (60 °C) by conventional procedures (peroxide index, specific extinction coefficient, and UV-Vis spectrophotometry) and by multivariate curve resolution with alternating least squares (MCR-ALS) chemometric method. The film with EP contributed to the oxidative stability of linseed oil being an interesting alternative of active biodegradable packaging for edible oils.

Emerging micropollutants determination by NIR spectroscopy using pseudo-univariate calibration and TF-SPME coupled with 96-well plate system

In this study, an innovative approach was developed to determine simultaneously the emerging micropollutants bisphenol A (BPA), 3-(4-methylbenzylidene) camphor (4-MBC), triclocarban (TCC), benzophenone (BZP) and 2-ethyl-hexyl-4-trimethoxycinnamate (EHMC) in water employing near infrared spectroscopy (NIR) coupled with thin film solid-phase microextraction (TF-SPME) using the chemometric tool of multivariate curve resolution with alternating least squares (MCR-ALS). In addition, a 96-well plate system was employed to increase the analysis throughput of the proposed methodology. Samples were submitted to TF-SPME procedure (previously optimized in previous work to exhibit the best performance for extraction and desorption conditions) and its extracts were analyzed by NIR. A pseudo-univariate calibration model for each analyte was developed by associating the relative concentration obtained using MCR-ALS with the reference concentration (i.e. theoretical concentration). The correlation coefficients obtained were 0.9238, 0.8722, 0.7872, 0.8856 and 0.9128 for BPA, 4-MBC, TCC, BZP and EHMC, respectively. With the use of these pseudo-univariate models, the determination of the analytes exhibited absolute errors lowers than those of the chromatographic technique for BZP and TCC. For BPA, 4-MBC and EHMC the absolute error was lower than 10 µg L − 1. This approach can provide a cheap and rapid strategy for the direct quantification of these micropollutants in water samples.