Coupling electrochemical and spectroscopic methods for river water dissolved organic matter characterization.

Monitoring dissolved organic matter (DOM) in surface waters is essential for assessing ecosystem health and detecting pollution. However, conventional spectroscopic techniques often provide limited information about the electrochemical behavior of DOM. This study integrates electrochemical impedance spectroscopy (EIS) with classical methods such as UV-Vis absorption and fluorescence spectroscopies to improve DOM characterization in river water samples.

Analysis of ischemic intestinal tissue composition based on visible and near-infrared reflectance hyperspectral imaging and multivariate curve resolution.

Acute mesenteric ischemia (AMI) is a life-threatening, rapidly progressive disease. Conventional diagnostic methods have their own limitations. Therefore, a non-invasive, real-time and efficient diagnostic method is needed.

Assessing neurological and cardiovascular effects caused by pharmaceuticals in river water: Insights from Daphnia magna and Danio rerio embryos.

Pharmaceutical residues in surface waters are an emerging environmental and public health issue, yet their biological impacts on aquatic life remain poorly understood. This study presents a cost-effective bioanalytical framework using Daphnia magna juveniles and zebrafish (Danio rerio) embryos to evaluate neurotoxic and cardiotoxic effects of pharmaceutical mixtures in rivers downstream of wastewater treatment plant (WWTP) effluents. Water samples from three rivers in north-eastern Spain (Besòs, Llobregat, and Onyar) were concentrated up to 5- and 20-fold using solid-phase extraction.

Improving Regions of Interest Multivariate Curve Resolution: Development of an Empirical Metric System through the Study of Passive Sampling Extracts of Wastewater in Antarctica.

The huge potential of liquid chromatography-high-resolution mass spectrometry (LC-HRMS) still comes along with the challenges of data analysis. Regions of interest multivariate curve resolution (ROIMCR) is a valid chemometric tool when working in data-independent acquisition (DIA), since it provides a link between precursor and product ions based on chromatographic and spectral profiles. Still, the quality of the ROIMCR models should be carefully evaluated for a consequent reliable annotation of non-target chemicals.

Enhancing Chemical Image Analysis: Utilizing the Successive Projection Algorithm for Unmixing.

Hyperspectral imaging (HSI) is a powerful, non-invasive analytical technique extensively utilized in chemistry as it simultaneously captures morphological and chemical information from samples across a broad spectrum of chemically informative wavelengths. In this context, morphological information refers to the spatial structure, shape, texture, and distribution of elements within the image. Enhancing its already widespread application requires reducing the computational load of the voluminous hyperspectral images while unmixing signals from different chemical species with unknown spectral fingerprints.

Elucidating mechanisms of action of environmental contaminants from Doce River in Brazilian fish embryos using metabolomics and chemometric methods.

Mining and other essential economic activities have a long historical contamination impact on diverse aquatic environments, such as the Doce River Basin (DRB), in Southeast Brazil. High concentrations of metals combined with organic chemicals released from multiple sources of contaminants may trigger complex toxicity pathways that are complicated to interpret and distinguish. This study aimed to investigate mechanisms of toxicity of environmental chemicals from DRB using a comprehensive untargeted LC-HRMS metabolomics approach (data-independent acquisition of all ion-fragmentation mode), in fish embryos (Rhamdia quelen) exposed to complex chemical mixtures.

Multiblock NIR and MIR spectralprint through AComDim to evaluate the effects of growing site, harvest season, and clone on yerba mate leaves composition.

The composition of yerba mate implies significant potential in the food, pharmaceutical, and cosmetic industries, which requires standardization of the raw material. This study explores the simultaneous influence of growing sites, harvest seasons, and clones on the spectralprint of leaves through near-infrared (NIR) and mid-infrared (MIR) spectroscopy coupled with ANOVA Common Dimensions (AComDim) multivariate analysis. MIR spectroscopy identifies only the main effects of growing site and harvesting season, and the interaction between these factors.

Feasible band boundaries computation in bilinear matrix decomposition using essential data.

Background: Multivariate curve resolution methods are usually confronted with non-unique pure component factors. This rotational ambiguity can be represented by ranges of feasible profiles, which are equally compatible with the imposed constraints. Sensor-wise N-BANDS is an effective algorithm for the calculation of the bounds of feasible profiles in the presence of noise, but suffers from high computational cost.

Metabolomic and sphingolipidomic profiling of human hepatoma cells exposed to widely used pharmaceuticals.

Pharmaceutical compounds have become one of the main contaminants of emerging concern (CECs) due to their high usage and increased release into the environment. This study aims to assess the effects caused by three widely consumed hepatotoxic pharmaceutical compounds: an antibiotic (amoxicillin), an antiepileptic (carbamazepine), and an antidepressant (trazodone), on human health when indirectly exposed to toxicologically relevant concentrations (30, 15, and 7.5 μM for amoxicillin and carbamazepine, and 4, 2, and 1 μM for trazodone).

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

Background: Metabolomics 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.

Linking MS1 and MS2 signals in positive and negative modes of LC-HRMS in untargeted metabolomics using the ROIMCR approach.

Data-independent acquisition (DIA) mode in liquid chromatography (LC) high-resolution mass spectrometry (HRMS) has emerged as a powerful strategy in untargeted metabolomics for detecting a broad range of metabolites. However, the use of this approach also represents a challenge in the analysis of the large datasets generated. The regions of interest (ROI) multivariate curve resolution (MCR) approach can help in the identification and characterization of unknown metabolites in their mixtures by linking their MS1 and MS2 DIA spectral signals.

UV-Vis absorption spectrophotometry and LC-DAD-MS-ESI(+)-ESI(-) coupled to chemometrics analysis of the monitoring of sulfamethoxazole degradation by chlorination, photodegradation, and chlorination/photodegradation.

Sulfamethoxazole (SMX) is one of the most widely used antibiotics worldwide and has been detected at high concentrations in wastewater treatment plant effluents and river waters. In this study, the SMX degradation process combining the simultaneous chlorine oxidation and UV photodegradation is assessed and compared with both photodegradation and chlorine oxidation processes individually. Photodegradation and Chlorine/UV tests were performed using Suntest CPS equipment.

Non-target ROIMCR LC-MS analysis of the disruptive effects of TBT over time on the lipidomics of Daphnia magna.

Introduction: This study has investigated the temporal disruptive effects of tributyltin (TBT) on lipid homeostasis in Daphnia magna. To achieve this, the study used Liquid Chromatography-Mass Spectrometry (LC-MS) analysis to analyze biological samples of Daphnia magna treated with TBT over time. The resulting data sets were multivariate and three-way, and were modeled using bilinear and trilinear non-negative factor decomposition chemometric methods.

SigSel: A MATLAB package for the pre and post-treatment of high-resolution mass spectrometry signals using the ROIMCR methodology.

The Regions of Interest Multivariate curve Resolution (ROIMCR) methodology has gained significance for analyzing mass spectrometry data. The new SigSel package improves the ROIMCR methodology by providing a filtering step to reduce computational costs and to identify chemical compounds giving low-intensity signals. SigSel allows the visualization and assessment of ROIMCR results and filters out components resolved as interferences and background noise.

Regions of Interest Multivariate Curve Resolution Liquid Chromatography with Data-Independent Acquisition Tandem Mass Spectrometry.

New data-independent acquisition (DIA) modes coupled to chromatographic separations are opening new perspectives in the processing of massive mass spectrometric (MS) data using chemometric methods. In this work, the application of the regions of interest multivariate curve resolution (ROIMCR) method is shown for the simultaneous analysis of MS1 and MS2 DIA raw data obtained by liquid chromatography coupled to quadrupole-time-of-flight MS analysis. The ROIMCR method proposed in this work relies on the intrinsic bilinear structure of the MS1 and MS2 experimental data which allows us for the fast direct resolution of the elution and spectral profiles of all sample constituents giving measurable MS signals, without needing any further data pretreatment such as peak matching, alignment, or modeling.

A Multi-Level Systems Biology Analysis of Aldrin's Metabolic Effects on Prostate Cancer Cells.

Although numerous studies support a dose-effect relationship between Endocrine disruptors (EDs) and the progression and malignancy of tumors, the impact of a chronic exposure to non-lethal concentrations of EDs in cancer remains unknown. More specifically, a number of studies have reported the impact of Aldrin on a variety of cancer types, including prostate cancer. In previous studies, we demonstrated the induction of the malignant phenotype in DU145 prostate cancer (PCa) cells after a chronic exposure to Aldrin (an ED).

A biochemical and lipidomic approach to perceive Halimione portulacoides (L.) response to mercury: An environmental perspective.

The impact of hazardous materials, such as Hg, on life is far from being understood and due to the high number of polluted sites it has generated great concern. A biochemical and lipidomic approach was used to assess the effects of Hg on the saltmarsh halophyte Halimione portulacoides. Plants were collected at two sites of a Hg contaminated saltmarsh.

Phenotypic and Metabolomic Characterization of 3D Lung Cell Cultures Exposed to Airborne Particulate Matter from Three Air Quality Network Stations in Catalonia.

Air pollution constitutes an environmental problem that it is known to cause many serious adverse effects on the cardiovascular and respiratory systems. The chemical characterization of particulate matter (PM) is key for a better understanding of the associations between chemistry and toxicological effects. In this work, the chemical composition and biological effects of fifteen PM air filter samples from three air quality stations in Catalonia with contrasting air quality backgrounds were investigated.

Mutual information map as a new way for exploring the independence of chemically meaningful solutions in two-component analytical data.

In the present contribution, a new approach based on mutual information (MI) is proposed for exploring the independence of feasible solutions in two component systems. Investigating how independent are different feasible solutions can be a way to bridge the gap between independent component analysis (ICA) and multivariate curve resolution (MCR) approaches and, to the best of our knowledge, has not been investigated before. For this purpose, different chromatographic and hyperspectral imaging (HSI) datasets were simulated, considering different noise levels and different degrees of overlap for two-component systems.

Effects of sulfamethoxazole exposure on mussels (Mytilus galloprovincialis) metabolome using retrospective non-target high-resolution mass spectrometry and chemometric tools.

In this work, the Regions of Interest-Multivariate Curve Resolution (ROIMCR) method is proposed for the analysis of non-target metabolomics data. Samples from marine mussels (Mytilus galloprovincialis) exposed to a sublethal concentration (10 μg/L) of sulfamethoxazole (SMX) during 4 days in different seasonal conditions (summer and winter) were analyzed by High-Performance Liquid Chromatography - High-Resolution Mass Spectrometry (HPLC-HRMS) to study the effect of their exposure to SMX and the different seasonal conditions. The Regions of Interest (ROI) procedure has been applied for data filtering, compression, preprocessing and storage steps.

Quantification strategies for two-dimensional liquid chromatography datasets using regions of interest and multivariate curve resolution approaches.

In this work, three chemometrics-based approaches are compared for quantification purposes when using two-dimensional liquid chromatography (LC×LC-MS), taking as a study case the quantification of amino acids in commercial drug mixtures. Although the approaches have been already used for one-dimensional gas or liquid chromatography, the main novelty of this work is the demonstration of their applicability to LC×LC-MS datasets. Besides, steps such as peak alignment and modelling, commonly applied in this type of data analysis, are not required with the approaches proposed here.

Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies.

The use of chemometric methods based on the analysis of variances (ANOVA) allows evaluation of the statistical significance of the experimental factors used in a study. However, classical multivariate ANOVA (MANOVA) has a number of requirements that make it impractical for dealing with metabolomics data. For this reason, in recent years, different options have appeared that overcome these limitations.

Combining Pharmacokinetics and Vibrational Spectroscopy: MCR-ALS Hard-and-Soft Modelling of Drug Uptake In Vitro Using Tailored Kinetic Constraints.

Raman microspectroscopy is a label-free technique which is very suited for the investigation of pharmacokinetics of cellular uptake, mechanisms of interaction, and efficacies of drugs in vitro. However, the complexity of the spectra makes the identification of spectral patterns associated with the drug and subsequent cellular responses difficult. Indeed, multivariate methods that relate spectral features to the inoculation time do not normally take into account the kinetics involved, and important theoretical information which could assist in the elucidation of the relevant spectral signatures is excluded.

Flexible Implementation of the Trilinearity Constraint in Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) of Chromatographic and Other Type of Data.

Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) can analyze three-way data under the assumption of a trilinear model using the trilinearity constraint. However, the rigid application of this constraint can produce unrealistic solutions in practice due to the inadequacy of the analyzed data to the characteristics and requirements of the trilinear model. Different methods for the relaxation of the trilinear model data requirements have been proposed, like in the PARAFAC2 and in the direct non-trilinear decomposition (DNTD) methods.