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. Compound annotation and identification can be achieved directly by the comparison of the ROIMCR-resolved MS1 and MS2 spectra with those from standards or from mass spectral libraries. ROIMCR elution profiles of the resolved components can be used to build calibration curves for the prediction of their concentrations in complex unknown samples. The application of the proposed procedure is shown for the analysis of mixtures of per- and polyfluoroalkyl substances in standard mixtures, spiked hen eggs, and gull egg samples, where these compounds tend to accumulate.

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. These methods allowed for the identification of specific patterns in the data and provided insight into the effects of TBT on lipid homeostasis in Daphnia magna. OBJECTIVES: Investigation of how are the changes in the lipid concentrations of Daphnia magna pools when they were exposed with TBT and over time using non-targeted LC-MS and advanced chemometric analysis. METHODS: The simultaneous analysis of LC-MS data sets of Daphnia magna samples under different experimental conditions (TBT dose and time) were analyzed using the ROIMCR method, which allows the resolution of the elution and mass spectra profiles of a large number of endogenous lipids. Changes obtained in the peak areas of the elution profiles of these lipids caused by the dose of TBT treatment and the time after its exposure are analyzed by principal component analysis, multivariate curve resolution-alternative least square, two-way ANOVA and ANOVA-simultaneous component analysis. RESULTS: 87 lipids were identified. Some of these lipids are proposed as Daphnia magna lipidomic biomarkers of the effects produced by the two considered factors (time and dose) and by their interaction. A reproducible multiplicative effect between these two factors is confirmed and the optimal approach to model this dataset resulted to be the application of the trilinear factor decomposition model. CONCLUSION: The proposed non-targeted LC-MS lipidomics approach resulted to be a powerful tool to investigate the effects of the two factors on the Daphnia magna lipidome using chemometric methods based on bilinear and trilinear factor decomposition models, according to the type of interaction between the design factors.

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. In this study, it is proposed for the first time the analysis of MS1 and MS2 DIA signals in positive and negative electrospray ionization modes simultaneously to increase the coverage of possible metabolites present in biological systems. In this work, this approach has been tested for the detection and identification of the amino acids present in a standard mixture solution and in fish embryo samples. The ROIMCR analysis allowed for the identification of all amino acids present in the analyzed mixtures in both positive and negative modes. The methodology allowed for the direct linking and correspondence between the MS signals in their different acquisition modes. Overall, this approach confirmed the advantages and possibilities of performing the proposed ROIMCR simultaneous analysis of mass spectrometry signals in their differing acquisition modes in untargeted metabolomics studies.

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. In this work, the trilinearity constraint of MCR-ALS is adapted to different data scenarios where the profiles of all or some of the components of the system are shifted (not equally synchronized) or even change their shape among different slices in one of their data modes. This adaptation is especially useful in gas and liquid chromatography (GC and LC) and in Flow Injection Analysis (FIA) with multivariate spectroscopic detection. In a first data example, a synthetic LC-DAD dataset is built to investigate the possibilities of the proposed method to handle systematic changes (shifts) in the retention times of the elution profiles and the results are compared with those obtained using alternative methods like ATLD, PARAFAC, PARAFAC2 and DNTD. In a second data example, multiple wine samples were simultaneously analyzed by GC-MS where elution profiles presented large deviations (shifts) in their peak retention times, although they still preserve the same peak shape. Different modelling scenarios are tested and the results are also compared. Finally, in the third example, sample mixtures of acid compounds were analyzed by FIA under a pH gradient and monitored by UV spectroscopy and also examined by different chemometric methods using a different number of components. In this case, however, the departure of the trilinear model comes from the acid base speciation of the system depending on the pH more than from the shifting of the FIA diffusion profiles.

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. In this work, we evaluate the performance of three of these multivariate ANOVA-based methods (ANOVA simultaneous component analysis-ASCA, regularized MANOVA-rMANOVA, and Group-wise ANOVA-simultaneous component analysis-GASCA) in the framework of metabolomics studies. Our main goals are to compare these various ANOVA-based approaches and evaluate their performance on experimentally designed metabolomic studies to find the significant factors and identify the most relevant variables (potential markers) from the obtained results. Two experimental data sets were generated employing liquid chromatography coupled to mass spectrometry (LC-MS) with different complexity in the design to evaluate the performance of the statistical approaches. Results show that the three considered ANOVA-based methods have a similar performance in detecting statistically significant factors. However, relevant variables pointed by GASCA seem to be more reliable as there is a strong similarity with those variables detected by the widely used partial least squares discriminant analysis (PLS-DA) method.

Big (Bio)Chemical Data Mining Using Chemometric Methods: A Need for Chemists.

This Review summarizes how big (bio)chemical data (BBCD) can be analyzed with multivariate chemometric methods and highlights some of the important challenges faced by modern analytical researches. Here, the potential of chemometric methods to solve BBCD problems that are being encountered in chromatographic, spectroscopic and hyperspectral imaging measurements will be discussed, with an emphasis on their applications to omics sciences. In addition, insights and perspectives on how to address the analysis of BBCD are provided along with a discussion of the procedures necessary to obtain more reliable qualitative and quantitative results. In this Review, the importance of "big data" and of their relevance to (bio)chemistry are first discussed. Thereafter, analytical tools which can produce BBCD are presented as well as the theoretical background of chemometric methods and their limitations when they are applied to BBCD. Finally, the importance of chemometric methods for the analysis of BBCD in different chemical disciplines is highlighted with some examples. In this work, we have tried to cover many of the current applications of big data analysis in the (bio)chemistry field.

Application of chemometric methods to the analysis of multimodal chemical images of biological tissues.

Current histology techniques, such as tissue staining or histochemistry protocols, provide very limited chemical information about the tissues. Chemical imaging technologies such as infrared, Raman, and mass spectrometry imaging, are powerful analytical techniques with a huge potential in describing the chemical composition of sample surfaces. In this work, three images of the same tissue slice using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry, infrared microspectroscopy, and an RGB picture from a conventional hematoxylin/eosin (H/E) staining are simultaneously analyzed. These fused images were analyzed by multivariate curve resolution-alternating least squares (MCR-ALS), which provided, for each component, its distribution within the tissue surface, its IR spectrum fingerprint, its characteristic mass values, and the contribution of the RGB channels of the H/E staining. Compared with the individual analysis of each of the images alone, the fusion of the three images showed the relationship between the different types of chemical/biological information and enabled a better interpretation of the tissue under study. In addition, the least-squares projection of the MCR-ALS resolved spectra of components at low spatial resolution onto the IR and RBG images at high spatial resolution, provided a better delimitation of the sample constituents on the image, giving a more precise description of their distribution on the investigated tissue. The application of this procedure can be of interest in different research areas in which a good description of the spatial distribution of the chemical constituents of the samples is needed, such as in biomedicine, food, or environmental research.

MCR-ALS analysis of 1H NMR spectra by segments to study the zebrafish exposure to acrylamide.

Metabolomics is currently an important field within bioanalytical science and NMR has become a key technique for drawing the full metabolic picture. However, the analysis of 1H NMR spectra of metabolomics samples is often very challenging, as resonances usually overlap in crowded regions, hindering the steps of metabolite profiling and resonance integration. In this context, a pre-processing method for the analysis of 1D 1H NMR data from metabolomics samples is proposed, consisting of the blind resolution and integration of all resonances of the spectral dataset by multivariate curve resolution-alternating least squares (MCR-ALS). The resulting concentration estimates can then be examined with traditional chemometric methods such as principal component analysis (PCA), ANOVA-simultaneous component analysis (ASCA), and partial least squares-discriminant analysis (PLS-DA). Since MCR-ALS does not require the use of spectral templates, the concentration estimates for all resonances are obtained even before being assigned. Consequently, the metabolomics study can be performed without neglecting any relevant resonance. In this work, the proposed pipeline performance was validated with 1D 1H NMR spectra from a metabolomics study of zebrafish upon acrylamide (ACR) exposure. Remarkably, this method represents a framework for the high-throughput analysis of NMR metabolomics data that opens the way for truly untargeted NMR metabolomics analyses. Graphical abstract.

Patterns of cyanobacterial abundance in a major drinking water reservoir: what 3 years of comprehensive monitoring data reveals?

The present study provides a detailed analysis of the factors influencing variation in cyanobacterial communities of a large shallow off-river drinking water reservoir on the east coast of Australia. Receiving multiple inflows from two unprotected mixed land-use catchments, the Grahamstown Reservoir is a model example of a reservoir which is highly vulnerable to adverse water quality issues, including phytoplankton blooms and the resulting filtration, toxin and taste and odour problems produced. The spatial and temporal distributions of cyanobacteria were assessed for a period of 3 years (January 2012-December 2014) based on samples collected from three monitoring stations within the reservoir. Relationships between cyanobacterial abundance and a range of environmental factors were evaluated by application of multivariate curve resolution-alternating least squares (MCR-ALS) analysis.Results of the analysis indicated that among the 22 physico-chemical variables and 14 cyanobacterial taxa measured, the vertical temperature gradient within the water column and nutrient availability were the most powerful explanatory factors for the observed temporal and spatial distribution patterns in the densities of cyanobacterial taxa. The abundance patterns of the dominant cyanobacterial taxa-Aphanocapsa, Aphanothece, Microcystis and Pseudanabaena-were strongly linked with rainfall and run-off patterns into the reservoir, while Coelosphaerium and Microcystis were the taxa most influenced by the apparent occurrence of thermal stratification. The findings demonstrate the capacity of rigorous multivariate data analysis to identify more subtle relationships between water quality variables, catchment factors and cyanobacterial growth in drinking water reservoirs.

ROIMCR: a powerful analysis strategy for LC-MS metabolomic datasets.

BACKGROUND: The analysis of LC-MS metabolomic datasets appears to be a challenging task in a wide range of disciplines since it demands the highly extensive processing of a vast amount of data. Different LC-MS data analysis packages have been developed in the last few years to facilitate this analysis. However, most of these strategies involve chromatographic alignment and peak shaping and often associate each "feature" (i.e., chromatographic peak) with a unique m/z measurement. Thus, the development of an alternative data analysis strategy that is applicable to most types of MS datasets and properly addresses these issues is still a challenge in the metabolomics field. RESULTS: Here, we present an alternative approach called ROIMCR to: i) filter and compress massive LC-MS datasets while transforming their original structure into a data matrix of features without losing relevant information through the search of regions of interest (ROIs) in the m/z domain and ii) resolve compressed data to identify their contributing pure components without previous alignment or peak shaping by applying a Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis. In this study, the basics of the ROIMCR method are presented in detail and a detailed description of its implementation is also provided. Data were analyzed using the MATLAB (The MathWorks, Inc., www.mathworks.com ) programming and computing environment. The application of the ROIMCR methodology is described in detail, with an example of LC-MS data generated in a lipidomic study and with other examples of recent applications. CONCLUSIONS: The methodology presented here combines the benefits of data filtering and compression based on the searching of ROI features, without the loss of spectral accuracy. The method has the benefits of the application of the powerful MCR-ALS data resolution method without the necessity of performing chromatographic peak alignment or modelling. The presented method is a powerful alternative to other existing data analysis approaches that do not use the MCR-ALS method to resolve LC-MS data. The ROIMCR method also represents an improved strategy compared to the direct applications of the MCR-ALS method that use less-powerful data compression strategies such as binning and windowing. Overall, the strategy presented here confirms the usefulness of the ROIMCR chemometrics method for analyzing LC-MS untargeted metabolomics data.

Stoichiometric gene-to-reaction associations enhance model-driven analysis performance: Metabolic response to chronic exposure to Aldrin in prostate cancer.

BACKGROUND: Genome-scale metabolic models (GSMM) integrating transcriptomics have been widely used to study cancer metabolism. This integration is achieved through logical rules that describe the association between genes, proteins, and reactions (GPRs). However, current gene-to-reaction formulation lacks the stoichiometry describing the transcript copies necessary to generate an active catalytic unit, which limits our understanding of how genes modulate metabolism. The present work introduces a new state-of-the-art GPR formulation that considers the stoichiometry of the transcripts (S-GPR). As case of concept, this novel gene-to-reaction formulation was applied to investigate the metabolic effects of the chronic exposure to Aldrin, an endocrine disruptor, on DU145 prostate cancer cells. To this aim we integrated the transcriptomic data from Aldrin-exposed and non-exposed DU145 cells through S-GPR or GPR into a human GSMM by applying different constraint-based-methods. RESULTS: Our study revealed a significant improvement of metabolite consumption/production predictions when S-GPRs are implemented. Furthermore, our computational analysis unveiled important alterations in carnitine shuttle and prostaglandine biosynthesis in Aldrin-exposed DU145 cells that is supported by bibliographic evidences of enhanced malignant phenotype. CONCLUSIONS: The method developed in this work enables a more accurate integration of gene expression data into model-driven methods. Thus, the presented approach is conceptually new and paves the way for more in-depth studies of aberrant cancer metabolism and other diseases with strong metabolic component with important environmental and clinical implications.

Chemometric Strategies for Peak Detection and Profiling from Multidimensional Chromatography.

The increasing complexity of omics research has encouraged the development of new instrumental technologies able to deal with these challenging samples. In this way, the rise of multidimensional separations should be highlighted due to the massive amounts of information that provide with an enhanced analyte determination. Both proteomics and metabolomics benefit from this higher separation capacity achieved when different chromatographic dimensions are combined, either in LC or GC. However, this vast quantity of experimental information requires the application of chemometric data analysis strategies to retrieve this hidden knowledge, especially in the case of nontargeted studies. In this work, the most common chemometric tools and approaches for the analysis of this multidimensional chromatographic data are reviewed. First, different options for data preprocessing and enhancement of the instrumental signal are introduced. Next, the most used chemometric methods for the detection of chromatographic peaks and the resolution of chromatographic and spectral contributions (profiling) are presented. The description of these data analysis approaches is complemented with enlightening examples from omics fields that demonstrate the exceptional potential of the combination of multidimensional separation techniques and chemometric tools of data analysis.

Deciphering the Underlying Metabolomic and Lipidomic Patterns Linked to Thermal Acclimation in Saccharomyces cerevisiae.

Temperature is one of the most critical parameters for yeast growth, and it has deep consequences in many industrial processes where yeast is involved. Nevertheless, the metabolic changes required to accommodate yeast cells at high or low temperatures are still poorly understood. In this work, the ultimate responses of these induced transcriptomic effects have been examined using metabolomics-derived strategies. The yeast metabolome and lipidome have been characterized by 1D proton nuclear magnetic resonance spectroscopy and ultra-high-performance liquid chromatography-mass spectrometry at four temperatures, corresponding to low, optimal, high, and extreme thermal conditions. The underlying pathways that drive the acclimation response of yeast to these nonoptimal temperatures were evaluated using multivariate curve resolution-alternating least-squares. The analysis revealed three different thermal profiles (cold, optimal, and high temperature), which include changes in the lipid composition, secondary metabolic pathways, and energy metabolism, and we propose that they reflect the acclimation strategy of yeast cells to low and high temperatures. The data suggest that yeast adjusts membrane fluidity by changing the relative proportions of the different lipid families (acylglycerides, phospholipids, and ceramides, among others) rather than modifying the average length and unsaturation levels of the corresponding fatty acids.

Differential gene transcription across the life cycle in Daphnia magna using a new all genome custom-made microarray.

BACKGROUND: Unravelling the link between genes and environment across the life cycle is a challenging goal that requires model organisms with well-characterized life-cycles, ecological interactions in nature, tractability in the laboratory, and available genomic tools. Very few well-studied invertebrate model species meet these requirements, being the waterflea Daphnia magna one of them. Here we report a full genome transcription profiling of D. magna during its life-cycle. The study was performed using a new microarray platform designed from the complete set of gene models representing the whole transcribed genome of D. magna. RESULTS: Up to 93% of the existing 41,317 D. magna gene models showed differential transcription patterns across the developmental stages of D. magna, 59% of which were functionally annotated. Embryos showed the highest number of unique transcribed genes, mainly related to DNA, RNA, and ribosome biogenesis, likely related to cellular proliferation and morphogenesis of the several body organs. Adult females showed an enrichment of transcripts for genes involved in reproductive processes. These female-specific transcripts were essentially absent in males, whose transcriptome was enriched in specific genes of male sexual differentiation genes, like doublesex. CONCLUSION: Our results define major characteristics of transcriptional programs involved in the life-cycle, differentiate males and females, and show that large scale gene-transcription data collected in whole animals can be used to identify genes involved in specific biological and biochemical processes.

Comparative analysis of 1H NMR and 1H-13C HSQC NMR metabolomics to understand the effects of medium composition in yeast growth.

In nuclear magnetic resonance (NMR) metabolomics, most of the studies have been focused on the analysis of one-dimensional proton (1D 1H) NMR, whereas the analysis of other nuclei, such as 13C, or other NMR experiments are still underrepresented. The preference of 1D 1H NMR metabolomics lies on the fact that it has good sensitivity and a short acquisition time, but it lacks spectral resolution because it presents a high degree of overlap. In this study, the growth metabolism of yeast ( Saccharomyces cerevisiae) was analyzed by 1D 1H NMR and by two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) NMR spectroscopy, leading to the detection of more than 50 metabolites with both analytical approaches. These two analyses allow for a better understanding of the strengths and intrinsic limitations of the two types of NMR approaches. The two data sets (1D and 2D NMR) were investigated with PCA, ASCA, and PLS DA chemometric methods, and similar results were obtained regardless of the data type used. However, data-analysis time for the 2D NMR data set was substantially reduced when compared with the data analysis of the corresponding 1H NMR data set because, for the 2D NMR data, signal overlap was not a major problem and deconvolution was not required. The comparative study described in this work can be useful for the future design of metabolomics workflows, to assist in the selection of the most convenient NMR platform and to guide the posterior data analysis of biomarker selection.

Quantifying the Prediction Error in Analytical Multivariate Curve Resolution Studies of Multicomponent Systems.

In multivariate curve resolution (MCR) analysis, a range of feasible solutions is often encountered, because of the rotational ambiguities associated with the bilinear decomposition of data matrices. For quantitative purposes, the analysis is usually applied to a carefully designed set of calibration and test samples having uncalibrated interferents. Under the usual minimal constraints (non-negativity, unimodality, species correspondence, etc.), concentration and spectral profiles of the analyte in the test samples are not univocally recovered, unlike those in the calibration samples, especially when profile overlapping with the interferents is significant and selective regions do not exist for the analyte. In this report, a quantitative measure of the prediction errors due to rotational ambiguities is discussed, based on the calculation of the differences between the maximum and minimum area under the analyte concentration profiles calculated by the MCR-BANDS procedure. This methodology can be applied in different analytical scenarios with any number of analytes and interferents. Both absolute and relative quantitative errors due to rotation ambiguities are estimated and discussed in both simulated and experimental examples derived from liquid chromatography with diode array detection. The proposed procedure can be generalized to most of the analytical situations where every instrumentally measured sample produces a data table or data matrix.

Handling Different Spatial Resolutions in Image Fusion by Multivariate Curve Resolution-Alternating Least Squares for Incomplete Image Multisets.

Data fusion of different imaging techniques allows a comprehensive description of chemical and biological systems. Yet, joining images acquired with different spectroscopic platforms is complex because of the different sample orientation and image spatial resolution. Whereas matching sample orientation is often solved by performing suitable affine transformations of rotation, translation, and scaling among images, the main difficulty in image fusion is preserving the spatial detail of the highest spatial resolution image during multitechnique image analysis. In this work, a special variant of the unmixing algorithm Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) for incomplete multisets is proposed to provide a solution for this kind of problem. This algorithm allows analyzing simultaneously images collected with different spectroscopic platforms without losing spatial resolution and ensuring spatial coherence among the images treated. The incomplete multiset structure concatenates images of the two platforms at the lowest spatial resolution with the image acquired with the highest spatial resolution. As a result, the constituents of the sample analyzed are defined by a single set of distribution maps, common to all platforms used and with the highest spatial resolution, and their related extended spectral signatures, covering the signals provided by each of the fused techniques. We demonstrate the potential of the new variant of MCR-ALS for multitechnique analysis on three case studies: (i) a model example of MIR and Raman images of pharmaceutical mixture, (ii) FT-IR and Raman images of palatine tonsil tissue, and (iii) mass spectrometry and Raman images of bean tissue.

Comprehensive characterization of neurochemicals in three zebrafish chemical models of human acute organophosphorus poisoning using liquid chromatography-tandem mass spectrometry.

There is a growing interest in biological models to investigate the effect of neurotransmitter dysregulation on the structure and function of the central nervous system (CNS) at different stages of development. Zebrafish, a vertebrate model increasingly used in neurobiology and neurotoxicology, shares the common neurotransmitter systems with mammals, including glutamate, GABA, glycine, dopamine, norepinephrine, epinephrine, serotonin, acetylcholine, and histamine. In this study, we have evaluated the performance of liquid chromatography-tandem mass spectrometry (LC-MS/MS) for the multiresidue determination of neurotransmitters and related metabolites. In a first step, ionization conditions were tested in positive electrospray mode and optimum fragmentation patterns were determined to optimize two selected reaction monitoring (SRM) transitions. Chromatographic conditions were optimized considering the chemical structure and chromatographic behavior of the analyzed compounds. The best performance was obtained with a Synergy Polar-RP column, which allowed the separation of the 38 compounds in 30 min. In addition, the performance of LC-MS/MS was studied in terms of linearity, sensitivity, intra- and inter-day precision, and overall robustness. The developed analytical method was able to quantify 27 of these neurochemicals in zebrafish chemical models for mild (P1), moderate (P2), and severe (P3) acute organophosphorus poisoning (OPP). The results show a general depression of synaptic-related neurochemicals, including the excitatory and inhibitory amino acids, as well as altered phospholipid metabolism, with specific neurochemical profiles associated to the different grades of severity. These results confirmed that the developed analytical method is a new tool for neurotoxicology research using the zebrafish model.

Chemometrics in analytical chemistry-part II: modeling, validation, and applications.

The contribution of chemometrics to important stages throughout the entire analytical process such as experimental design, sampling, and explorative data analysis, including data pretreatment and fusion, was described in the first part of the tutorial "Chemometrics in analytical chemistry." This is the second part of a tutorial article on chemometrics which is devoted to the supervised modeling of multivariate chemical data, i.e., to the building of calibration and discrimination models, their quantitative validation, and their successful applications in different scientific fields. This tutorial provides an overview of the popularity of chemometrics in analytical chemistry.

Chemometrics comparison of gas chromatography with mass spectrometry and comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry Daphnia magna metabolic profiles exposed to salinity.

The performances of gas chromatography with mass spectrometry and of comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry are examined through the comparison of Daphnia magna metabolic profiles. Gas chromatography with mass spectrometry and comprehensive two-dimensional gas chromatography with mass spectrometry were used to compare the concentration changes of metabolites under saline conditions. In this regard, a chemometric strategy based on wavelet compression and multivariate curve resolution-alternating least squares is used to compare the performances of gas chromatography with mass spectrometry and comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry for the untargeted metabolic profiling of Daphnia magna in control and salinity-exposed samples. Examination of the results confirmed the outperformance of comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry over gas chromatography with mass spectrometry for the detection of metabolites in D. magna samples. The peak areas of multivariate curve resolution-alternating least squares resolved elution profiles in every sample analyzed by comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry were arranged in a new data matrix that was then modeled by partial least squares discriminant analysis. The control and salt-exposed daphnids samples were discriminated and the most relevant metabolites were estimated using variable importance in projection and selectivity ratio values. Salinity de-regulated 18 metabolites from metabolic pathways involved in protein translation, transmembrane cell transport, carbon metabolism, secondary metabolism, glycolysis, and osmoregulation.