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Time-resolved fluorescence spectroscopy (TRFS), i.e., measurement of fluorescence decay curves for different excitation and/or emission wavelengths, provides specific and sensitive local information on molecules and on their environment. However, TRFS relies on multiexponential data fitting to derive fluorescence lifetimes from the measured decay curves and the time resolution of the technique is limited by the instrumental response function (IRF). We propose here a multivariate curve resolution (MCR) approach based on data slicing to perform tailored and fit-free analysis of multiexponential fluorescence decay curves. MCR slicing, taking as a basic framework the multivariate curve resolution-alternating least-squares (MCR-ALS) soft-modeling algorithm, relies on a hybrid bilinear/trilinear data decomposition. A key feature of the method is that it enables the recovery of individual components characterized by decay profiles that are only partially describable by monoexponential functions. For TRFS data, not only pure multiexponential tail information but also shorter time delay information can be decomposed, where the signal deviates from the ideal exponential behavior due to the limited time resolution. The accuracy of the proposed approach is validated by analyzing mixtures of three commercial dyes and characterizing the mixture composition, lifetimes, and associated contributions, even in situations where only ternary mixture samples are available. MCR slicing is also applied to the analysis of TRFS data obtained on a photoswitchable fluorescent protein (rsEGFP2). Three fluorescence lifetimes are extracted, along with the profile of the IRF, highlighting that decomposition of complex systems, for which individual isomers are characterized by different exponential decays, can also be achieved.

Image fusion is often oriented to solve differences in spatial scale and orientation among different spectroscopic platforms. However, an additional problem arises when the nature of the spectroscopic information differs in dimensionality as well. Indeed, most imaging systems, e.g., Raman, IR, MS, etc., allow acquisition of 3D images, with a linear spectrum per pixel, but new platforms have emerged, such as the recent excitation-emission fluorescence imaging platforms that provide 4D images, with a 2D spectral landscape per pixel. A proper 3D/4D image fusion needs to take into account the difference in the dimension of the spectral information and in the underlying models of both measurements (bilinear for 3D images and trilinear for 4D images). This work solves this image fusion problem through a new dedicated variant of the multivariate curve resolution-alternating least squares (MCR-ALS) algorithm for multiset analysis based on the incorporation of a hybrid bilinear/trilinear model that can handle the image fused structure preserving the natural behavior of the 3D and 4D imaging techniques coupled. The example is illustrated on the fusion of real 3D Raman and 4D fluorescence images recorded on cross sections of rice leaf samples.

International audience; Laser-induced breakdown spectroscopy (LIBS) imaging is an innovative technique that associates the valuable atomic, ionic and molecular emission signals of the parent spectroscopy with spatial information. LIBS works using a powerful pulse laser as excitation source, to generate a plasma exhibiting emission lines of atoms, ions and molecules present in the ablated matter. The advantages of LIBS imaging are potential high sensitivity (in the order of ppm), easy sample preparation, fast acquisition rate (up to 1 kHz) and μm scale spatial resolution (weight of the ablated material in the order of ng). Despite these positive aspects, LIBS imaging easily provides datasets consisting of several million spectra, each containing several thousand spectral channels. Under these conditions, the current chemometric analyses of the raw data are still possible, but require too high computing resources. Therefore, the aim of this work is to propose a data compression strategy oriented to keep the most relevant spectral channel and pixel information to facilitate, fast and reliable signal unmixing for an exhaustive exploration of complex samples. This strategy will apply not only to the context of LIBS image analysis, but to the fusion of LIBS with other imaging technologies, a scenario where the data compression step becomes even more mandatory. The data fusion strategy will be applied to the analysis of a heterogeneous kyanite mineral sample containing several trace elements by LIBS imaging associated with plasma induced luminescence (PIL) imaging, these two signals being acquired simultaneously by the same microscope. The association of compression and spectral data fusion will allow extracting the compounds in the mineral sample associated with a fused LIBS/PIL fingerprint. This LIBS/PIL association will be essential to interpret the PIL spectral information, which is nowadays very complex due to the natural overlapped signals provided by this technique.

Recent advances in the latest generation of MEMS (micro-electro-mechanical system) Fabry-Pérot interferometers (FPI) for near infrared (NIR) wavelengths has led to the development of ultra-fast and low cost NIR sensors with potential to be used by the process industry. One of these miniaturised sensors operating from 1350 to 1650 nm, was integrated into a software platform to monitor a multiphase solid-gas-liquid process, for the production of saturated polyester resins. Twelve batches were run in a 2 L reactor mimicking industrial conditions (24 h process, with temperatures ranging from 220 to 240 °C), using an immersion NIR transmission probe. Because of the multiphase nature of the reaction, strong interference produced by process disturbances such as temperature variations and the presence of solid particles and bubbles in the online spectra required robust pre-processing algorithms and a good long-term stability of the probe. These allowed partial least squares (PLS) regression models to be built for the key analytical parameters acid number and viscosity. In parallel, spectra were also used to build an end-point detection model based on principal component analysis (PCA) for multivariate statistical process control (MSPC). The novel MEMS-FPI sensor combined with robust chemometric analysis proved to be a suitable and affordable alternative for online process monitoring, contributing to sustainability in the process industry.

Multivariate Curve Resolution (MCR) covers a wide span of algorithms designed to tackle the mixture analysis problem by expressing the original data through a bilinear model of pure component meaningful contributions. Since the seminal work by Lawton and Sylvestre in 1971, MCR methods are dynamically evolving to adapt to a wealth of diverse and demanding scientific scenarios. To do so, essential concepts, such as basic constraints, have been revisited and new modeling tasks, mathematical properties and domain-specific information have been incorporated; the initial underlying bilinear model has evolved into a flexible framework where hybrid bilinear/multilinear models can coexist, the regular data structures have undergone a turn of the screw and incomplete multisets and matrix and tensor combinations can be now analyzed. Back to the fundamentals, the theoretical core of the MCR methodology is deeply understood due to the thorough studies about the ambiguity phenomenon. The adaptation of the method to new analytical measurements and scientific domains is continuous. At this point of the story, MCR can be considered a mature yet lively methodology, where many steps forward can still be taken., Authors acknowledge the Ministerio de Ciencia e Innovación (MINECO) from the Spanish government for financial support through the projects PID 2019-1071586B-IOO and PID 2019-105732 GB-C21.

Data modeling is a wide concept that exists since long and encompasses all possible ways to interpret the information associated with a process, analytical measurement or set of related parameters that presents a systematic variation. Data modeling can follow the path of knowledge and be based on first principles or can focus on measurements and empirical models. These different approaches are known as hard- and soft-modeling, respectively. It seemed to us very appropriate to dedicate this article to Paul Gemperline, a person who has significantly contributed to the worlds of hard- and soft-modeling, presumably acknowledging the value of looking at data from all possible perspectives. The following pages do not intend to be an extensive review, but a personal perspective on values, milestones and progress of hard- and soft-modeling and on the necessary existence and valuable combination of both ways to interpret chemical information.

Purpose The current trend for continuous drug product manufacturing requires new, affordable process analytical techniques (PAT) to ensure control of processing. This work evaluates whether property models based on spectral data from recent Fabry–Pérot Interferometer based NIR sensors can generate a high-resolution moisture signal suitable for process control. Methods Spectral data and offline moisture content were recorded for 14 fluid bed dryer batches of pharmaceutical granules. A PLS moisture model was constructed resulting in a high resolution moisture signal, used to demonstrate (i) endpoint determination and (ii) evaluation of mass transfer performance. Results The sensors appear robust with respect to vibration and ambient temperature changes, and the accuracy of water content predictions (±13 % ) is similar to those reported for high specification NIR sensors. Fusion of temperature and moisture content signal allowed monitoring of water transport rates in the fluidised bed and highlighted the importance water transport within the solid phase at low moisture levels. The NIR data was also successfully used with PCA-based MSPC models for endpoint detection. Conclusions The spectral quality of the small form factor NIR sensor and its robustness is clearly sufficient for the construction and application of PLS models as well as PCA-based MSPC moisture models. The resulting high resolution moisture content signal was successfully used for endpoint detection and monitoring the mass transfer rate.

Process analytical technologies (PAT) applied to process monitoring and control generally provide multiple outputs that can come from different sensors or from different model outputs generated from a single multivariate sensor. This paper provides a contribution to current data fusion strategies for the combination of sensor and/or model outputs in the development of multivariate statistical process control (MSPC) models. Data fusion is explored through three real process examples combining output from multivariate models coming from the same sensor uniquely (in the near-infrared (NIR)-based end point detection of a two-stage polyester production process) or the combination of these outputs with other process variable sensors (using NIR-based model outputs and temperature values in the end point detection of a fluidized bed drying process and in the on-line control of a distillation process). The three examples studied show clearly the flexibility in the choice of model outputs (e.g. key properties prediction by multivariate calibration, process profiles issued from a multivariate resolution method) and the benefit of using MSPC models based on fused information including model outputs towards those based on raw single sensor outputs for both process control and diagnostic and interpretation of abnormal process situations. The data fusion strategy proposed is of general applicability for any analytical or bioanalytical process that produces several sensor and/or model outputs. Graphical abstract.

The study of pollutant effects on living organisms provides information about the possible biological and environmental response to a contaminant. Progression of prostate cancer may be related to exposure to pesticides or other chemical substances. In this work, the effect of the pesticide aldrin on human prostate cancer cells (DU145) is studied using Raman spectroscopy and chemometric techniques. Prostate cancer cell line DU145 has been exposed acutely the pesticide aldrin. Individual Raman spectra coming from control and treated cell populations have been acquired. Partial least squares discriminant analysis (PLSDA) has been used to assess differences among treated and control samples and to identify spectral biomarkers associated with pollutant stress. Some preprocessing methodologies have been tested in order to improve the capability of discrimination between fingerprints. Partial least squares discriminant analysis results suggest that the best normalization–scaling preprocessing combination is provided by Euclidean normalization (EN)-SIMPLISMA-based scaling (SBS). SIMPLISMA-based scaling has been proposed as a scaling method focused on the classification objective, which enhances variables with high relative variation among samples. The most relevant spectral variables related to aldrin effect on DU145 seem to be mainly related to lipids, proteins, and variations in nucleic acids. © 2017, © The Author(s) 2017., The authors acknowledge the financial support from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement no. 32073. They also belong to the network of recognized research groups by the Catalan government (2014 SGR 1106).

The use of hyperspectral imaging techniques in biological studies has increased in the recent years. Hyperspectral images (HSI) provide chemical information and preserve the morphology and original structure of heterogeneous biological samples, which can be potentially useful in environmental –omics studies when effects due to several factors, e.g., contaminant exposure, phenotype,… at a specific tissue level need to be investigated. Yet, no available strategies exist to exploit adequately this kind of information. This work offers a novel chemometric strategy to pass from the raw image information to useful knowledge in terms of statistical assessment of the multifactor effects of interest in –omic studies. To do so, unmixing of the hyperspectral image measurement is carried out to provide tissue-specific information. Afterwards, several specific ANOVA-Simultaneous Component Analysis (ASCA) models are generated to properly assess and interpret the diverse effect of the factors of interest on the spectral fingerprints of the different tissues characterized. The unmixing step is performed by Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) on multisets of biological images related to each studied condition and provides reliable HSI spectral signatures and related image maps for each specific tissue in the regions imaged. The variability associated with these signatures within a population is obtained through an MCR-based resampling step on representative pixel subsets of the images analyzed. All spectral fingerprints obtained for a particular tissue in the different conditions studied are used to obtain the related ASCA model that will help to assess the significance of the factors studied on the tissue and, if relevant, to describe the associated fingerprint modifications. The potential of the approach is assessed in a real case of study linked to the investigation of the effect of exposure time to chlorpyrifos-oxon (CPO) on ocular tissues of different phenotypes of zebrafish larvae from Raman HSI of eye cryosections. The study allowed the characterization of melanin, crystalline and internal eye tissue and the phenotype, exposure time and the interaction of the two factors were found to be significant in the changes found in all kind of tissues. Factor-related changes in the spectral fingerprint were described and interpreted per each kind of tissue characterized. © 2018 Elsevier B.V., The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme ( FP/2007-2013 ) / ERC Grant Agreement n. 32073 (CHEMAGEB project). The authors of this work belong to the network of recognized research groups by the Catalan government (2017 SGR 753 / 2017 SGR 902) and acknowledge the support of the Spanish government through project CTQ2015-66254-C2-2-P , and from NATO (SfP project MD.SFPP 984777 ). ICFO acknowledges financial support from the Spanish Ministry of Economy and Competitiveness through the “Severo Ochoa” program for Centres of Excellence in R&D ( SEV-2015-0522 ), from Fundación Cellex , Fundación Mig-Puig and from Generalitat de Catalunya through the CERCA program", Laserlab-Europe ( EU-H2020 654148 ), Fundació la Marató de TV3 (Molecular imaging of the retina in patients with Multiple Sclerosis by Raman Spectroscopy, 20142030 ), and the National Institutes of Health (NIH, grant 5R21CA187890-02 ). Appendix A

This article describes a non-destructive analytical method developed to solve forensic document examination problems involving crossed lines and obliteration. Different strategies combining confocal Raman imaging and multivariate curve resolution-alternating least squares (MCR-ALS) are presented. Multilayer images were acquired at subsequent depth layers into the samples. It is the first time that MCR-ALS is applied to multilayer images for forensic purposes. In this context, this method provides a single set of pure spectral ink signatures and related distribution maps for all layers examined from the sole information in the raw measurement. Four cases were investigated, namely, two concerning crossed lines with different degrees of ink similarity and two related to obliteration, where previous or no knowledge about the identity of the obliterated ink was available. In the crossing line scenario, MCR-ALS analysis revealed the ink nature and the chronological order in which strokes were drawn. For obliteration cases, results making active use of information about the identity of the obliterated ink in the chemometric analysis were of similar quality as those where the identity of the obliterated ink was unknown. In all obliteration scenarios, the identity of inks and the obliterated text were satisfactorily recovered. The analytical methodology proposed is of general use for analytical forensic document examination problems, and considers different degrees of complexity and prior available information. Besides, the strategies of data analysis proposed can be applicable to any other kind of problem in which multilayer Raman images from multicomponent systems have to be interpreted.

The effect of physicochemical factors and use of skim milk powder on milk rennet-coagulation was investigated combining near infrared (NIR) spectroscopic monitoring and Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS). Coagulum formation has been studied by reference approaches (Formagraph and fundamental rheology) and with NIR spectroscopy on unaltered reconstituted milk samples, pasteurized samples, samples with calcium chloride addition and samples of reconstituted milk mixed with fresh milk. The MCR-ALS models successfully described the process evolution, explaining more than 99.9% of variance. The MCR-ALS profiles revealed to be significantly directly correlated with Formagraph and rheological data (p 0.05) of the CaCl2 concentration level added and the heat treatment applied. The MCR-ALS models calculated for the coagulation trials of pasteurized skimmed milk mixed with reconstituted milk samples highlighted shorter coagulation times with the increasing of reconstituted milk amount (from 4.3-6.6 min to 2–5 min). Profiles extracted from MCR-ALS models developed for a wide range of coagulation conditions proved to be suitable non-destructive, non-invasive and on-line tools to evaluate the rennet-induced coagulation of reconstituted milks.

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. © 2018 American Chemical Society., A.d.J. and R.T. acknowledge financial support from the European Research Council under the European Union’s Seventh Framework Program (FP/2007-2013)/ERC Grant Agreement n. 32073 (CHEMAGEB project). They also belong to the network of recognized research groups by the Catalan government (2014 SGR 1106). S.P. acknowledges financial support from the Spanish government project CTQ2015-66254-C2-2-P.

Background: Plant cell walls are nanocomposites based on cellulose microfibrils embedded in a matrix of polysaccharides and aromatic polymers. They are optimized for different functions (e.g. mechanical stability) by changing cell form, cell wall thickness and composition. To reveal the composition of plant tissues in a non-destructive way on the microscale, Raman imaging has become an important tool. Thousands of Raman spectra are acquired, each one being a spatially resolved molecular fingerprint of the plant cell wall. Nevertheless, due to the multicomponent nature of plant cell walls, many bands are overlapping and classical band integration approaches often not suitable for imaging. Multivariate data analysing approaches have a high potential as the whole wavenumber region of all thousands of spectra is analysed at once. Results: Three multivariate unmixing algorithms, vertex component analysis, non-negative matrix factorization and multivariate curve resolution–alternating least squares were applied to find the purest components within datasets acquired from micro-sections of spruce wood and Arabidopsis. With all three approaches different cell wall layers (including tiny S1 and S3 with 0.09–0.14 µm thickness) and cell contents were distinguished and endmember spectra with a good signal to noise ratio extracted. Baseline correction influences the results obtained in all methods as well as the way in which algorithm extracts components, i.e. prioritizing the extraction of positive endmembers by sequential orthogonal projections in VCA or performing a simultaneous extraction of non-negative components aiming at explaining the maximum variance in NMF and MCR-ALS. Other constraints applied (e.g. closure in VCA) or a previous principal component analysis filtering step in MCR-ALS also contribute to the differences obtained. Conclusions: VCA is recommended as a good preliminary approach, since it is fast, does not require setting many input parameters and the endmember spectra result in good approximations of the raw data. Yet the endmember spectra are more correlated and mixed than those retrieved by NMF and MCR-ALS methods. The latter two give the best model statistics (with lower lack of fit in the models), but care has to be taken about overestimating the rank as it can lead to artificial shapes due to peak splitting or inverted bands., Plant Methods, 14 (1), ISSN:1746-4811

The use of multivariate unmixing/resolution methods for the analysis of biological vibrational hyperspectral images is crucial to characterize the morphology and spectral signatures of the different biological tissues or cell compartments. This work provides a general data analysis protocol to interpret Raman and FT-IR hyperspectral images of biological samples. To do so, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) is the core tool proposed. The protocol starts by describing dedicated preprocessing steps suitable to handle the nature and artifacts of the spectroscopic technique used. Later on, the focus is on the use of MCR-ALS to analyze single images or image multiset structures that contain images with related information. Relevant issues related to advanced use of MCR-ALS on biological image analysis, such as the modeling of mixed non-biological signal contributions or the elucidation and active use of information related to the presence/absence of biological contributions in different images (samples) in multiset structures, are described. Additional aspects, such as the only use of the FT-IR fingerprint region vs. using extended spectral ranges in FT-IR to improve the differentiation among contributions, are also considered. The proposed data analysis methodology is demonstrated on real Raman and FT-IR images from zebrafish tissue cryosections. © 2017 Elsevier B.V., ICFO would like to acknowledge financial support from Laserlab-Europe (EU-H2020 654148), the Spanish MINECO (Severo Ochoa grant SEV-2015-0522), Marat? de TV3 (20142030), and the National Institute of Health (NIH, grant 5R21CA187890-02). The research conducted at ICFO's Super Resolution Light Microscopy and Nanoscopy Facility has been partially supported by Fundaci? Cellex Barcelona.

Hyperspectral images collected with different spectroscopic techniques can be combined to benefit from complementary information and to improve the general description of chemical systems. The simultaneous analysis of images collected by different spectroscopic platforms can only be carried out when images are spatially matched with each other (i.e., different pixel sizes should be balanced and translation/rotation/scaling transformations should be done if required). The main goal of this work is the proposal of a general methodology to match image spatial properties that uses all pixels acquired in the images and, therefore, avoids the step of selecting analogous reference pixels to be compared. The effect of working with different kinds of image starting information on the robustness of the retrieved optimal translation and rotation parameters has also been assessed. The study has been tested in two different representative situations, namely: a) imaged sample with a clear contour b) imaged sample without defined contour. A final study on the effect of proper image matching is performed by applying Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) to the multiset formed by the appended images from different spectroscopic platforms before and after matching. © 2017 Elsevier B.V., A. de Juan and R. Tauler acknowledge financial support from the European Research Council under the European Union's Seventh Framework Programme (FP/2007?2013) / ERC Grant Agreement no. 32073 (CHEMAGEB project). They also belong to the network of recognized research groups by the Catalan government (2014 SGR 1106). S. Piqueras acknowledge financial support from the Spanish government project CTQ2015-66254-C2-2-P.

This work aims at screening the presence and predicting the quantity of the binding media used to produce red and blue medieval paints based on the combined use of μ-Fourier-transform infrared (μ-FTIR) spectroscopy and multivariate curve resolution alternating least squares (MCR-ALS). Using different modes of application, MCR-ALS is proposed with a dual objective: 1) classical MCR-ALS is used for a first qualitative analysis of the presence/absence of certain binders in historical samples; 2) correlation constrained MCR-ALS is applied for quantitative analysis of binding media used to produce red and blue medieval paints and to provide structural spectral information about all compounds in the sample. Correlation constrained MCR-ALS introduces a constraint to establish internal calibration models for quantitative analysis in the resolution algorithm. This approach has been first applied to synthetic binder mixtures in order to check for the possibility of designing an appropriate calibration model to validate the quantitative analysis of the three binders that were potentially used in the medieval paints under study: parchment glue, egg white and egg yolk. Finally, classical and correlation constrained MCR-ALS were applied in a set of historical medieval manuscripts (illuminations) of three Portuguese Monasteries to provide qualitative and quantitative information about their binding media. Parchment glue and egg white have been often found separately or in mixtures of different proportions, whereas the egg yolk binder seems to be generally absent in all samples (or in very minor amounts in a few of them). Correlation constrained MCR-ALS resolution has provided reliable results with less rotational ambiguity than classical MCR-ALS with softer constraints. Quantitative values in historical samples should be interpreted only as current values, since there is no available knowledge about the effects of aging processes on the binders of these paints.

A distillation device that acquires continuous and synchronized measurements of temperature, percentage of distilled fraction and NIR spectra has been designed for real-time monitoring of distillation processes. As a process model, synthetic commercial gasoline batches produced in Brazil, which contain mixtures of pure gasoline blended with ethanol have been analyzed. The information provided by this device, i.e., distillation curves and NIR spectra, has served as initial information for the proposal of new strategies of process modeling and multivariate statistical process control (MSPC). Process modeling based on PCA batch analysis provided global distillation trajectories, whereas multiset MCR-ALS analysis is proposed to obtain a component-wise characterization of the distillation evolution and distilled fractions. Distillation curves, NIR spectra or compressed NIR information under the form of PCA scores and MCR-ALS concentration profiles were tested as the seed information to build MSPC models. New on-line PCA-based MSPC approaches, some inspired on local rank exploratory methods for process analysis, are proposed and work as follows: a) MSPC based on individual process observation models, where multiple local PCA models are built considering the sole information in each observation point; b) Fixed Size Moving Window - MSPC, in which local PCA models are built considering a moving window of the current and few past observation points; and c) Evolving MSPC, where local PCA models are built with an increasing window of observations covering all points since the beginning of the process until the current observation. Performance of different approaches has been assessed in terms of sensitivity to fault detection and number of false alarms. The outcome of this work will be of general use to define strategies for on-line process monitoring and control and, in a more specific way, to improve quality control of petroleum derived fuels and other substances submitted to automatic distillation processes monitored by NIRS.

The use of sparseness in chemometrics is a concept that has increased in popularity. The advantage is, above all, a better interpretability of the results obtained. In this work, sparseness is implemented as a constraint in multivariate curve resolution - alternating least squares (MCR-ALS), which aims at reproducing raw (mixed) data by a bilinear model of chemically meaningful profiles. In many cases, the mixed raw data analyzed are not sparse by nature, but their decomposition profiles can be, as it is the case in some instrumental responses, such as mass spectra, or in concentration profiles linked to scattered distribution maps of powdered samples in hyperspectral images. To induce sparseness in the constrained profiles, one-dimensional and/or two-dimensional numerical arrays can be fitted using a basis of Gaussian functions with a penalty on the coefficients. In this work, a least squares regression framework with L0-norm penalty is applied. This L0-norm penalty constrains the number of non-null coefficients in the fit of the array constrained without having an a priori on the number and their positions. It has been shown that the sparseness constraint induces the suppression of values linked to uninformative channels and noise in MS spectra and improves the location of scattered compounds in distribution maps, resulting in a better interpretability of the constrained profiles. An additional benefit of the sparseness constraint is a lower ambiguity in the bilinear model, since the major presence of null coefficients in the constrained profiles also helps to limit the solutions for the profiles in the counterpart matrix of the MCR bilinear model. © 2017 Elsevier B.V., The authors thank Paul Eilers for his involvement in the development of the algorithms and appreciate the fruitful discussions on the topic. C. R. thanks the Agence National de la Recherche ( ANR-15-CE09-0020-01 Blink). C.B. and A.J have received funding from the European Research Council under the European Union's Seventh Framework Program ( FP/2007-2013 )/ ERC Grant Agreement n. 320737 (CHEMAGEB project). S.P. acknowledges the support of the Spanish government through project CTQ2015-66254-C2-2-P .

Changes on an organism by the exposure to environmental stressors may be characterized by hyperspectral images (HSI), which preserve the morphology of biological samples, and suitable chemometric tools. The approach proposed allows assessing and interpreting the effect of contaminant exposure on heterogeneous biological samples monitored by HSI at specific tissue levels. In this work, the model example used consists of the study of the effect of the exposure of chlorpyrifos-oxon on zebrafish tissues. To assess this effect, unmixing of the biological sample images followed by tissue-specific classification models based on the unmixed spectral signatures is proposed. Unmixing and classification are performed by multivariate curve resolution-alternating least squares (MCR-ALS) and partial least squares-discriminant analysis (PLS-DA), respectively. Crucial aspects of the approach are: (1) the simultaneous MCR-ALS analysis of all images from 1 population to take into account biological variability and provide reliable tissue spectral signatures, and (2) the use of resolved spectral signatures from control and exposed populations obtained from resampling of pixel subsets analyzed by MCR-ALS multiset analysis as information for the tissue-specific PLS-DA classification models. Classification results diagnose the presence of a significant effect and identify the spectral regions at a tissue level responsible for the biological change., The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 32073 (CHEMAGEB project). The authors of this work belong to the network of recognized research groups by the Catalan government (2014 SGR 1106) and acknowledge the support of the Spanish government through project CTQ2015-66254-C2-2-P. ICFO would like to acknowledge financial support from Laserlab-Europe (EU-H2020 654148), the Spanish MINECO (Severo Ochoa grant SEV-2015-0522), Marató de TV3 (20142030), and the National Institute of Health (NIH, grant 5R21CA187890-02). The research conducted at ICFO’s Super Resolution Light Microscopy and Nanoscopy Facility has been partially supported by Fundació Cellex Barcelona.

This work is focused on the study of the photodegradation of 2-bromophenol under the action of UV light and sunlight. The photodegradation process has been monitored using UV–Vis spectroscopy and High Performance Liquid Chromatography coupled to diode array and mass spectrometry detectors in tandem (HPLC–DAD–MS). Multivariate resolution methods, such as Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and hybrid soft- and hard-modeling-Multivariate Curve Resolution (HS-MCR), have been applied to the experimental data to obtain the information about the kinetic evolution and identification of the compounds involved in the photodegradation process. From the analysis of HPLC–DAD results, the complexity of the photodegradation process has been confirmed. Ten components were found to be involved in parallel, second- or higher-order reactions, which could not be ascertained from the spectroscopic results. The HPLC–MS results allowed postulating the identity of some of the compounds (such as hydroxyderivatives and bromophenol homologs) which resulted from the reactions of photohydrolysis, debromination and bromine transfer to different position of the phenol ring. The effect of the UV light and sunlight on the photodegradation process was found to affect mainly the rate of the reaction, but not the identity of the photoproducts formed. The advantages and limitations of the spectroscopic and chromatographic analysis were also discussed. The potential of combining spectroscopic and chromatographic data in a single multiset structure was also shown. This strategy, uses the advantage of the good definition of the process time axis from the spectroscopic experiment and the capability to distinguish among compounds, linked to the use of chromatographic information.

Organic pollutants include a very wide variety of chemical compounds with different structures, properties, functions and origins, which may produce diverse damages to the ecosystem and the human beings. This review presents the recent progress on the use of chemometrics to evaluate the occurrence of these substances in the environment. The main topics addressed are: (a) the problems related to the interpretation of the analytical measurements used in the determination of organic pollutants (quantitative analytical determinations section), (b) the profiling of the related environmental pollution sources through their compositional, geographical and temporal distribution patterns (environmental exploratory studies section) and (c) the prediction of the toxicological activity of these substances through models based on the use of structural or physical/chemical descriptors (toxicity studies section). Each section includes selected works related to pesticides, polycyclic aromatic hydrocarbons and other organic pollutants., The authors gratefully acknowledge Agencia Española de Cooperación Internacional (AECI), Project A/8964/07, Universidad Nacional de Rosario, Agencia Nacional de Promoción Científica y Tecnológica (Project PAE 22204) and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET, Argentina, Project PIP No. 1950), for financial support of this work.

This paper shows the effect of using local rank and selectivity constraints based on spatial information of spectroscopic images to increase the performance of Multivariate Curve Resolution (MCR) methods and to decrease the ambiguity of final results. Fixed Size Image Window-Evolving Factor Analysis (FSIW-EFA) is applied to discover which pixels are more suitable for the application of local rank constraints. An automated method to help in setting appropriate threshold values for the application of FSIW-EFA, based on global and local use of Singular Value Decomposition (SVD) is proposed. Additional use of correlation coefficients between selected reference spectra and pixel spectra of the image is shown to provide an alternative way for the application of the selectivity constraint in spectroscopic images for the first time. This alternative method resulted to be satisfactory when pure pixels exist.

A calcium fluoride (CaF2) plate was exposed to pulsed laser irradiations inducing surface morphological and ionization changes on its surface. More precisely surface damages mainly correspond to intrinsic defects. Electron paramagnetic resonance (EPR) hyperspectral imaging is a powerful technique able to characterize the defects formed on the CaF2 surface. Indeed, EPR hyperspectral images provide spatial and spectral information about the sample studied. In fact, these images possess a great potential to obtain accurate and reliable knowledge about the chemical composition and the distribution of the component due to the presence of the spatial aspect. However, the complexity of such hyperspectral data sets imposes the use of advanced chemometric tools to extract valuable information on the considered physicochemical system. Therefore, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) is proposed to identify and locate the different constituents in the images. The originality of this work is that it reports on the application of MCR-ALS, for the first time, on electron paramagnetic resonance (EPR) imaging data sets that will furnish the distribution maps and the spectral signatures of all components present in the sample. The results show the identification of different intrinsic defects on a CaF2 sample from the sole information in the raw image measurements and, therefore, confirm the potential of this methodology and the important role of spatial information contained in the image.

The combination of FT-IR flow titrations and second-order calibration methods is assessed for the first time as a potential method for quantifying mixtures of organic acids. FT-IR spectral information is richer and more specific than that provided by other spectrometric techniques. Also, the flow titration of the sample allows the use of second-order data analysis methods, such as multivariate curve resolution, which can quantify multianalyte systems using pure analyte standards in the presence of unknown interferents. Problems linked to FT-IR flow titrations, such as the impossibility of using buffer substances and the presence of major baseline contributions in the measurements due to changes in the water structureless absorption, have been overcome through the design of an adapted experimental setup and the use of second derivative spectra. Malic and/or tartaric acid are the model substances chosen to be quantified in the presence or absence of interferents. Both substances are diprotic acids with similar p Ka values and, therefore, similar pH-dependent evolution. Samples with one or both acids with their related standards in the presence or absence of an inert interferent (sugar) are successfully resolved and quantified for a wide range of [analyte]: [interferent] ratios with no need for pH control in the process. These results suggest this methodology as an optimal and robust alternative for routine analysis.

Electron paramagnetic resonance (EPR) spectra of mixtures are often difficult to interpret due to the superposition of spectral contribution of various species present in the complex materials. It is challenging to accurately identify the number of pure compounds present and to extract their pure spectra. In this study, the powerful chemometric method, multivariate curve resolution-alternating least squares (MCR-ALS), is applied to identify different paramagnetic centers. This method is used to simultaneously extract, with no prior knowledge, the pure spectra and the corresponding concentration profiles of all the compounds in the unknown and unresolved mixtures. The goal of our work is to apply, for the first time, this new chemometrics methodology, MCR-ALS, on EPR spectroscopic data in order to characterize a series of distinct but strongly overlapping spectra of various paramagnetic species.

The term multivariate curve resolution (MCR) designates a family of methods devoted to solving the mixture analysis problem in multicomponent samples. MCR provides the qualitative and quantitative contribution (profile) of each of the compounds in a sample from the sole information of the raw experimental data acquired. Food analysis is about knowing the qualitative and quantitative composition of foodstuffs and, hence, MCR fits very well in this scenario. Typical problems related to food analysis that can be solved by MCR are the identification and analytical determination of target compounds in the presence of unknown interferences/compounds, obtaining food fingerprint information to be used for authentication, adulteration or other purposes, and the interpretation of food processes. All these situations can be solved by handling measurements as simple as a data table with one spectrum (response) per sample or as complex as flexible multiset structures formed by several data tables (e.g. excitation/emission spectra, hyphenated separation techniques: high-performance liquid chromatography with diode array detection, liquid chromatography or gas chromatography–mass spectrometry, etc.), each of them related to a sample or to a particular food condition.

Photodegradation processes are of high relevance in environmental and pharmaceutical contexts, since the fate and identity of photoproducts are often linked to contamination or toxicology issues. The aim of this work is proposing a new general methodology to monitor and interpret photodegradation processes based on chromatographic and spectroscopic measurements and data fusion analysis. The new approach relies on two steps: (a) data fusion of HPLC-DAD–MS runs coming from process monitoring and analysis by soft-modeling multivariate curve resolution (MCR) to achieve the identification of photoproducts and a first insight on their kinetic behavior; and (b) data fusion of UV–vis spectroscopic monitoring data with HPLC-DAD process runs and analysis by hybrid hard- and soft-modeling MCR (HS-MCR) to complete the description of the kinetic mechanism of the process and to provide the related rate constants. These data fusions overcome limitations of previous approaches and have never been reported in real examples of process analysis. The ketoprofen photodegradation has been selected as the real case study to illustrate the possibilities of the described approach. In this particular case, a comprehensive description of the kinetics linked to the photochemical degradation of the ketoprofen, with all successive steps, related rate constants and photoproducts formed has been proposed. Four photodegradation products (3-(1-hydroperoxyethyl)benzophenone, 3-acetylbenzophenone, 3-(1-hydroxyethyl)benzophenone and 3-ethvlbenzophenone) could be resolved as a result of the decarboxylation of ketoprofen under UV light and the photodegradation process could be described with a kinetic model including parallel and consecutive reactions.

This work tackles the problem of convolution of femtosecond spectrokinetic data with the instantaneous response function (IRF) of the measurement by hybrid hard- and soft-modeling multivariate curve resolution (MCR). Fitting the time-dependent concentration profiles obtained during the MCR iterative optimization by a kinetic model convoluted with a parametric description of the IRF, the approach benefits of the advantages of both hard- and soft-modeling. The performance of the method is first proven on synthetic data sets with overlap of pure time-dependent concentration and/or spectral profiles and presence of interfering contributions. The method is afterwards applied to the resolution of femtosecond transient absorption spectroscopy data probing ultrafast photoinduced processes implied in the photochromism of anils. An excited-state proton transfer appearing in ~ 80 fs is characterized using an instrumental system with a severe influence of the convoluted instrumental response function, defined by a time resolution of ~ 150 fs.

A general procedure for the study of complex photodegradation processes of environmental pollutants based on chromatographic monitoring and chemometric method is proposed. The procedure consists of multiset data analysis of aliquots collected at different reaction times and injected in High-Performance Liquid Chromatography coupled to diode array detection and mass spectrometry (HPLC-DAD-MS). In this study, photodegradation of six bromophenols with different degrees of bromination has been investigated in order to find out their photodegradation pathways and kinetics and to show the potential of the procedure proposed. Multivariate curve resolution-alternating least squares (MCR-ALS) has been used to resolve chromatographic elution profiles and pure spectra of species involved in the photodegradation process and, hence, to elucidate the photodegradation mechanism and to propose the chemical structure of the main photoproducts. This study shows that chromatographic monitoring is the preferred option when photochemical systems with large number of components with similar spectra and kinetic evolution are analyzed. This work reveals the advantages of the double DAD and MS detection to provide mechanistic and structural information about these complex photodegradation processes.

Natural photochemical processes often require special instrumentation to monitor them at a suitable time scale. Rapid-scan FTIR difference spectroscopy is one of the preferred techniques to obtain rich structural information in the scale of milliseconds about photochemical processes of complex natural systems. The difference spectra obtained by this technique enhance the fine spectroscopic changes undergone during the process but require powerful data analysis methodologies to take full advantage of the information provided. Hybrid hard- and soft-modeling methodologies allow for coping with difficulties linked to the nature of the time-resolved measurement and to the complexity of the kinetic model describing the natural photochemical process. Thus, this methodology presents the following advantages: (a) handles difference spectra, taking into account the consequences of the lack of measurement about the initial stage of the process, (b) models events of the process that may be defined by a kinetic model (by hard modeling) and events that do not obey a mechanistic behavior (by soft modeling), (c) adapts to the photoaccumulation/relaxation stages of reversible photochemical processes, and (d) works simultaneously with series of experiments performed in different conditions and showing different kinetic behavior. The results of this data treatment provide complete kinetic information on the photochemical processes, e.g., rate constants, and a global picture of the difference spectra and the concentration profiles linked to each of the events (hard or soft modeled) contributing to the measured signal. The performance of the combination of time-resolved differential FTIR and hybrid hard and soft modeling is shown in a complex case study related to the photosynthetic activity of the reaction center of the purple bacteria Rhodobacter sphaeroides.

The error structure inherent to a transient absorption spectroscopy kinetic experiment is described through the examination of the related measurement error covariance matrices in the time and the spectral direction. The systematic approach proposed by Wentzell's group to characterize the measurement error covariance matrices is used for this purpose. The goals of this study are identifying and modelling the factors contributing to the error measurement and proposing a weighting error-based scheme that can be generally used when analysing data coming from this experimental setup. From the analysis performed, independent noise structure is associated with the time direction of the data sets, whereas the spectral direction is affected by two sources of correlated errors, which can be related to the probe spectrum and to the mean signal registered.

This work presents an exploratory study of the photochemical degradation process of decabromodiphenyl ether (decaBDE) and gives an interpretation of the kinetic pathway, species and effects of the key factors involved in the degradation process. Use of lowly brominated diphenyl ethers (PBDE) has been banned by the EU and there seems to be evidence of the photolytic degradation of highly brominated PBDEs into lowly brominated congeners. Hence, the importance of knowing the photodegradation process of decaBDE. The photodegradation was investigated under UV light by UV-spectrophotometric monitoring. A novel hybrid data analysis approach, based on the combination of hard- and soft-spectrophotometric multivariate curve resolution, was applied to elucidate the mechanism of the degradation process, to resolve kinetic profiles and pure spectra of the photodegradation products and to evaluate the rate constants. The photodegradation process could be described with a kinetic model based on three consecutive first-order reactions and a decrease of the degradation process was observed as solvent polarity increased. Complementary identification of photodegradation products by gas chromatography coupled to mass spectrometry using negative chemical ionization (GC-NCI-MS) is attempted. This work presents a novel attempt of describing in a comprehensive way the photochemical degradation process of decaBDE, with all successive steps and related rate constants. This study proves also the potential of the proposed hybrid data analysis methodology as a general strategy to interpret the evolution of these photochemical reactions.

This work presents some strategies to obtain time-dependent concentration profiles and spectra from time resolved spectroscopic data of complex systems. Multivariate curve resolution–alternating least squares (MCR–ALS) has been of particular interest in this field because of the possibility to implement simultaneously soft-modeling constraints and kinetic models on a multibatch resolution. This approach allows circumventing a rank deficiency inherent to time resolved difference data, even in presence of interferent species. The methodology is illustrated using examples from femtosecond photochemistry and biochemistry. Copyright © 2008 John Wiley & Sons, Ltd.

10 pages, 4 tables, 2 figures.-- PMID: 17511022 [PubMed]., We propose an instrumental method based on liquid chromatography coupled to negative ionspray ionization (ISP(−)) tandem mass spectrometry (MS/MS), for the simultaneous analysis of eight hydroxylated brominated diphenyl ethers (OH-PBDEs) in four different environmental matrices (soil, fish, sludge and particulate matter). The reversed-phase chromatographic separation was performed on a 50 mm Xbridge C18 column, and the compounds were well resolved with a gradient consisting of a ternary mixture of 5 mMammonium acetate, methanol and acetonitrile. Detection was performed in the multiple reaction monitoring (MRM) mode using the [M − H](−) ion as base peak. The fragmentation pathways of the OH-PBDEs varied according to the hydroxyl substitution in the benzene rings and produced characteristic MRM transitions needed for resolving isomeric compounds. The method is acceptable for quantification in the high picogram per gram dry weight (dw) level for allmatrices analyzed. Repeatability and reproducibility tested at 75 pg μl−1 were below 10% using internal standard quantification. The ISP(−) enhancement due to matrix effects was in the 76–132% range and the highest values corresponded to sludge samples. The use of the proposed method based on LC-ISP(−)-MS/MS opens a new way to directly determine OH-PBDEs without the need of derivatization., This research has been financed by the Spanish Ministry of Education and Science (project CTM2005-07402-C02-01/TECNO). Sílvia Mas acknowledges a Ph.D. grant UAC 2005-0071 from the Ministry of Education and Science (Associated Unit between University of Barcelona and CSIC). Dr. Isidre Casals from the Scientific and Technical Services of the University of Barcelona is acknowledged for his comments and suggestions.

Difference spectroscopy is used to monitor small changes or variations in a complex environment. Processes monitored by difference spectroscopy always have the spectrum of the initial stage subtracted and, as a consequence, a rank-deficient data set is obtained. This inherent rank-deficiency of the measurement can add to typical rank-deficiency problems in process spectroscopic monitoring, such as the presence of co-evolving reactions. In this work, we first demonstrate that a rank decrease occurs when working with difference spectra irrespective of the fact that we have full-rank or rank-deficient processes and we provide an interpretation of the chemical meaning of the process profiles and difference spectra that can be actually resolved. In a second point, the hybrid Hard–Soft-Modeling version of Multivariate Curve Resolution-Alternating Least Squares is used to resolve this kind of data sets. Special attention is paid to the strategy of application of specific hard-modeling constraints to circumvent rank-deficiency in difference spectroscopy. It is illustrated on simulated data and on two case studies chosen in the field of time-resolved FTIR spectroscopy and UV–Vis transient absorption spectroscopy. Differences in interpretation of the resolved profiles and in the application of constraints between direct and difference spectroscopy are exposed in detail.