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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.

Tessellated cartilage is a distinctive composite tissue forming the bulk of the skeleton of cartilaginous fishes (e.g. sharks and rays), built from unmineralized cartilage covered at the surface by a thin layer of mineralized tiles called tesserae. The finescale structure and composition of elasmobranch tessellated cartilage has largely been investigated with electron microscopy, micro-computed tomography and histology, but many aspects of tissue structure and composition remain uncharacterized. In our study, we demonstrate that the tessellated cartilage of a stingray exhibits a strong and diverse autofluorescence, a native property of the tissue which can be harnessed as an effective label-free imaging technique. The autofluorescence signal was excited using a broad range of wavelengths in confocal and light sheet microscopy, comparing several sample preparations (fresh; demineralized and paraffin-embedded; non-demineralized and plastic-embedded) and imaging the tissue at different scales. Autofluorescence varied with sample preparation with the signal in both plastic- and paraffin-embedded samples strong enough to allow visualization of finescale (≥ 1 μm) cellular and matrix structures, such as cell nuclei and current and former mineralization fronts, identifiable by globular mineralized tissue. A defined pericellular matrix (PCM) surrounding chondrocytes was also discernible, described here for the first time in elasmobranchs. The presence of a PCM suggests similarities with mammalian cartilage regarding how chondrocytes interact with their environment, the PCM in mammals acting as a transducer for biomechanical and biochemical signals. A posterior analysis of hyperspectral images by an MCR-ALS unmixing algorithm allowed identification of several distinct fluorescence signatures associated to specific regions in the tissue. Some fluorescence signatures identified could be correlated with collagen type II, the most abundant structural molecule of cartilage. Other fluorescence signatures, however, remained unidentified, spotlighting tissue regions that deserve deeper characterization and suggesting the presence of molecules still unidentified in elasmobranch skeletal cartilage. Our results show that autofluorescence can be a powerful exploratory imaging tool for characterizing less-studied skeletal tissues, such as tessellated cartilage. The images obtained are largely comparable with more commonly used techniques, but without the need for complicated sample preparations or external staining reagents standard in histology and electron microscopy (TEM, SEM).

Heterogeneity characterization is crucial to define the quality of end products and to describe the evolution of processes that involve blending of compounds. The heterogeneity concept describes both the diversity of physicochemical characteristics of sample fragments (constitutional heterogeneity) and the diversity of spatial distribution of the materials/compounds in the sample (distributional heterogeneity, DH). Hyperspectral images (HSIs) are unique analytical measurements that provide physicochemical and spatial information on samples and, hence, are ideal to perform heterogeneity studies. This work proposes a new methodology combining HSI and variographic analysis to obtain a good qualitative and quantitative description of global heterogeneity (GH) and DH for samples and blending processes. An initial step of image unmixing provides a set of pure distribution maps of the blending constituents as a function of time that allows a qualitative visualization of the heterogeneity variation along the blending process. These maps are used as seeding information for a subsequent variographic analysis that furnishes the newly designed quantitative global heterogeneity index (GHI) and distributional uniformity index (DUI), related to GH and DH indices, respectively. GHI and DUI indices can be described at a sample level and per component within the sample. GHI and DUI curves of blending processes are easily interpretable and adaptable for blending monitoring and control and provide invaluable information to understand the sources of the abnormal blending behavior.

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.

Synchronization of variable trajectories from batch process data is a delicate operation that can induce artifacts in the definition of multivariate statistical process control (MSPC) models for real-time monitoring of batch processes. The current paper introduces a new synchronization-free approach for online batch MSPC. This approach is based on the use of local MSPC models that cover a normal operating conditions (NOC) trajectory defined from principal component analysis (PCA) modeling of non-synchronized historical batches. The rationale behind is that, although non-synchronized NOC batches are used, an overall NOC trajectory with a consistent evolution pattern can be described, even if batch-to-batch natural delays and differences between process starting and end points exist. Afterwards, the local MSPC models are used to monitor the evolution of new batches and derive the related MSPC chart. During the real-time monitoring of a new batch, this strategy allows testing whether every new observation is following or not the NOC trajectory. For a NOC observation, an additional indication of the batch process progress is provided based on the identification of the local MSPC model that provides the lowest residuals. When an observation deviates from the NOC behavior, contribution plots based on the projection of the observation to the best local MSPC model identified in the last NOC observation are used to diagnose the variables related to the fault. This methodology is illustrated using two real examples of NIR-monitored batch processes: a fluidized bed drying process and a batch distillation of gasoline blends with ethanol.

Industrially tempered dark chocolate processed under different conditions was analyzed using Attenuated Total Reflection-Fourier Transform-Infrared spectroscopy (ATR-FT-IR). Spectra were collected during the cooling process right after tempering using two distinct machines and machine operational modes and over short and long storage (1 day and two months, respectively). To interpret the spectra collected, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis was used. MCR-ALS incorporated a dedicated implementation of hard modeling constraints for the elucidation of the spectral and concentration profiles of chocolate contributions during the monitored process with the aim of reducing ambiguity in the data interpretation. Thus, the spectral signature of the amorphous form of chocolate was constrained to follow a Gaussian shape, in line with previously reported works and with measurements observed at the beginning of the cooling process. Additionally, hard modeling constraints were applied to the concentration profiles linked to the cooling process by implementing the Avrami kinetic model, often postulated to describe thermally induced fat crystal transformations. The application of the hard modeling constraints eased the generation of interpretable solutions and resulted in concentration profiles and spectral signatures that were defining much better the crystal state of the chocolate samples. A two-component system explained the studied procedures, with a contribution S1 representing the highly crystalline state of the chocolate fats and S2 representative of the amorphous/less stable state. In general, we were able to track the transition from a less ordered to a highly ordered crystal state of the fats during the cooling stage of chocolate. After short storage, the chocolates had contributions of both S1 and S2 profiles. After long storage, the S1 crystalline form was the most dominant interpreted as the reorganization of the triglyceride acyl chains towards a thermodynamically favorable state. The latter was observed for all analyzed chocolates, implying that, regardless the tempering process or temper regime tested, the fats in chocolates reach the same high crystalline state after maturing.

Recently, the presence of i-motif structures at C-rich sequences in human cells and their regulatory functions have been demonstrated. Despite numerous steady-state studies on i-motif at neutral and slightly acidic pH, the number and nature of conformation of this biological structure are still controversial. In this work, the fluorescence lifetime of labelled molecular beacon i-motif-forming DNA sequences at different pH values is studied. The influence of the nature of bases at the lateral loops and the presence of a Watson–Crick-stabilized hairpin are studied by means of time-correlated single-photon counting technique. This allows characterizing the existence of several conformers for which the fluorophore has lifetimes ranging from picosecond to nanosecond. The information on the existence of different i-motif structures at different pH values has been obtained by the combination of classical global decay fitting of fluorescence traces, which provides lifetimes associated with the events defined by the decay of each sequence and multivariate analysis, such as principal component analysis or multivariate curve resolution based on alternating least squares. Multivariate analysis, which is seldom used for this kind of data, was crucial to explore similarities and differences of behaviour amongst the different DNA sequences and to model the presence and identity of the conformations involved in the pH range of interest. The results point that, for i-motif, the intrachain contact formation and its dissociation show lifetimes ten times faster than for the open form of DNA sequences. They also highlight that the presence of more than one i-motif species for certain DNA sequences according to the length of the sequence and the composition of the bases in the lateral loop., Funding from Spanish government [CTQ2014-61758-EXP, CTQ2014-52588-R, CTQ2015-66254-C2-2-P]; Autonomous Catalan government [2017SGR114]. Funding for open access charge: University of Bracelona [CTQ2015-66254-C2-2-P] and Université de Lille, LASIR CNRS.

Hyperspectral imaging (HSI) is a useful non-invasive technique that offers spatial and chemical information of samples. Often, different HSI techniques are used to obtain complementary information from the sample by combining different image modalities (Image Fusion). However, issues related to the different spatial resolution, sample orientation or area scanned among platforms need to be properly addressed. Unmixing methods are helpful to analyze and interpret the information of HSI related to each of the components contributing to the signal. Among those, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) offers very suitable features for image fusion, since it can easily cope with multiset structures formed by blocks of images coming from different samples and platforms and allows the use of optional and diverse constraints to adapt to the specific features of each HSI employed. In this work, a case study based on the investigation of cross-sections from rice leaves by Raman, synchrotron infrared and fluorescence imaging techniques is presented. HSI of these three different techniques are fused for the first time in a single data structure and analyzed by MCR-ALS. This example is challenging in nature and allows describing clearly the necessary steps required to perform unmixing in an image fusion context.

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.

Controlling blending processes of solid material using advanced real-time sensing technologies tools is crucial to guarantee the quality attributes of manufactured products from diverse industries. The use of process analytical technology (PAT) tools based on chemical imaging systems are useful to assess heterogeneity information during mixing processes. Recently, a powerful procedure for heterogeneity assessment based on the combination of off-line acquired chemical images and variographic analysis has been proposed to provide specific heterogeneity indices related to global and distributional heterogeneity. This work proposes a novel PAT tool combining in situ chemical imaging and variogram-derived quantitative heterogeneity indices for the real-time monitoring of blending processes. The proposed method, so called sliding window variographic image analysis (SWiVIA), derives heterogeneity indices in real-time associated with a sliding image window that moves continuously until the full blending time interval is covered. The SWiVIA method is thoroughly assessed paying attention at the effect of relevant factors for continuous blending monitoring and heterogeneity description, such as the scale of scrutiny needed for heterogeneity definition or the blending period defined to set the sliding image window. SWiVIA is tested on blending runs of pharmaceutical and food products monitored with an in situ near-infrared chemical imaging system. The results obtained help to detect abnormal mixing phenomena and can be the basis to establish blending process control indicators in the future. SWiVIA is adapted to study blending behaviors of the bulk product or compound-specific blending evolutions.

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 implies often the concatenation of data sets that present an enormous diversity in terms of information, size, and behavior. The pieces of information connected reflect the variation apportioned by components, events, or sources that are differently represented and, yet, complement each other in the data blocks analyzed simultaneously. Multivariate curve resolution (MCR) was born as a tool to unmix the information in a single data set into a bilinear model of chemically meaningful profiles associated with pure components or sources. With the increase of complexity of chemical problems and the need to perform data fusion to understand all the aspects related to a particular scenario, multiset analysis by MCR came into play. Multiset analysis performed by MCR has two main advantages, the first stemming from the intrinsic versatile multiset structure and the second linked to the per block, per component, and per mode flexible application of constraints to model pure profiles by MCR, which covers the specific needs of the diverse blocks of information present in a data fusion framework. These two essential aspects are extensively developed in this chapter, and a final representative report on the main fields of application of data fusion by MCR is also provided.

Multivariate curve resolution (MCR) designs a family of methods able to provide qualitative (spectral signatures) and quantitative (concentration maps) information from image constituents using only the spectroscopic image measurement as initial information. MCR is a chemometric tool that uses constraints related to natural chemical or mathematical properties to drive the optimization of the concentration and spectral profiles sought. Progress in the use of MCR to analyze hyperspectral images has implied the development of constraints that take into account spatial image properties, or the use of the method for the analysis of image multisets formed by related images collected with the same platform or for image fusion scenarios involving images of different platforms. Besides, MCR scores and loadings are excellent compressed, noise-filtered, and compound-specific image information that can be used for further analysis purposes, such as segmentation, identification, quantitative analysis, or discriminant analysis.

This article describes the general modus operandi of model-free Multivariate Curve Resolution iterative methods, i.e., the recovery of pure concentration profiles and responses (spectra) from the iterative optimization of initial estimates under the action of constraints. The basic bilinear curve resolution model is expressed in two different forms, as: (1) or (2). Methods based on Eq. (1), such as iterative target transformation factor analysis (ITTFA) and multivariate curve resolution-alternating least squares (MCR-ALS), solve for the C and/or ST matrices directly, whereas methods based on Eq. (2), such as Resolving Factor Analysis (RFA) and the resolution of matrices through elementary matrix transformations (Gentle) optimize the transformation matrix R in such a way that are chemically meaningful. All these methods are described but, since MCR-ALS is the method that has evolved more in time, explanations about advances specifically linked to the use of this algorithm are explained in more detail.

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.

This paper shows the application of Raman and infrared hyperspectral imaging combined with multivariate curve resolution (MCR) to the analysis of the constituents of commercial chocolate samples. The combination of different spectral data pretreatment methods allowed decreasing the high fluorescent Raman signal contribution of whey in the investigated chocolate samples. Using equality constraints during MCR analysis, estimations of the pure spectra of the chocolate sample constituents were improved, as well as their relative contributions and their spatial distribution on the analyzed samples. In addition, unknown constituents could be also resolved. White chocolate constituents resolved from Raman hyperspectral image indicate that, at macro scale, sucrose, lactose, fat, and whey constituents were intermixed in particles. Infrared hyperspectral imaging did not suffer from fluorescence and could be applied for white and milk chocolate. As a conclusion of this study, micro-hyperspectral imaging coupled to the MCR method is confirmed to be an appropriate tool for the direct analysis of the constituents of chocolate samples, and by extension, it is proposed for the analysis of other mixture constituents in commercial food samples.

The objective of this work is assessing whether the combination of Raman spectroscopy and chemometric tools is appropriate to differentiate blue ballpoint pen inks. Fourteen commercial blue ballpoint pen inks from different brands and models were studied and Raman spectra were obtained on ink lines written on A4 sulfite paper. First, a study of the best Raman configurations, in terms of laser intensity used and acquisition mode, was carried out to ensure sufficient spectroscopic quality without damaging the sample. Chemometric methods were applied first to improve the definition of spectral bands and to suppress fluorescence contributions from the signal. Once the spectra were suitably preprocessed, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied to explore whether the different inks could be distinguished from their Raman spectra. Almost all inks could be gradually differentiated, through successive PCA analyses or looking at the different levels of the dendrogram structure provided by HCA. From these exploratory results, a tree structure was constructed based on PCA and HCA results in order to reflect the degree of similarity among ink classes. This tree structure was used as the basis to develop hierarchical classification models based on partial least squares-discriminant analysis (PLS-DA). Correct classification of inks was achieved by these PLS-DA models built and the most important regions to identify the ink classes were detected using the variable importance in projection plots (VIPs). The combination of Raman spectroscopy and chemometrics has been proven to be a promising fast non-destructive tool to differentiate among very similar ink types in documents.

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 i-motif is a DNA structure formed by cytosine-rich sequences, very relevant from a biochemical point of view and potentially useful in nanotechnology as pH-sensitive nanodevices or nanomotors. To provide a different view on the structural changes and dynamics of direct excitation processes involving i-motif structures, the use of rapid-scan FTIR spectroscopy is proposed. Hybrid hard- and soft-modelling based on the Multivariate Curve Resolution by Alternating Least Squares (MCR-ALS) algorithm has been used for the resolution of rapid-scan FTIR spectra and the interpretation of the photochemically induced time-dependent conformational changes of i-motif structures. The hybrid hard- and soft-modelling version of MCR-ALS (HS-MCR), which allows the introduction of kinetic models to describe process behavior, provides also rate constants associated with the transitions modeled. The results show that UV irradiation does not produce degradation of the studied sequences but induces the formation of photodimers. The presence of these affect much more the stability of i-motif structures formed by short sequences than that of those formed by longer sequences containing additional structural stabilizing elements, such as hairpins., Sanae Benabou thanks the LAboratoire de Spectrochimie Infrarouge et Raman (LASIR) for all the support, especially, Dr. Isabelle Waele for her help in the acquisition of rapid-scan FTIR measurements. Funding from Spanish government (CTQ2017-84415-R and CTQ2015-66254-C2-2-P) and recognition from the Autonomous Catalan government (2017SGR753 and 2017SGR114) are acknowledged.

Raman spectroscopy (RS) has shown promise as a tool to reveal biochemical changes that occur in cancer processes at the cellular level. However, when analyzing clinical samples, RS requires improvements to be able to resolve biological components from the spectra. We compared the strengths of Multivariate Curve Resolution (MCR) versus Principal Component Analysis (PCA) to deconvolve meaningful biological components formed by distinct mixtures of biological molecules from a set of mixed spectra. We exploited the flexibility of the MCR algorithm to easily accommodate different initial estimates and constraints. We demonstrate the ability of MCR to resolve undesired background signals from the RS that can be subtracted to obtain clearer cancer cell spectra. We used two triple negative breast cancer cell lines, MDA-MB 231 and MDA-MB 435, to illustrate the insights obtained by RS that infer the metabolic changes required for metastasis progression. Our results show that increased levels of amino acids and lower levels of mitochondrial signals are attributes of bone metastatic cells, whereas lung metastasis tropism is characterized by high lipid and mitochondria levels. Therefore, we propose a method based on the MCR algorithm to achieve unique biochemical insights into the molecular progression of cancer cells using RS.

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.

The implementation of a blend monitoring and control method based on a process analytical technology such as near infrared spectroscopy requires the selection and optimization of numerous criteria that will affect the monitoring outputs and expected blend end-point. Using a five component formulation, the present article contrasts the modeling strategies and end-point determination of a traditional quantitative method based on the prediction of the blend parameters employing partial least-squares regression with a qualitative strategy based on principal component analysis and Hotelling's T(2) and residual distance to the model, called Prototype. The possibility to monitor and control blend homogeneity with multivariate curve resolution was also assessed. The implementation of the above methods in the presence of designed experiments (with variation of the amount of active ingredient and excipients) and with normal operating condition samples (nominal concentrations of the active ingredient and excipients) was tested. The impact of criteria used to stop the blends (related to precision and/or accuracy) was assessed. Results demonstrated that while all methods showed similarities in their outputs, some approaches were preferred for decision making. The selectivity of regression based methods was also contrasted with the capacity of qualitative methods to determine the homogeneity of the entire formulation.

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.

This work explores the feasibility of screening and determination of ten polycyclic aromatic hydrocarbons (PAHs) through excitation–emission fluorescence matrices (EEMs) and in the presence of interferences by using different second-order data analysis algorithms: parallel factor analysis (PARAFAC), multivariate curve resolution–alternating least squares (MCR–ALS), and unfolded partial least squares coupled to residual bilinearization (U-PLS/RBL). The scope of the proposed techniques is discussed for qualitative and quantitative analysis of the selected PAHs in the presence of interferences and sample matrix effects. The target compounds were 9 of the 16 United States Environmental Protection Agency (US-EPA) priority PAHs: fluoranthene, benzo[a]anthracene, chrysene, benzo[b]fluoranthene, benzo[k]fluoranthene, benzo[a]pyrene, dibenzo[a,h]anthracene, benzo[ghi]perylene, indeno[1,2,3-cd]pyrene, and one internal standard: 2-2′ binaphthyl. The suitability of these methods was compared under different chemical situations, where they were demonstrated to be powerful tools to resolve complex mixtures of analytes of similar structure in the presence of unexpected compounds. Qualitative and quantitative analysis of samples required the joint effort of the different algorithms to exploit the advantages of fast screening (PARAFAC and MCR–ALS) and accurate analyte determination (U-PLS/RBL) provided by the different methods.

The development of a method based on the combination of excitation–emission fluorescence matrices (EEMs) and second-order algorithms is proposed to identify and quantify 10 polycyclic aromatic hydrocarbons (PAHs) in extracts of aerosol samples, a particularly complex sample matrix because of the low amount of PAHs and the numerous fluorescent interferences. Parallel factor analysis, unfolded partial least squares coupled to residual bilinearization (RBL) and multivariate curve resolution – alternating least squares offered satisfactory results for their identification and quantification and were also helpful for the optimization of the extraction procedure for these substances by means of analysis of a standard reference material. Multivariate curve resolution – alternating least squares and parallel factor analysis combined with EEM proved to be fast and cheap analysis methods that are able to do semiquantitative monitoring of PAHs patterns in the inhalable and respirable fractions of atmospheric aerosols, important features for routine environmental monitoring. Accurate quantifications could also be achieved when the strategy of standard addition method was used. Unfolded partial least squares/RBL achieved slightly better quantitative results when sample matrices were moderately complex; in aerosol sample analysis, the selection of number of RBL contributions turned particularly difficult—because of the lack of information on the sample composition—and the time-consuming application of the method did not suggest its use for routine environmental monitoring. Finally, results of EEM with second-order methods in agreement with gas chromatography-mass spectrometry analysis of ambient air samples proved the suitability of the methodology proposed because of the sensitivity of fluorescence measurements and the lower analytical cost and fast computation time compared with standard analytical procedures. Copyright © 2014 John Wiley & Sons, Ltd.

This article is a tutorial that focuses on the main aspects to be considered when applying Multivariate Curve Resolution to analyze multicomponent systems, particularly when the Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) algorithm is used. These aspects include general MCR comments on the potential fields of application and construction of data structures and details linked to each of the steps in the application workflow of the MCR-ALS algorithm (e.g., selection of initial estimates, choice and application of constraints, quality parameters of models and assessment of ambiguity,…). Two examples with downloadable data sets are shown for orientation on the practical use of this methodology.