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Multilabel fluorescence imaging is essential for the visualization of complex systems, though a major challenge is the limited width of the usable spectral window. Here, we present a new method, exNEEMO, enabling per-pixel unmixing of spectrally-overlapping fluorophores based on their light-induced dynamics, in a way that is compatible with a very broad range of timescales over which these dynamics may occur. Our approach makes use of non-negative least squares projection to decompose the observed emission given the reference responses of the individual fluorophores that are present in the sample. We apply the approach to the simultaneous of four green photochromic fluorescent proteins, allowing us to visualize the distribution of each fluorophore at the full resolution of the imaging. Our methodology opens new possibilities for the multiplexing of fluorescence imaging.

In the context of multivariate curve resolution (MCR) and spectral unmixing, essential information (EI) corresponds to the most linearly dissimilar rows or/and columns of a two-way data matrix. These rows/columns are called essential because they are indispensable to reproduce the full data matrix in a convex linear way. The selection of EI is driven by the properties of linear spectral mixtures. In recent works, it has been revealed to be a very useful practical tool to select the most relevant spectral information before MCR analysis, key features being speed and compression ability. However, the canonical approach relies on principal component analysis (PCA) to evaluate the convex hull that encapsulates the data structure in the normalized score space. This implies that the evaluation of the essentiality of each spectrum can only be achieved after all the spectra have been acquired by the instrument. This paper proposes a new approach, denominated EIFD, which consists in extracting EI in the Fourier domain. Spectral information is transformed into Fourier coefficients and EI is assessed from a convex hull analysis of the data point cloud in the 2D phasor plots of the first few harmonics. The proposed EIFD approach is broadly applicable and very efficient in terms of spectral compression and denoising. In particular, it has the crucial advantage that the coordinate system of a phasor plot does not depend on the data themselves. This enables dynamic (i.e., performed during data acquisition) and chemometric-driven (i.e., based on spectral relevance for unmixing) selective sampling. The usefulness of EIFD is shown by analyzing Raman hyperspectral microimaging data, demonstrating the possibility of a 50-fold acceleration of Raman acquisition.

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.

Alternating least squares, within the multivariate curve resolution framework has seen a lot of practical applications and shows its distinction with its relatively simple and flexible implementation. However, the limitations of least squares should be considered carefully when deviating from the standard assumed data structure. Within this work we highlight the effects of noise in the presence of minor components, and we propose a novel weighting scheme within the weighted multivariate curve-resolution-alternating least squares framework, to resolve it. Two simulated and one Raman imaging case is investigated, by comparing the novel methodology against standard multivariate curve resolution-alternating least squares and essential spectral pixel selection. A trade-off is observed between current methods, while the novel weighting scheme demonstrates a balance, where the benefits of the previous two methods are retained.

The way in which aquatic systems is sampled has a strong influence on our understanding of them, especially when they are highly dynamic. High frequency sampling has the advantage over spot sampling for representativeness but leads to a high amount of analysis. This study proposes a new methodology to choose when sampling accurately with an automated sampler coupled with a high frequency (HF) multiparameter probe. After each HF measurement, an optimised sampling algorithm (OSA) determines on-the-fly the relevance of taking a new sample in relation to previous waters already collected. Once the OSA was optimised, considering the number of HF parameters and their variabilities, it was demonstrated through a study case that the number of samples could be significantly reduced, while still covering periods of low and high variabilities. The comparison between the total HF dataset and the sampled subdataset shows that physicochemical parameter variability is preserved (Pearson correlations0.96) as well as the multiparameter variability (PCA axes remained similar with Tucker congruence0.99). This algorithm simplifies HF studies by making it easier to take samples during brief phenomena such as storms or accidental spills that are often poorly monitored. In addition, it optimises the number of samples to be taken to correctly describe a system and thus reduce the human and financial costs of these environmental studies.

Least squares-based estimations lay behind most chemometric methodologies. Their properties, though, have been extensively studied mainly in the domain of regression, in relation to which the effect of well-known deleterious factors (like object leverage or data distributions deviating from ideal conditions) on the accuracy of the prediction of an external response variable have been thoroughly assessed. Conversely, much less attention has been paid to what these factors might yield in alternative scenarios, where least squares approaches are still utilised, yet the objectives of data modelling may be very different. As an example, one can think of multivariate curve resolution (MCR) problems which are usually addressed by means of Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). In this respect, this article wants to offer a perspective on the basic principles of MCR-ALS from the regression point of view. In particular, the following critical aspects will be highlighted: i) in the presence of minor components, if the number of analysed data points is too large, the leverage of those that may be essential for a MCR-ALS resolution might become too low for guaranteeing its correctness and ii) in order to overcome this black hole effect and improve the accuracy of the MCR-ALS output, data pruning can be exploited. More in detail, this communication will provide a practical illustration of such aspects in the field of hyperspectral imaging where even single experimental runs may lead to the generation of massive amounts of spectral recordings.

Multivariate curve resolution (MCR) methods aim at extracting pure component profiles from mixed spectral data and can be applied to high-dimensional data, e.g., from process spectroscopy or hyperspectral imaging techniques. One often observes that some parts of this data, namely certain rows and columns of the data matrix, are considered essential for MCR outcomes, while other parts are of minor importance. Some methods for determining essential data are known, but all have different disadvantages concerning the application for noisy data. This work presents a new approach on how to detect the essential information for noisy, experimental spectral data. Active nonnegativity constraints in combination with duality arguments are the key ingredients for determining essential spectra and frequency channels. The new approach is conceptually simple, computationally cheap and stable with respect to noise. The algorithm is tested for noisy experimental Raman, UV-Vis and FTIR-SEC data.

An approach is proposed and illustrated for the joint selection of essential samples and essential variables of a data matrix in the frame of spectral unmixing. These essential features carry the signals required to linearly recover all the information available in the rows and columns of a data set. Working with hyperspectral images, this approach translates into the selection of essential spectral pixels (ESPs) and essential spatial variables (ESVs). This results in a highly-reduced data set, the benefits of which can be minimized computational effort, meticulous data mining, easier model building as well as better problem understanding or interpretation. Working with both simulated and real data, we show that (i) reduction rates of over 99% can be typically obtained, (ii) multivariate curve resolution - alternating least squares (MCR-ALS) can be easily applied on the reduced data sets and (iii) the full distribution maps and spectral profiles can be readily obtained from the reduced profiles and the reduced data sets (without using the full data matrix).

Ripening is a crucial step to guarantee the high commercial value of cheddar cheese, one of the dairy products the European Union exports the most. Although several methods have lately been proposed to assess its ageing process from a chemical point of view, the majority of them is not particularly time-efficient and implies destructive analytical tests, thus, exhibiting limitations for, e.g., industrial applications. Here, a fast approach based on combining Raman and Mid-InfraRed (MIR) spectroscopy with ANOVA-Simultaneous Component Analysis (ASCA) is proposed in a low-level data fusion framework. This approach allowed to evaluate how storage temperature and time (as well as their interaction) influence cheddar ripening in a relatively cheap, rapid and green fashion.

International audience; A substantial number of Orange Carotenoid Protein (OCP) studies have aimed to describe the evolution of singlet excited states leading to the formation of a photoactivated form, OCPR. The most recent one suggests that 3 ps-lived excited states are formed after the sub-100 fs decay of the initial S2 state. The S* state, which has the longest reported lifetime of a few to tens of picoseconds, is considered to be the precursor of the first red photoproduct P1. Here, we report the ultrafast photodynamics of the OCP from Synechocystis PCC 6803 carried out using visible-near infrared femtosecond time-resolved absorption spectroscopy as a function of the excitation pulse power and wavelength. We found that a carotenoid radical cation can form even at relatively low excitation power, obscuring the determination of photoactivation yields for P1. Moreover, the comparison of green (540 nm) and blue (470 nm) excitations revealed the existence of an hitherto uncharacterized excited state, denoted as S∼, living a few tens of picoseconds and formed only upon 470 nm excitation. Because neither the P1 quantum yield nor the photoactivation speed over hundreds of seconds vary under green and blue continuous irradiation, this S∼ species is unlikely to be involved in the photoactivation mechanism leading to OCPR. We also addressed the effect of His-tagging at the N- or C-termini on the excited-state photophysical properties. Differences in spectral signatures and lifetimes of the different excited states were observed at a variance with the usual assumption that His-tagging hardly influences protein dynamics and function. Altogether our results advocate for the careful consideration of the excitation power and His-tag position when comparing the photoactivation of different OCP variants and beg to revisit the notion that S* is the precursor of photoactivated OCPR.

Least squares-based estimations lay behind most chemometric methodologies. Their properties, though, have been extensively studied mainly in the domain of regression, in relation to which the effect of well-known deleterious factors (like object leverage or data distributions deviating from ideal conditions) on the accuracy of the prediction of an external response variable have been thoroughly assessed. Conversely, much less attention has been paid to what these factors might yield in alternative scenarios, where least squares approaches are still utilised, yet the objectives of data modelling may be very different. As an example, one can think of multivariate curve resolution (MCR) problems which are usually addressed by means of Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). In this respect, this article wants to offer a perspective on the basic principles of MCR-ALS from the regression point of view. In particular, the following critical aspects will be highlighted: in certain situations, i) if the number of analysed data points is too large, the leverage of those that may be essential for a MCR-ALS resolution might become too low for guaranteeing its correctness and ii) in order to overcome this black hole effect and improve the accuracy of the MCR-ALS output, data pruning - i.e., the reduction of the amount of observations of the investigated datasets - can be exploited. More in detail, this communication will provide a practical illustration of such aspects in the field of hyperspectral imaging where even single experimental runs may lead to the generation of massive amounts of spectral recordings.

We present a fast and simple algorithm for super-resolution with single images. It is based on penalized least squares regression and exploits the tensor structure of two-dimensional convolution. A ridge penalty and a difference penalty are combined; the former removes singularities, while the latter eliminates ringing. We exploit the conjugate gradient algorithm to avoid explicit matrix inversion. Large images are handled with ease: zooming a 100 by 100 pixel image to 800 by 800 pixels takes less than a second on an average PC. Several examples, from applications in wide-field fluorescence microscopy, illustrate performance.

International audience; RsEGFP2 is a reversibly photoswitchable fluorescent protein used in super-resolved optical microscopies, which can be toggled between a fluorescent On state and a nonfluorescent Off state. Previous time-resolved ultraviolet-visible spectroscopic studies have shown that the Off-to-On photoactivation extends over the femto- to millisecond time scale and involves two picosecond lifetime excited states and four ground state intermediates, reflecting a trans-to-cis excited state isomerization, a millisecond deprotonation, and protein structural reorganizations. Femto- to millisecond time-resolved multiple-probe infrared spectroscopy (TRMPS-IR) can reveal structural aspects of intermediate species. Here we apply TRMPS-IR to rsEGFP2 and implement a Savitzky-Golay derivative analysis to correct for baseline drift. The results reveal that a subpicosecond twisted excited state precursor controls the trans-to-cis isomerization and the chromophore reaches its final position in the protein pocket within 100 ps. A new step with a time constant of 42 ns is reported and assigned to structural relaxation of the protein that occurs prior to the deprotonation of the chromophore on the millisecond time scale.

A substantial number of Orange Carotenoid Protein (OCP) studies have aimed to describe the evolution of singlet excited states leading to the formation of photo-activated form, OCPR. The most recent one suggests that three picosecond-lived excited states are formed after the sub-100 fs decay of the initial S2 state. The S* state which has the longest reported lifetime of a few to tens of picoseconds is considered to be the precursor of the first red photoproduct P1. Here, we report the ultrafast photo-dynamics of the OCP from Synechocystis PCC 6803, carried out using Visible-NIR femtosecond time-resolved absorption spectroscopy as a function of the excitation pulse power and wavelength. We found that a carotenoid radical cation can form even at relatively low excitation power, obscuring the determination of photo-activation yields for P1. Moreover, the comparison of green (540 nm) and blue (470 nm) excitations revealed the existence of an hitherto uncharacterized excited state, denoted as S∼, living a few tens of picoseconds and formed only upon 470 nm excitation. Since neither the P1 quantum yield nor the photo-activation speed over hundreds of seconds vary under green and blue continuous irradiation, this S∼ species is unlikely to be involved in the photo-activation mechanism leading to OCPR. We also addressed the effect of His-tagging at the N- or C-termini on excited state photo-physical properties. Differences in spectral signatures and lifetimes of the different excited states were observed, at variance with the usual assumption that His-tagging hardly influences protein dynamics and function. Altogether our results advocate for careful consideration of the excitation power and His-tag position when comparing the photo-activation of different OCP variants, and beg to revisit the notion that S* is the precursor of photoactivated OCPR.TOC

A substantial number of Orange Carotenoid Protein (OCP) studies have aimed to describe the evolution of singlet excited states leading to the formation of a photoactivated form, OCP

A novel fast and automatic methodology for the hierarchical classification and similarity matching of mid-infrared spectra of paint samples based on the principles of Soft Independent Modelling of Class Analogy (SIMCA) and on the definition and properties of the Mahalanobis distance is here proposed. This approach was tested in a so-called market study (i.e., targeting products largely accessible to the general public and conceived for a considerably wide range of usages) conducted across the surroundings of the city of Lille, in France, and has permitted not only to successfully achieve the chemical characterisation of most of the analysed samples but also to discover specific commonality patterns among specimens sharing the same chemical features.

We propose a methodology to select essential spectral pixels (ESPs) of chemical images. These pixels are on the outer envelope of the principal component scores of the data and can be identified by convex-hull computation. As ESPs carry all the linearly mixed spectral information, large hyperspectral images can be dramatically reduced before multivariate curve resolution (MCR) analysis. We investigated chemical images of different spectroscopies, sizes, and complexities and show that the analysis of full data sets of hundreds of thousands of spectral pixels only require a few tenths of them.

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 is a way to explore the spatial and spectral information of the different compounds in chemical or biological samples. In addition, multivariate curve resolution - alternating least squares (MCR-ALS) can be used to extract this information based on the bilinearity assumption. However, it is well-known that using proper constraints can reduce the amount of uncertainty in the results of MCR, which is called rotational ambiguity. In MCR-ALS analysis of hyperspectral images, different image processing techniques, such as model fitting, image segmentation or sparse image recovery can be applied as spatial constraints. In this contribution, we aim to investigate how the use of these spatial constraints limits the extent of rotational ambiguity of MCR-ALS solutions. For this purpose, we evaluate the extent of rotational ambiguity and use Borgen plots to visualize it. We show on simulations and real hyperspectral imaging data that accuracy of the results is improved when spatial constraints are applied.

The emergence of new spectral imaging applications in many science fields and in industry has not come to be a surprise, considering the immense potential this technique has to map spectral information. In the case of near-infrared spectral imaging, a rapid evolution of the technology has made it more and more appealing in non-destructive analysis of food and materials as well as in process monitoring applications. However, despite its great diffusion, some challenges remain open from the data analysis point of view, with the aim to fully uncover patterns and unveil the interplay between both the spatial and spectral domains. Here we propose a new approach, called Image Decomposition, Encoding and Localization (IDEL), where a spatial perspective is taken for the analysis of spectral images, while maintaining the significant information within the spectral domain. The methodology benefits from wavelet transform to exploit spatial features, encoding the outcoming images into a set of descriptors and utilizing multivariate analysis to isolate and extract the significant spatial-spectral information. A forensic case study of near-infrared images of biological stains on cotton fabrics is used as a benchmark. The stain and fabric have hardly distinguishable spectral signatures due to strong scattering effects that originate from the rough surface of the fabric and the high spectral absorbance of cotton in the near-infrared range. There is no selective information that can isolate signals related to these two components in the spectral images under study, and the complex spatial structure is highly interconnected to the spectral signatures. IDEL was capable of isolating the stains, (spatial) scattering effects, and a possible drying effect from the stains. It was possible to recover, at the same time, specific spectral regions that mostly highlight these isolated spatial structures, which was previously unobtainable.

Super-resolution optical fluctuation imaging (SOFI) is a well-known super-resolution technique appreciated for its versatility and broad applicability. However, even though an extended theoretical description is available, it is still not fully understood how the interplay between different experimental parameters influences the quality of a SOFI image. We investigated the relationship between five experimental parameters (measurement time, on-time ton, off-time toff, probe brightness, and out of focus background) and the quality of the super-resolved images they yielded, expressed as Signal to Noise Ratio (SNR). Empirical relationships were modeled for second- and third-order SOFI using data simulated according to a D-Optimal design of experiments, which is an ad-hoc design built to reduce the experimental load when the total number of trials to be conducted becomes too high for practical applications. This approach proves to be more reliable and efficient for parameter optimization compared to the more classical parameter by parameter approach. Our results indicate that the best image quality is achieved for the fastest emitter blinking (lowest ton and toff), lowest background level, and the highest measurement duration, while the brightness variation does not affect the quality in a statistically significant way within the investigated range. However, when the ranges spanned by the parameters are constrained, a different set of optimal conditions may arise. For example, for second-order SOFI, we identified situations in which the increase of toff can be beneficial to SNR, such as when the measurement duration is long enough. In general, optimal values of ton and toff have been found to be highly dependent from each other and from the measurement duration.

The analysis of reaction systems and their kinetic modeling is important for both exploratory research and process design. Multivariate curve resolution (MCR) methods are state-of-the-art tools for the analysis of spectral series, but are also affected by an unavoidable solution ambiguity that impacts the obtained concentration profiles, spectra and model parameters. These uncertainties depend on the underlying model and the magnitude of the measurement perturbations. We present a general theoretical approach together with a computational method for the analysis of the solution ambiguity underlying arbitrary kinetic models. The main idea is to determine all those model parameters for which the corresponding pure component factorizations satisfy all given constraints within small error tolerances. This makes it possible to determine bands of concentration profiles and spectra that reflect the underlying ambiguity and circumscribes the potential reliability of MCR solutions. False conclusions on the uniqueness of a solution can be prevented. The procedure can be applied as a post-processing step to MCR methods as MCR-ALS, ReactLab or others. The Matlab program code is freely accessible and includes not only the proposed ambiguity analysis but also an MCR hard-modeling approach. Application studies are presented for two experimental data sets, namely for UV/Vis spectra on the relaxation of a photoexcited state of benzophenone and for Raman spectra on an aldehyde formation process.

The aim of this paper is to better understand the functioning of the River Selle (northern France) during dry weather and storm events, to assess the impact of a town on the surface water quality and to suggest qualitative assumptions on the vulnerability of water quality to weather conditions. Two high-frequency monitoring stations covering the Cateau-Cambrésis town were deployed during 4 months in 2016. River flow, water temperature, pH, dissolved oxygen, turbidity, conductivity, total organic carbon, nitrates and phosphates were monitored every 10 min. The water supply of the River Selle is mainly dominated by groundwater as shown by the behaviour of the river flow, the water temperature and the nitrate concentrations in both surface water and groundwater. The reference station located at the upstream of the river (Saint Souplet) exhibits low anthropogenic pressure during dry weather but is significantly impacted during storm events. At the downstream of the Cateau-Cambrésis town, the water quality is severely impacted by phosphates during dry weather mainly due to wastewater inputs into the river. An additional load of pollution is highlighted during storm events. According to our results, the water quality of the River Selle would degrade if actions to reduce dry-weather and storm events pollution sources are not undertaken rapidly. Moreover, nutrients, particularly phosphates, are clearly in excess in this system. Efforts to combat soil leaching and the revision of sewage systems and urban wastewater treatment in the catchment are two key points to tackle. Finally, this study shows the importance of understanding the current behaviour of a given river towards dry weather and storm events before suggesting local scenarios of the impact of climate change on surface water quality.

Fluorescence microscopy is an extremely powerful technique that allows to distinguish multiple labels based on their emission color or other properties, such as their photobleaching and fluorescence recovery kinetics. These kinetics are ideally assumed to be mono-exponential in nature, where the time constants intrinsic to each fluorophore can be used to quantify their presence in the sample. However, these time constants also depend on the specifics of the illumination and sample conditions, meaning that identifying the different contributions in a mixture using a single-channel detection may not be straightforward. In this work, we propose a factor analysis approach called Slicing to identify the different contributions in a multiplexed fluorescence microscopy image exploiting a single measurement channel. With Slicing, a two-way dataset is rearranged into a three-way dataset, which allows the application of a trilinear decomposition model to derive individual profiles for all the model components. We demonstrate this method on bleaching - recovery fluorescence microscopy imaging data of U2OS cells, allowing us to determine the spatial distribution of the dyes and their associated characteristic relaxation traces, without relying on a parametric fitting. By requiring little a priori knowledge and efficiently handling perturbation factors, our method represents a general approach for the recovery of multiple mono-exponential profiles from single-channel microscopy data.

Multivariate curve resolution unmixing of hyperspectral imaging data can be challenging when low sources of variance are present in complex samples, as for minor (low-concentrated) chemical compounds in pharmaceutical formulations. In this work, it was shown how the reduction of hyperspectral imaging data matrices through the selection of essential spectra can be crucial for the analysis of complex unknown pharmaceutical formulation applying Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS). Results were obtained on simulated datasets and on real FT-IR and Raman hyperspectral images of both genuine and falsified tablets. When simulating the presence of minor compounds, different situations were investigated considering the presence of single pixels of pure composition as well as binary and ternary mixtures. The comparison of the results obtained applying MCR-ALS on the reduced data matrices with those obtained on the full matrices revealed unequivocal: more accurate decomposition could be achieved when only essential spectra were analyzed. Indeed, when analyzing the full dataset, MCR-ALS failed resolving minor compounds even though pure spectra were provided as initial estimation, as shown for Raman hyperspectral imaging data obtained on a medicine sample containing 7 chemical compounds. In contrast, when considering the reduced dataset, all minor contributions (down to 1 pixel over 17,956) were successfully unmixed. The same conclusion could be drawn from the results obtained analysing FT-IR hyperspectral imaging data of a falsified medicine.

In recent years, in the context of the application of Multivariate Curve Resolution (MCR) to hyperspectral image analysis, attention has been more and more put onto the possibility of exploiting not only the spectral but also the spatial information for constraining the algorithmic solution. Examples involve the introduction of different spatial constraints during the iterative Alternating Least Squares (ALS) calculation of the MCR solution or the post-processing of the score images using conventional image processing techniques. In this framework, this work proposes an approach for constraining concentration distribution maps within MCR-ALS analysis of hyperspectral images, based on the use of spatio-spectral fuzzy clustering in order to obtain smoother, more contrasted, and better interpretable chemical images. We show the relevance of the proposed approach and investigate the effect of the application of a spectral-spatial fuzzy clustering constraint on samples of different nature.

Reversibly switchable fluorescent proteins (RSFPs) serve as markers in advanced fluorescence imaging. Photoswitching from a non-fluorescent off-state to a fluorescent on-state involves trans-to-cis chromophore isomerization and proton transfer. Whereas excited-state events on the ps timescale have been structurally characterized, conformational changes on slower timescales remain elusive. Here we describe the off-to-on photoswitching mechanism in the RSFP rsEGFP2 by using a combination of time-resolved serial crystallography at an X-ray free-electron laser and ns-resolved pump–probe UV-visible spectroscopy. Ten ns after photoexcitation, the crystal structure features a chromophore that isomerized from trans to cis but the surrounding pocket features conformational differences compared to the final on-state. Spectroscopy identifies the chromophore in this ground-state photo-intermediate as being protonated. Deprotonation then occurs on the μs timescale and correlates with a conformational change of the conserved neighbouring histidine. Together with a previous excited-state study, our data allow establishing a detailed mechanism of off-to-on photoswitching in rsEGFP2., rsEGFP2 is a reversibly photoswitchable fluorescent protein used in super-resolution light microscopy. Here the authors present the structure of an rsEGFP2 ground-state intermediate after excited state-decay that was obtained by nanosecond time-resolved serial femtosecond crystallography at an X-ray free electron laser, and time-resolved absorption spectroscopy measurements complement their structural analysis.

This article highlights the importance of properly taking into account spatial structures and features to better resolve near-infrared (NIR) hyperspectral images by Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS), especially when highly mixed components (in terms of spatial and spectral overlap) underlying the systems under study are dealt with. As in the NIR domain these components can explain both chemical properties and physical phenomena, their improved unravelling can therefore represent an alternative or a complement to more standard approaches for, e.g., spectral data preprocessing. These points will be illustrated through the comprehensive analysis of a complex real-world forensic case-study where texture characterization is crucial for the sake of a more appropriate resolution. ispartof: ANALYTICA CHIMICA ACTA vol:1095 pages:30-37 ispartof: location:Netherlands status: published

In the last decades, spectroscopic techniques have played an increasingly crucial role in analytical chemistry, due to the numerous advantages they offer. Several of these techniques (e.g., Near-InfraRed—NIR—or Fourier Transform InfraRed—FT-IR—spectroscopy) are considered particularly valuable because, by means of suitable equipment, they enable a fast and non-destructive sample characterization. This aspect, together with the possibility of easily developing devices for on- and in-line applications, has recently favored the diffusion of such approaches especially in the context of foodstuff quality control. Nevertheless, the complex nature of the signal yielded by spectroscopy instrumentation (regardless of the spectral range investigated) inevitably calls for the use of multivariate chemometric strategies for its accurate assessment and interpretation. This review aims at providing a comprehensive overview of some of the chemometric tools most commonly exploited for spectroscopy-based foodstuff analysis and authentication. More in detail, three different scenarios will be surveyed here: data exploration, calibration and classification. The main methodologies suited to addressing each one of these different tasks will be outlined and examples illustrating their use will be provided alongside their description.