Multivariate Curve Resolution Slicing of Multiexponential Time-Resolved Spectroscopy Fluorescence Data.

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.

Blind instrument response function identification from fluorescence decays.

Time-resolved fluorescence spectroscopy plays a crucial role when studying dynamic properties of complex photochemical systems. Nevertheless, the analysis of measured time decays and the extraction of exponential lifetimes often requires either the experimental assessment or the modeling of the instrument response function (IRF). However, the intrinsic nature of the IRF in the measurement process, which may vary across measurements due to chemical and instrumental factors, jeopardizes the results obtained by reconvolution approaches. In this paper, we introduce a novel methodology, called blind instrument response function identification (BIRFI), which enables the direct estimation of the IRF from the collected data. It capitalizes on the properties of single exponential signals to transform a deconvolution problem into a well-posed system identification problem. To delve into the specifics, we provide a step-by-step description of the BIRFI method and a protocol for its application to fluorescence decays. The performance of BIRFI is evaluated using simulated and time-correlated single-photon counting data. Our results demonstrate that the BIRFI methodology allows an accurate recovery of the IRF, yielding comparable or even superior results compared with those obtained with experimental IRFs when they are used for reconvolution by parametric model fitting.

Kernelizing: A way to increase accuracy in trilinear decomposition analysis of multiexponential signals.

The unmixing of multiexponential decay signals into monoexponential components using soft modelling approaches is a challenging task due to the strong correlation and complete window overlap of the profiles. To solve this problem, slicing methodologies, such as PowerSlicing, tensorize the original data matrix into a three-way data array that can be decomposed based on trilinear models providing unique solutions. Satisfactory results have been reported for different types of data, e.g., nuclear magnetic resonance or time-resolved fluorescence spectra. However, when decay signals are described by only a few sampling (time) points, a significant degradation of the results can be observed in terms of accuracy and precision of the recovered profiles. In this work, we propose a methodology called Kernelizing that provides a more efficient way to tensorize data matrices of multiexponential decays. Kernelizing relies on the invariance of exponential decays, i.e., when convolving a monoexponential decaying function with any positive function of finite width (hereafter called "kernel"), the shape of the decay (determined by the characteristic decay constant) remains unchanged and only the preexponential factor varies. The way preexponential factors are affected across the sample and time modes is linear, and it only depends on the kernel used. Thus, using kernels of different shapes, a set of convolved curves can be obtained for every sample, and a three-way data array generated, for which the modes are sample, time and kernelizing effect. This three-way array can be afterwards analyzed by a trilinear decomposition method, such as PARAFAC-ALS, to resolve the underlying monoexponential profiles. To validate this new approach and assess its performance, we applied Kernelizing to simulated datasets, real time-resolved fluorescence spectra collected on mixtures of fluorophores and fluorescence-lifetime imaging microscopy data. When the measured multiexponential decays feature few sampling points (down to fifteen), more accurate trilinear model estimates are obtained than when using slicing methodologies.

Comparison of two-dimensional fast Raman imaging versus point-by-point acquisition mode for human bone characterization.

Recent technical developments gave rise to a new technology for two-dimensional fast Raman imaging: the DuoScan averaging mode (DS-Avg). This technology allows the acquisition of a Raman spectrum over a rastered macro spot. The aim of this study was to evaluate the interest of the DS-Avg applied on trabecular human bone. The evaluation was based on the comparison of the DS-Avg versus the point-by-point mapping mode in real usage conditions. The signal-to-noise ratio, the spectral difference, and the physicochemical parameters were estimated for comparison of the efficiency of both modes. Principal component analysis was performed to explore the capacity of both modes to detect compositional variations. Results showed that the DS-Avg spectrum was equivalent to the average spectrum of individual spectra acquired with the point-by-point mode for the same sample area. The physicochemical parameters can be also determined from DS-Avg acquisition. The DS-Avg combined with an objective ×50 allows a drastic decrease of the acquisition time, but the information about the micrometric composition is lost. The combination of the DS-Avg with an objective ×100 is a good compromise between acquisition time and resolution. The DS-Avg is a useful technology for imaging mineral and organic phases of bones and for assessing their spatial distribution on large samples. The point-by-point imaging mode is more appropriate to assess the heterogeneous composition of bone within the micrometer scale. For the first time, this study compares the DuoScan averaging mode to the point-by-point imaging mode on a trabecular human bone.

Multilinear Slicing for curve resolution of fluorescence imaging with sequential illumination.

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.

Hierarchical classification and matching of mid-infrared spectra of paint samples for forensic applications.

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.

Multivariate statistical process control (MSPC) using Raman spectroscopy for in-line culture cell monitoring considering time-varying batches synchronized with correlation optimized warping (COW).

Multivariate statistical process control (MSPC) is increasingly popular as the challenge provided by large multivariate datasets from analytical instruments such as Raman spectroscopy for the monitoring of complex cell cultures in the biopharmaceutical industry. However, Raman spectroscopy for in-line monitoring often produces unsynchronized data sets, resulting in time-varying batches. Moreover, unsynchronized data sets are common for cell culture monitoring because spectroscopic measurements are generally recorded in an alternate way, with more than one optical probe parallelly connecting to the same spectrometer. Synchronized batches are prerequisite for the application of multivariate analysis such as multi-way principal component analysis (MPCA) for the MSPC monitoring. Correlation optimized warping (COW) is a popular method for data alignment with satisfactory performance; however, it has never been applied to synchronize acquisition time of spectroscopic datasets in MSPC application before. In this paper we propose, for the first time, to use the method of COW to synchronize batches with varying durations analyzed with Raman spectroscopy. In a second step, we developed MPCA models at different time intervals based on the normal operation condition (NOC) batches synchronized by COW. New batches are finally projected considering the corresponding MPCA model. We monitored the evolution of the batches using two multivariate control charts based on Hotelling's T2 and Q. As illustrated with results, the MSPC model was able to identify abnormal operation condition including contaminated batches which is of prime importance in cell culture monitoring We proved that Raman-based MSPC monitoring can be used to diagnose batches deviating from the normal condition, with higher efficacy than traditional diagnosis, which would save time and money in the biopharmaceutical industry.

Correction: New Insights on the Composition and the Structure of the Acellular Extrinsic Fiber Cementum by Raman Analysis.

[This corrects the article DOI: 10.1371/journal.pone.0167316.].

New Insights on the Composition and the Structure of the Acellular Extrinsic Fiber Cementum by Raman Analysis.

Acellular extrinsic fiber cementum is a mineralized tissue that covers the cervical half of the tooth root surface. It contains mainly extrinsic or Sharpey's fibers that run perpendicular to the root surface to anchor the tooth via the periodontal ligament. Acellular cementum is continuously and slowly produced throughout life and exhibits an alternating bright and dark pattern under light microscopy. However, although a better understanding of the structural background of acellular cementum is relevant to many fields, such as cementochronology, periodontology and tissue engineering, acellular cementum remains rarely studied and poorly understood. In this work, we studied the acellular cementum at the incremental line scale of five human mandibular canines using polarized Raman spectroscopy. We provided Raman imaging analysis and polarized acquisitions as a function of the angular orientation of the sample. The results showed that mineral crystals were always parallel to collagen fibrils, and at a larger scale, we proposed an organizational model in which we found radial collagen fibers, "orthogonal" to the cementum surface, and "non-orthogonal" fibers, which consist of branching and bending radial fibers. Concerning the alternating pattern, we observed that the dark lines corresponded to smaller, more mineralized and probably more organized bands, which is consistent with the zoological assumption that incremental lines are produced during a winter rest period of acellular cementum growth.

Sparse deconvolution of high-density super-resolution images.

In wide-field super-resolution microscopy, investigating the nanoscale structure of cellular processes, and resolving fast dynamics and morphological changes in cells requires algorithms capable of working with a high-density of emissive fluorophores. Current deconvolution algorithms estimate fluorophore density by using representations of the signal that promote sparsity of the super-resolution images via an L1-norm penalty. This penalty imposes a restriction on the sum of absolute values of the estimates of emitter brightness. By implementing an L0-norm penalty--on the number of fluorophores rather than on their overall brightness--we present a penalized regression approach that can work at high-density and allows fast super-resolution imaging. We validated our approach on simulated images with densities up to 15 emitters per µm(-2) and investigated total internal reflection fluorescence (TIRF) data of mitochondria in a HEK293-T cell labeled with DAKAP-Dronpa. We demonstrated super-resolution imaging of the dynamics with a resolution down to 55 nm and a 0.5 s time sampling.

In-line and real-time prediction of recombinant antibody titer by in situ Raman spectroscopy.

The Food and Drug Administration's (FDA) process analytical technology (PAT) framework has been initiated to encourage drug manufacturers to develop innovative techniques in order to better understand their processes and institute high level quality control which allows action at any point in the manufacturing process. While Raman spectroscopy and chemometrics have been successfully used to predict concentration of conventional metabolites in cell cultures, it is really not the case for active substances. Thus, we propose, for the first time, an in-line and real-time prediction of recombinant antibody titer using an immersion probe link to a spectrometer without the tacking of samples. A good robustness of the method is observed on different culture batches and the contamination risk is drastically reduced which is an important issue in biotechnology manufacturing processes.

Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils.

Classification is an important task in chemometrics. For several years now, support vector machines (SVMs) have proven to be powerful for infrared spectral data classification. However such methods require optimisation of parameters in order to control the risk of overfitting and the complexity of the boundary. Furthermore, it is established that the prediction ability of classification models can be improved using pre-processing in order to remove unwanted variance in the spectra. In this paper we propose a new methodology based on genetic algorithm (GA) for the simultaneous optimisation of SVM parameters and pre-processing (GENOPT-SVM). The method has been tested for the discrimination of the geographical origin of Italian olive oil (Ligurian and non-Ligurian) on the basis of near infrared (NIR) or mid infrared (FTIR) spectra. Different classification models (PLS-DA, SVM with mean centre data, GENOPT-SVM) have been tested and statistically compared using McNemar's statistical test. For the two datasets, SVM with optimised pre-processing give models with higher accuracy than the one obtained with PLS-DA on pre-processed data. In the case of the NIR dataset, most of this accuracy improvement (86.3% compared with 82.8% for PLS-DA) occurred using only a single pre-processing step. For the FTIR dataset, three optimised pre-processing steps are required to obtain SVM model with significant accuracy improvement (82.2%) compared to the one obtained with PLS-DA (78.6%). Furthermore, this study demonstrates that even SVM models have to be developed on the basis of well-corrected spectral data in order to obtain higher classification rates.

Mixture models for two-dimensional baseline correction, applied to artifact elimination in time-resolved spectroscopy.

Baseline correction and artifact removal are important pre-processing steps in analytical chemistry. We propose a correction algorithm using a mixture model in combination with penalized regression. The model is an extension of a method recently introduced for baseline estimation in the case of one-dimensional data. The data are modeled as a smooth surface using tensor product P-splines. The weights of the P-splines regression model are computed from a mixture model where a datapoint is either allocated to the noise around the baseline, or to the artifact component. The method is broadly applicable for anisotropic smoothing of two-way data such as two-dimensional gel electrophoresis and two-dimensional chromatography data. We focus here on the application of the approach in femtosecond time-resolved spectroscopy, to eliminate strong artifact signals from the solvent.

Multivariate curve resolution--alternating least squares applied to the investigation of ultrafast competitive photoreactions.

The main advantage of multivariate curve resolution - alternating least squares method (MCR-ALS) is the possibility to act as multiset analysis method, combining data coming from different experiments to provide a complete and more accurate description of a chemical system. Exploiting the multiset side, the combination of experiments obtained from two photo-active systems with complementary pathways and monitored by femtosecond UV-vis transient absorption spectroscopy is presented in this work. A multiset hard- and soft-multivariate curve resolution model (HS-MCR) was built allowing the description of the spectrokinetic features of the entire system. Additionally, reaction quantum yields were incorporated in the hard-model in order to describe branching ratios for intermediate species. The photodynamics of salicylidene aniline (SA) was investigated as a case study. The overall reaction scheme involves two competitive and parallel pathways. On the one hand, a photoinduced excited state intramolecular proton transfer (ESIPT) followed by a cis-trans isomerization leads to the so-called photochromic form of the molecule, which absorbs in the visible. The formation of the photochromic species is well characterized in the literature. On the other hand, a complex internal rotation of the molecule takes place, which is a competing reaction. The rotation mechanism is based on a trans-cis isomerization. This work aimed at providing a detailed spectrokinetic characterization of both reaction pathways for SA. For this purpose, the photodynamics of two molecules of identical parent structures and different substituent patterns were investigated in femtosecond transient absorption spectroscopy. For SA, the mechanism described above involving the two parallel pathways was observed, whereas for the derivative form of SA, the photochromic reaction was blocked because of the replacement of an H atom by a methyl group. The application of MCR approaches enabled to obtain transient spectra for the different intermediate species involved and rate constants for the photochromic reaction, thus contributing to a comprehensive description of the photodynamics of SA.

Baseline correction methods to deal with artifacts in femtosecond transient absorption spectroscopy.

In femtosecond transient absorption spectroscopy, artifact contributions are usually observed at ultra-short time scale. These complex signals are very challenging because of their nature, related to ultrafast phenomena, and because they strongly distort the structure of the spectrokinetic data. The purpose of this work is to evaluate the potential of baseline correction methods for femtosecond transient absorption spectroscopy data pre-processing. Indeed, artifacts removal should ideally be performed before multivariate data analysis. The work is thus mainly focused on two different approaches which are filtering by discrete wavelet transform, on the one hand, and smoothing by asymmetric least squares, on the other hand. The results obtained both on simulated data and on femtosecond pump-probe spectroscopy data are discussed. It can be concluded that asymmetric least squares smoothing procedure turns out to perform satisfactory for artifacts removal. Indeed, only mild discrepancies are observed in the transient spectra and, most important, good recovery of the kinetics is obtained at ultra-short time scale.

Characterisation of heavy oils using near-infrared spectroscopy: optimisation of pre-processing methods and variable selection.

In this study, chemometric predictive models were developed from near infrared (NIR) spectra for the quantitative determination of saturates, aromatics, resins and asphaltens (SARA) in heavy petroleum products. Model optimisation was based on adequate pre-processing and/or variable selection. In addition to classical methods, the potential of a genetic algorithm (GA) optimisation, which allows the co-optimisation of pre-processing methods and variable selection, was evaluated. The prediction results obtained with the different models were compared and decision regarding their statistical significance was taken applying a randomization t-test. Finally, the results obtained for the root mean square errors of prediction (and the corresponding concentration range) expressed in %(w/w), are 1.51 (14.1-99.1) for saturates, 1.59 (0.7-61.1) for aromatics, 0.77 (0-34.5) for resins and 1.26 (0-14.7) for asphaltens. In addition, the usefulness of the proposed optimisation method for global interpretation is shown, in accordance with the known chemical composition of SARA fractions.

Correction of inner filter effect in mirror coating cells for trace level fluorescence measurements.

The sensitivity of the spectrofluorometric technique can be improved by a factor of about 3.6 using a mirror coating cell. In the case of a large working range, the nonlinear relationship due to the absorbance of solutions between concentration of the analyte of interest and fluorescence intensity (called inner filter effect) must be corrected. This paper suggests a universal inner filter correction equation based on the physical absorbance phenomenon of a mirror coating cell that only depends on the solution absorbance spectra and the cell parameters. These parameters are determined with rhodamine b standard solutions and a simplex method-based mathematical fitting. The methodology has been successfully applied to the correction of classical and synchronous spectra in absorbent media. The partial least squares (PLS) quantification of a mixture of trace levels (approximately 1-10 microg L(-1)) of six polycyclic aromatic hydrocarbons (PAHs) by synchronous fluorescence is possible, even in an absorbent matrix. This simple method allows extension of the analytical field of fluorescence quantification to a large working range in absorbent solutions.

Use of a Plackett-Burman design with multivariate calibration for the analysis of polycyclic aromatic hydrocarbons in micellar media by synchronous fluorescence.

This work describes the use of a multilevel Plackett-Burman design (PB) for the creation of a calibration set for partial least square regression (PLS). The PB calibration set was compared to a collinear analogue by testing these two PLS models for the analysis of six polycyclic aromatic hydrocarbons (PAHs). These compounds were analyzed in micellar media by synchronous fluorescence after determination of the experimental conditions (choice of surfactant, analytical conditions such as deltalambda, step, and scan range). The external validation shows that the collinear set is inappropriate to quantify PAH in real samples, but the PB calibration set affords optimal results.