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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., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

We propose a multivariate curve resolution approach for the investigation of photochromic systems using UV-Visible spectroscopy. The incorporation of photochemical hard-models as constraints in multivariate curve resolution alternating least squares (MCR-ALS) allows extracting reaction quantum yields in situations where a complete knowledge of the system is not available. We apply this approach to the study of the photochromism of CMTE (cis-1,2-dicyano-1,2-bis(2,4,5-trimethyl-3-thienyl)ethene) under continuous monochromatic irradiation. The mechanism, involving 3 species and 2 reversible reactions, is written and translated into a kinetic constraint that can be applied to the concentration profiles within ALS. First, ambiguity of the solution obtained for photochemical model(s) is calculated and discussed for single set analysis. Multiset analysis is then proposed combining data obtained under different irradiation wavelengths to provide more reliable results. Finally, the photochemical reactivity of CMTE is widely unraveled, and some description of the mechanism observed under irradiation at 365 nm is given., (Copyright © 2018 Elsevier B.V. All rights reserved.)

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 T 2 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., (Copyright © 2016 Elsevier B.V. All rights reserved.)

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., (Copyright © 2015 Elsevier B.V. All rights reserved.)

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., (Copyright © 2013 Elsevier B.V. All rights reserved.)

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., (Copyright © 2013 Elsevier B.V. All rights reserved.)

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., (Copyright © 2011 Elsevier B.V. All rights reserved.)

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., (Copyright © 2011 Elsevier B.V. All rights reserved.)

In this work, different approaches for variable selection are studied in the context of near-infrared (NIR) multivariate calibration of textile. First, a model-based regression method is proposed. It consists in genetic algorithm optimisation combined with partial least squares regression (GA-PLS). The second approach is a relevance measure of spectral variables based on mutual information (MI), which can be performed independently of any given regression model. As MI makes no assumption on the relationship between X and Y, non-linear methods such as feed-forward artificial neural network (ANN) are thus encouraged for modelling in a prediction context (MI-ANN). GA-PLS and MI-ANN models are developed for NIR quantitative prediction of cotton content in cotton-viscose textile samples. The results are compared to full-spectrum (480 variables) PLS model (FS-PLS). The model requires 11 latent variables and yielded a 3.74% RMS prediction error in the range 0-100%. GA-PLS provides more robust model based on 120 variables and slightly enhanced prediction performance (3.44% RMS error). Considering MI variable selection procedure, great improvement can be obtained as 12 variables only are retained. On the basis of these variables, a 12 inputs ANN model is trained and the corresponding prediction error is 3.43% RMS error.