Cyril Ruckebusch

Multivariate curve resolution: A review of advanced and tailored applications and challenges

Multivariate curve resolution (MCR) is a widespread methodology for the analysis of process data in many different application fields. This article intends to propose a critical review of the recently published works. Particular attention will be paid to situations requiring advanced and tailored applications of multivariate curve resolution, dealing with improvements in preprocessing methods, multi-set data arrangements, tailored constraints, issues related to non-ideal noise structure and deviation to linearity. These analytical issues are tackling the limits of applicability of MCR methods and, therefore, they can be considered as the most challenging ones.

Investigation of ultrafast photoinduced processes for salicylidene aniline in solution and gas phase: toward a general photo-dynamical scheme

Photodynamics of 2-hydroxybenzylideneaniline (photochromic salicylidene aniline SAOH) and N-(2-methoxybenzylidene)aniline (SAOMe) are studied by steady state and transient optical spectroscopy in solution and gas phase at different excitation wavelengths (266, 355 and 390 nm). Two competitive processes are observed from the enol* excited state: on one hand a rotation to get a twisted-enol, and on the other hand an excited state intramolecular proton transfer (ESIPT) followed by a cis-trans isomerisation to get the trans-keto photochromic product. For the first time both processes are characterized at an ultrashort time scale for salicylidene aniline. Resolution of the spectrokinetic data is achieved by multivariate curve resolution and attribution of the intermediate species recovered is performed in comparison with the results obtained for SAOMe, which can only undergo enol rotational isomerisation. It shows that ESIPT and rotation to the twisted-enol for SAOH occur within 100 fs, as predicted by recent quantum dynamical simulations, with an efficiency ratio dependent on the excitation wavelength. Therefore a general photoinduced mechanism for salicylidene aniline is drawn.

Multivariate curve resolution methods in imaging spectroscopy: influence of extraction methods and instrumental perturbations.

Imaging spectroscopy is becoming a key field of analytical chemistry. In the face of more and more complex samples, we actually need accurate microscopic insight. Nowadays, the methods used to produce concentration maps of the pure compounds from spectral data sets are based on the classical univariate approach although multivariate approaches are sometimes investigated. But in any case, the analytical quality of the chemical images thus provided cannot be discussed since no reference methods are at our disposal. Thus the proposed research focuses on the application of multivariate methods such as Orthogonal Projection Approach (OPA), SIMPLE-to-use Self-modeling Mixture Analysis (SIMPLISMA), Multivariate Curve Resolution - Alterning Least Squares (MCR-ALS), and Positive Matrix Factorization (PMF) for imaging spectroscopy. A systematic and quantitative characterization of the accuracy of spectra and images extraction is investigated on mid-infrared spectral data sets. Of special interest is the influence of instrumental perturbations such as noise and spectral shift on the extraction ability to access the algorithms robustness.

Identification of Modified Starches Using Infrared Spectroscopy and Artificial Neural Network Processing

The authentication of food is a very important issue both for the consumers and for the food industry with respect to all levels of the food chain from raw materials to finished products. Corn starch can be used in a wide variety of food preparation as bakery cream fillings, sauce, or dry mixes. There are many modifications of the corn starch in connection with its use in the agrofood industry. This paper describes a novel approach to the classification of modified starches and the recognition of their modifications by artificial neural network (ANN) processing of attenuated total reflection Fourier transform spectroscopy (ATR/FT-IR) spectra. Using the self-organizing artificial neural network of the Kohonen type, we can obtain natural groupings of similarly modified samples on a two-dimensional plane. Such mapping provides the expert with the possibility of analyzing the distribution of samples and predicting modifications of unknown samples by using their relative position with respect to existing clusters. On the basis of the available information in the infrared spectra, a feedforward artificial neural network, trained with the intensities of the derivative infrared spectra as input and the starch modifications as output, allows the user to identify modified starches presented as prediction samples.

Statistical tests for comparison of quantitative and qualitative models developed with near infrared spectral data

Near-infrared spectroscopy (NIRS) has been applied for both qualitative and quantitative evaluation of sugar beet. However, chemometrics methods are numerous and a choice criterion is sometime difficult to determine. In order to select the most accurate chemometrics method, statistical tests are developed. In the first part, quantitative models, which predict sucrose content of sugar beet, are compared. To realize a systematic study, 54 models are developed with different spectral pre-treatments (Standard Normal Variate (SNV), Detrending (D), first and second Derivative), different spectral ranges and different regression methods (Principal Component Regression (PCR), Partial Least Squares (PLS), Modified PLS (MPLS)). Analyze of variance and Fishers tests are computed to compare respectively bias and Standard Error of Prediction Corrected for bias (SEP(C)). The model developed with full spectra pre-treated by SNV, second derivative and MPLS methods gives accurate results: bias is 0.008 and SEP(C) is 0.097 g of sucrose per 100 g of sample on a concentration range between 14 and 21 g/100 g. In the second part, McNemars test is applied to compare the classification methods. The classifications are used with two data sets: the first data set concerns the disease resistance of sugar beet and the second deals with spectral differences between four spectrometers. The performances of four well-known classification methods are compared on the NIRS data: Linear Discriminant Analysis (LDA), K Nearest Neighbors method (KNN), Simple Modeling of Class Analogy (SIMCA) and Learning Vector Quantization neural network (LVQ) are computed. In this study, the most accurate method (SIMCA) has a prediction rate of 81.9% of good classification on the disease resistance determination and has 99.4% of good classification on the instrument data set.

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.

Hybrid hard- and soft-modelling applied to analyze ultrafast processes by femtosecond transient absorption spectroscopy: Study of the photochromism of salicylidene anilines

Multivariate curve resolution-alternating least squares (MCR-ALS) of multi-experiment data analysis was successfully applied to elucidate the photodynamics of the N-(3-methylsalicylidene)-3-methylaniline by analyzing UV-vis femtosecond transient absorption spectra. The two-way data obtained present some specific difficulties linked to the nature of the transient spectra collected and to the overlapping of the photodynamics of the solvent and other contributions at short time scale (below 1 ps). Advantage was taken from the flexibility of the hybrid hard-soft multivariate curve resolution (HS-MCR) approach to consider a non-absorbing contribution in the kinetic model and to provide a functional description of the solvent in soft-modelling. The results obtained confirm the existence of an intermediate excited state in the process, which is created just after the ESIPT. It was observed that this intermediate relaxes in a few hundreds of femtosecond to the S(1) fluorescent cis-keto excited state and a decay time constant of 219 fs was found. These results confirm other femtosecond time-resolved fluorescence studies on salicylidene aniline molecules. Previous hypothesis on the formation of the trans-keto photoproduct from the S(1) fluorescent cis-keto state (time constant 14 ps) is also confirmed.

Deciphering the protonation and tautomeric equilibria of firefly oxyluciferin by molecular engineering and multivariate curve resolution

The mysterious flashes of light communicated by fireflies conceal a rich and exciting solution spectrochemistry that revolves around the chemiexcitation and photodecay of the fluorophore, oxyluciferin. A triple chemical equilibrium by double deprotonation and keto–enol tautomerism turns this simple molecule into an intricate case where the relative spectral contributions of six chemical species combine over a physiologically relevant pH range, rendering physical isolation and spectral characterization of most of the species unmanageable. To disentangle the individual spectral contributors, here we demonstrate the advantage of chemical oriented multivariate data analysis. We designed a set of specific oxyluciferin derivatives and applied a multivariate curve resolution-alternating least squares (MCR-ALS) procedure simultaneously to an extensive set of pH-dependent spectroscopic data for oxyluciferin and the target derivatives. The analysis provided, for the first time, the spectra of the pure individual components free of contributions from the other forms, their pH-dependent profiles and distributions, and the most accurate to date values for the three equilibrium constants.

Monitoring and interpretation of photoinduced biochemical processes by rapid-scan FTIR difference spectroscopy and hybrid hard and soft modeling.

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

Quantitative analysis of cotton-polyester textile blends from near-infrared spectra.

Quantitative analysis of textile blends and textile fabrics is currently of particular interest in the industrial context. In this frame, this work investigates whether the use of Fourier transform (FT) near-infrared (NIR) spectroscopy and chemometrics is powerful for rapid and accurate quantitative analysis of cotton–polyester content in blend products. As samples of the same composition have many sources of variability that affect NIR spectra, indirect prediction is particularly challenging and a large sample population is required to design robust calibration models. Thus, a total of more than three-hundred cotton–polyester samples were selected covering the range from the 0% to 100% cotton and the corresponding NIR reflectance spectra were measured on raw fabrics. The data set obtained was used to develop multivariate models for quantitative prediction from reference measurements. A successful approach was found to rely on partial least squares (PLS) regression combined with genetic algorithms (GAs) for wavelength selection. It involved evaluating a set of calibration models considering different spectral regions. The results obtained considering 27.5% of the original variables yielded a prediction error (RMSEP) of 2.3 in percent cotton content. It demonstrates that FT-NIR spectroscopy has the potential to be used in the textile industry for the prediction of the composition of cotton– polyester blends. As a further consequence, it was observed that the spectral preprocessing and the complexity of the model are simplified compared to the full-spectrum approach. Also, the relevancy of the spectral intervals retained after variable selection can be discussed.