Jean-Pierre Huvenne

Investigation of the glycosidic linkages in several oligosaccharides using FT-IR and FT Raman spectroscopies

FT-IR and FT Raman spectra of eight oligosaccharides have been recorded in the crystalline state. The FT-Raman measurements have been done in the 1500-100 cm−1 range and the FT-IR ones between 1500 and 600 cm−1. Five of these oligosaccharides are oligomers of amylose and four are oligomers of cellulose. The two series present the same monosaccharide composition which is glucose the difference between them being that maltose (the disaccharide analog of amylose) and cellobiose (the disaccharide analog of cellulose) present the different configurations of the glycosidic linkages, α 1–4 and β 1–4 respectively. Features were revealed that appear to be characteristic of details of the stereochemistry and bands associated with the glycosidic linkages were studied.

BOVINE ELASTIN AND KAPPA -ELASTIN SECONDARY STRUCTURE DETERMINATION BY OPTICAL SPECTROSCOPIES

Elastin is the macromolecular polymer of tropoelastin molecules responsible for the elastic properties of tissues. The understanding of its specific elasticity is uncertain because its structure is still unknown. Here, we report the first experimental quantitative determination of bovine elastin secondary structures as well as those of its corresponding soluble κ-elastin. Using circular dichroism and Fourier transform infrared and near infrared Fourier transform Raman spectroscopic data, we estimated the secondary structure contents of elastin to be ∼10% α-helices, ∼45% β-sheets, and ∼45% undefined conformations. These values were very close to those we had previously determined for the free monomeric tropoelastin molecule, suggesting thus that elastin would be constituted of a closely packed assembly of globular β structural class tropoelastin molecules cross-linked to form the elastic network (liquid drop model of elastin architecture). The presence of a strong hydration shell is demonstrated for elastin, and its possible contribution to elasticity is discussed.

Comparison of supervised pattern recognition methods with McNemar’s statistical test: Application to qualitative analysis of sugar beet by near-infrared spectroscopy

Abstract The application of supervised pattern recognition methodology is becoming important within chemistry. The aim of the study is to compare classification method accuracies by the use of a McNemar’s statistical test. Three qualitative parameters of sugar beet are studied: disease resistance (DR), geographical origins and crop periods. Samples are analyzed by near-infrared spectroscopy (NIRS) and by wet chemical analysis (WCA). Firstly, the performances of eight well-known classification methods on NIRS data are compared: Linear Discriminant Analysis (LDA), K -Nearest Neighbors (KNN) method, Soft Independent Modeling of Class Analogy (SIMCA), Discriminant Partial Least Squares (DPLS), Procrustes Discriminant Analysis (PDA), Classification And Regression Tree (CART), Probabilistic Neural Network (PNN) and Learning Vector Quantization (LVQ) neural network are computed. Among the three data sets, SIMCA, DPLS and PDA have the highest classification accuracies. LDA and KNN are not significantly different. The non-linear neural methods give the less accurate results. The three most accurate methods are linear, non-parametric and based on modeling methods. Secondly, we want to emphasize the power of near-infrared reflectance data for sample discrimination. McNemar’s tests compare classification developed with WCA or with NIRS data. For two of the three data sets, the classification results are significantly improved by the use of NIRS data.

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.

On-Line Monitoring of a Latex Emulsion Polymerization by Fiber-Optic FT-Raman Spectroscopy. Part I: Calibration:

The on-line monitoring of a styrene/butadiene latex emulsion polymerization was completed. Our aim was to determine in real time the dry extract percentage or the amount of styrene monomer in the reactor. We constructed two partial least-squares (PLS) calibration models with mean-centered spectra over the 3500–2700 U 1800–500 cm−1 spectral range. Four factors were required to model the evolution of the dry extract, and a two-factors model calibrated the amount of free styrene. The results were very satisfactory: the dry extract was predicted with a root mean squared error of prediction (RMSEP) lower than 1% over the 2–50% dry extract range, and for the amount of styrene monomer, the RMSEP was lower than 5000 ppm over the 0–70 000 ppm range. The nonlinear effects of temperature and water reabsorption were considered, and they did not prevent PLS from giving good-quality results.

Direct structural identification of polysaccharides from red algae by FTIR microspectrometry I: Localization of agar inGracilaria verrucosa sections

Unlike carrageenans, agars have not been studied very extensively by infrared spectroscopy, in so far as the structures of this kind of polygalactanes are not as well defined as carrageenans. However, in a previous work we have carried out a vibrational analysis of both carrageenans and agars and some important assignments of the main absorptions have been made. Consequently, the present work has been undertaken in order to identify agars without any extraction directly in various seaweeds using the infrared microspectrometry method. The main advantage of this method is that the sample consists only of a dehydrated algal section. The red algaeGradlaria verrucosa has been the subject of the present study. In the first place, spectra of extracted agars were recorded, as they can help us to confirm the nature of the compound identified by this technique. In a second stage, spectra of different parts of the sections have been carried out. The comparison between the resulting spectra with those of the extracted polysaccharides, has demonstrated, firstly that the best results are obtained from the cortical area, because, as expected, the agar is mainly located in the cell wall of this area of the algae. Indeed, the feature bands of agars are all observed, especially the intense ones between 1000 and 1100 cm−1 and the more characteristic absorptions in the wavenumbers range below 1000 cm−1 so as the ones at 988, 965, 930, 890, 870, 771 and 741 cm−1. Secondly, it may be also identified in smaller amounts in the medullar area, the cells are greater than in the cortical area and the cytoplasm is preponderent. However, in the latter case a coexisting polysaccharide, present in a considerable quantity and called floridean starch (Its structure is not very well known, as it varies from one algae to another), masks the spectra of agar, as its spectrum is very similar to those of polygalactanes.

Sucrose content determination of sugar beets by near infrared reflectance spectroscopy. Comparison of calibration methods and calibration transfer

The legal method (polarimetric measurement) for the determination of sucrose content since 1964 uses lead acetate. Because heavy metals are polluting, a law could forbid their use in the near future. Near infrared (NIR) spectroscopy is a suitable alternative method to replace it. For two years, 2412 samples of beet brei were analysed by NIR spectroscopy. In this article, spectral pre-processing and regression methods were compared in order to obtain an accurate prediction of sugar content. Analyse of variance and Fishers tests were calculated to compare models (bias and Standard Error of Prediction corrected for bias) in terms of statistical significance. The model developed with spectra pre-treated by standard normal variate and second derivative gave the best results. The standard error of prediction of the ratio sucrose content/fresh beet weight was low (0.11 g / 100 g). The second part of this study shows that updating of the spectral database was possible. This makes it possible to take into account new variabilities of beet. For an industrial application, calibration transfer has to be studied. Sugar beet spectra or generic standard spectra were used on two NIR instruments. A simple linear regression wavelength by wavelength gives good results for the standardisation, demonstrating a possible use of the model on different instruments. In conclusion, the replacement of the polarimetric method by NIR spectroscopy was feasible.

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.

Multivariate curve resolution applied to Fourier transform infrared spectra of macromolecules: structural characterisation of the acid form and the salt form of humic acids in interaction with lead

The significance of evolving mixtures structural spectroscopic studies might appear limited when the experimental spectra do not present a sufficient quality for a precise interpretation. It is the case when the chemical behaviour of macromolecules is studied on the basis of infrared spectra. If the effective resolution is low, the spectral profiles appear similar despite the applied chemical conditions change. This makes impossible the interpretation of the raw spectra and mathematical treatments are required to separate the different contributions that overlap. To determine the behaviour of the reactive sites of humic acids in the binding with heavy metals, infrared spectra are recorded under various chemical conditions. The cation to be considered is Pb2+ and the two chemical variables to be studied are pH and initial lead concentration. Four series of FTIR spectra are recorded, but no visible difference can be directly assigned to the different chemical states of the macromolecules. Multivariate self-modelling curve resolution is thus proposed as a tool for resolving these complex and strong overlapping datasets. First, initial estimates are obtained from pure variable detection methods: it comes out that two spectra are enough to reconstruct the experimental matrices. In a further step, the application of the multivariate curve resolution-alternating least squares (MCR-ALS) algorithm with additional constraints on each individual dataset, as well as on column-wise augmented matrices, allows to optimise the profiles and spectra that appear to be highly characterising the acid and the salt form of the molecule. Moreover, the concentrations profiles associated to these two limit spectral forms allow interpreting the analytical measurements made during the reactions between humic acids and H+ or Pb2+. Consequently, depending on the initial state of the humic acid, two distinct reactional mechanisms are proposed.