Studying radiolytic ageing of nuclear power plant electric cables with FTIR spectroscopy.

Due to the willingness to extend the nuclear power plants length of life, it is of prime importance to understand long term ageing effect on all constitutive materials. For this purpose gamma-irradiation effects on insulation of instrumentation and control cables are studied. Mid-infrared spectroscopy and principal components analysis (PCA) were used to highlight molecular modifications induced by gamma-irradiation under oxidizing conditions. In order to be closer to real world conditions, a low dose rate of 11Gyh-1 was used to irradiate insulations in full cable or alone with a dose up to 58 kGy. Spectral differences according to irradiation dose were extracted using PCA. It was then possible to observe different behaviors of the insulation constitutive compounds i.e. ethylene vinyl acetate (EVA), ethylene propylene diene monomer (EPDM) and aluminium trihydrate (ATH). Irradiation of insulations led to the oxidation of their constitutive polymers and a modification of filler-polymer ratio. Moreover all these modifications were observed for insulations alone or in full cable indicating that oxygen easily diffuses into the material. Spectral contributions were discussed considering different degradation mechanisms.

Water quality assessment of a small peri-urban river using low and high frequency monitoring.

The biogeochemical behaviors of small rivers that pass through suburban areas are difficult to understand because of the multi-origin inputs that can modify their behavior. In this context, a monitoring strategy has been designed for the Marque River, located in Lille Metropolitan area of northern France, that includes both low-frequency monitoring over a one-year period (monthly sampling) and high frequency monitoring (measurements every 10 minutes) in spring and summer. Several environmental and chemical parameters are evaluated including rainfall events, river flow, temperature, dissolved oxygen, turbidity, conductivity, nutritive salts and dissolved organic matter. Our results from the Marque River show that (i) it is impacted by both urban and agricultural inputs, and as a consequence, the concentrations of phosphate and inorganic nitrogen have degraded the water quality; (ii) the classic photosynthesis/respiration processes are disrupted by the inputs of organic matter and nutritive salts; (iii) during dry periods, the urban sewage inputs (treated or not) are more important during the day, as indicated by higher river flows and maximal concentrations of ammonium; (iv) phosphate concentrations depend on oxygen contents in the river; (v) high nutrient concentrations result in eutrophication of the Marque River with lower pH and oxygen concentrations in summer. During rainfalls, additional inputs of ammonium, biodegradable organic matter as well as sediment resuspension result in anoxic events; and finally (vi) concentrations of nitrate are approximately constant over the year, except in winter when higher inputs can be recorded. Having better identified the processes responsible for the observed water quality, a more informed remediation effort can be put forward to move this suburban river to a good status of water quality.

Monitoring polymorphic transformations by using in situ Raman hyperspectral imaging and image multiset analysis.

Polymorphism is often encountered in many crystalline compounds. To control the quality of the products, it is relevant knowing the potential presence of polymorph transformations induced by different agents, such as light exposure or temperature changes. Raman images offer a great potential to identify polymorphs involved in a process and to accurately describe this kind of solid-state transformation in the surface scanned. As a way of example, this work proposes the use of multiset analysis on the series of Raman hyperspectral images acquired during a thermal induced transformation of carbamazepine as the optimal way to extract useful information about polymorphic or any other kind of dynamic transformation among process compounds. Image multiset analysis, performed by using Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS), will furnish pure spectra and distribution maps of the compounds involved in the process and, hence, will allow the identification of polymorphs and, more important, the description of the process evolution at a global and local (pixel) level. Thus, process will be defined from a spatial point of view and by means of a set of global process profiles dependent on the process control variable. The results obtained confirm the power of this methodology and show the crucial role of the spatial information contained in the image (absent in conventional spectroscopy) for a correct process description.

Chemometric strategies to unmix information and increase the spatial description of hyperspectral images: a single-cell case study.

Hyperspectral images are analytical measurements that provide spatial and structural information. The spatial description of the samples is the specific asset of these measurements and the reason why they have become so important in (bio)chemical fields, where the microdistribution of sample constituents or the morphology or spatial pattern of sample elements constitute very relevant information. Often, because of the small size of the samples, the spatial detail provided by the image acquisition systems is insufficient. This work proposes a data processing strategy to overcome this instrumental limitation and increase the natural spatial detail present in the acquired raw images. The approach works by combining the information of a set of images, slightly shifted from each other with a motion step among them lower than the pixel size of the raw images. The data treatment includes the application of multivariate curve resolution (unmixing) multiset analysis to the set of collected images to obtain the distribution maps and spectral signatures of the sample constituents. These sets of maps are noise-filtered and compound-specific representations of all the relevant information in the pixel space and decrease the dimensionality of the original image from hundreds of spectral channels to few sets of maps, one per sample constituent or element. The information in each compound-specific set of maps is combined via a super-resolution post-processing algorithm, which takes into account the shifting, decimation, and point spread function of the instrument to reconstruct a single map per sample constituent with much higher spatial detail than that of the original image measurement.

Resolution and segmentation of hyperspectral biomedical images by multivariate curve resolution-alternating least squares.

MCR-ALS is a resolution method that has been applied in many different fields, such as process analysis, environmental data and, recently, hyperspectral image analysis. In this context, the algorithm provides the distribution maps and the pure spectra of the image constituents from the sole information in the raw image measurement. Based on the distribution maps and spectra obtained, additional information can be easily derived, such as identification of constituents when libraries are available or quantitation within the image, expressed as constituent signal contribution. This work summarizes first the protocol followed for the resolution on two examples of kidney calculi, taken as representations of images with major and minor compounds, respectively. Image segmentation allows separating regions of images according to their pixel similarity and is also relevant in the biomedical field to differentiate healthy from non-healthy regions in tissues or to identify sample regions with distinct properties. Information on pixel similarity is enclosed not only in pixel spectra, but also in other smaller pixel representations, such as PCA scores. In this paper, we propose the use of MCR scores (concentration profiles) for segmentation purposes. K-means results obtained from different pixel representations of the data set are compared. The main advantages of the use of MCR scores are the interpretability of the class centroids and the compound-wise selection and preprocessing of the input information in the segmentation scheme.

Time-resolved step-scan FT-IR spectroscopy: focus on multivariate curve resolution.

The present paper describes the application of step-scan FT-IR spectroscopy in combination with chemometric analysis of the spectral data for the study of the photocycle of bacteriorhodopsin. The focus is on the performance of this instrumentation for time-resolved experiments. Three-dimensional data-spectra recorded over time-are studied using various factor analysis techniques, e.g., singular values decomposition, evolving factor analysis, and multivariate curve resolution based on alternating least squares. Transient intermediates formed in the time domain ranging from 1 micros to 6.6 ms are clearly detected through reliable pure time evolving profiles. At the same time, pure difference absorbance spectra are provided. As a result, valuable information about transitions and dynamics of the protein can be extracted. We conclude first that step-scan FT-IR spectroscopy is a useful technique for the direct study of difficult photochemical systems. Second, and this is the essential motivation of this paper, chemometrics provide a step forward in the description of the photointermediates.

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 algorithm's robustness.