Topological data analysis: A promising big data exploration tool in biology, analytical chemistry and physical chemistry

An important feature of experimental science is that data of various kinds is being produced at an unprecedented rate. This is mainly due to the development of new instrumental concepts and experimental methodologies. It is also clear that the nature of acquired data is significantly different. Indeed in every areas of science, data take the form of always bigger tables, where all but a few of the columns (i.e. variables) turn out to be irrelevant to the questions of interest, and further that we do not necessary know which coordinates are the interesting ones. Big data in our lab of biology, analytical chemistry or physical chemistry is a future that might be closer than any of us suppose. It is in this sense that new tools have to be developed in order to explore and valorize such data sets. Topological data analysis (TDA) is one of these. It was developed recently by topologists who discovered that topological concept could be useful for data analysis. The main objective of this paper is to answer the question why topology is well suited for the analysis of big data set in many areas and even more efficient than conventional data analysis methods. Raman analysis of single bacteria should be providing a good opportunity to demonstrate the potential of TDA for the exploration of various spectroscopic data sets considering different experimental conditions (with high noise level, with/without spectral preprocessing, with wavelength shift, with different spectral resolution, with missing data)

Chapter 15 - Super-resolution in vibrational spectroscopy: From multiple low-resolution images to high-resolution

International audienceImaging spectroscopy is a key tool in analytical chemistry. Although spatial molecular characterization is achieved for many applications, it often fails to produce chemical images of micron size samples as needed in chemical, environmental, and biological analysis. The aim of this chapter is thus to introduce the potential of super-resolution in vibrational spectroscopic imaging. This new approach uses several low-resolution images of the same sample observed from different angles in order to generate a higher-resolution chemical image. It is thus possible to overcome in a certain way some physical and instrumental limitations. However, we will see that Multivariate Curve Resolution methods (see Chapter 2) have to be applied prior to super-resolution when complex samples are explored

A new chemometrics preprocessing based on effective information truncation to handle matrix rank deficiencies as well as the effects of noise and light scattering in 3D excitation emission fluorescence matrices

International audience1 IntroductionFluorescence spectroscopy exploits the phenomenon of natural or induced fluorescence emission, from intrinsic fluorophores or fluorescent chemical derivatives after addition of extrinsic fluorophores, respectively [1]. Polycyclic aromatic compounds (PACs) constitute a large family of mainly anthropogenic chemical contaminants. They have at least two aromatic rings which give them intrinsic fluorescence [2]. Their characterization by eco-friendly 3D fluorescence spectroscopy coupled with chemometrics algorithms constitutes a powerful alternative to the separative techniques conventionally used. However, the systematic presence of Rayleigh and Raman scattering signals in the Excitation Emission Matrices (EEMs) makes spectral decomposition via PARAllel FACtor analysis (PARAFAC) difficult due to the non-trilinear structure of these signals and the matrix rank deficiencies that they generate. There are several strategies to overcome these light scattering effects but weakness remain [3]. Thus, a new chemometrics approach to push back matrix rank deficiencies and to handle these interferences and noise in the data is suggested in this work. It is based on advanced truncation strategy in singular value decomposition (SVD) [4].2 TheoryThe home-made algorithm is structured in three main steps, the step #1 is about data formatting. It allows to prepare data for processing. In the case of EEMs, the reshape operation allows to toggle from 3D space to 2D space thanks to the row-wise or column-wise matrix augmentation. Step #2 exploits an advanced SVD truncation strategy. The challenge with this method is determining the truncation threshold which is the number of singular values to retain and which low rank to choose. The proposed approach attempts to overcome this difficulty because the optimal low-rank is not chosen according to singular values curve versus their numbers, but is deduced through image analysis. Step #3 is a reconstruction of clean data matrix deduced from selected singular values representing all the chemical compound information.3 Material and methodsIn order to implement the home-made algorithm, EEMs of 47 samples were acquired to build a database. Four PACs were selected: Naphthalene (NPH); Benz[a]Anthracene (BaA); Anthracene (ANT) and Pyrene (PYR). The database was distributed as three datasets, where dataset 1 was for individual PAC in dichloromethane at six different concentrations (20, 10, 1, 0.25, 0.1 and 0.05 mg. L-1), while dataset 2 was for mixtures of NPH and BaA at varying concentrations in the same solvent. Dataset 3 was for mixtures of the four species at varying concentrations. An Aqualog® fluorescence spectrometer was used to acquire EEMs. It is equipped with a charge coupled device detector (CCD) set to medium gain and time integration equal to 1 second. The samples were excited using a range of excitation wavelengths between 239 and 800 nm with a pitch of 3 nm. The fluorescence emission was collected in a wavelength range between 248.27 and 829.32 nm with a resolution of 4 pixels (i.e. 2.33 nm). A Quartz SUPRASIL® cell with a light path equal to 10 mm was used. 4 Results and discussionIn the example shown in the Figure 1, the chemical information is kept intact while the scattering signals have been removed by the preprocessing. Furthermore, the optimal low-rank is found through image analysis and the percentage of information, explained at the truncated matrix level coupled with the analysis of residual information. Finally, a region-based segmentation algorithm enabled automatic cropping of the cleaned map.5 ConclusionThe method proposed in this work is based on one of the most common algorithms in linear algebra (i.e. SVD) with an original imaging approach to its application with EEM or EEMs data. Its advantages are that it does not require any information concerning the scattering signals and effectively handle these interferences and noise. Moreover, it provides the percentage of chemical information and noise in the raw data. Finally, it fends off matrix rank deficiencies and generates an estimation of the number of factors to choose for spectral decomposition like PARAFAC

Identification par spectroscopie Raman de polymorphes de CaCO3présents dans des bétons de granulats de béton recyclé carbonatés etutilisés comme puits de CO2

International audienceLa préservation des ressources naturelles et à de réduire les émissions de CO2, dontcelle causée par la production de ciment, et à recycler les déchets de béton issus de ladéconstruction a conduit au programme national FastCarb. Il vise à utiliser desgranulats de béton recyclé (GBR) pour de nouveaux bétons. Cependant, certainescaractéristiques microstructurales des GBR, dont la porosité, doivent être améliorées.Ainsi, leur carbonatation par du CO2 des sites de production de ciment est une solution.Cette étape réduit la porosité et améliore les propriétés futures du béton élaborés avecdes GBR carbonatés. Deux procédés de carbonatation différents ont été mis en oeuvreet les GBR ainsi traités mélangés dans des proportions différentes avec des granulatsnaturels pour élaborer de nouveaux bétons. Des mesures Raman ont ensuite étéeffectuées sur certaines coupes pour analyser les phases carbonatées [1, 2] et leurrépartition spatiale. Les résultats et les analyses chimiométriques ont montré unedifférence dans les distributions des polymorphes de CaCO3 selon le processus decarbonatation, et l'épaisseur de l'interface entre les anciennes et les nouvelles pâtes deciment.carbonatation par lit fluidisé carbonatation par tambour rotatifcalcitearagonitevatériteFigure 1. Exemples de distributions spatiales de polymorphes de CaCO3 selon le process decarbonatation par traitement chimiométriques MCR-ALS de mesures RamanRéférences:[1] Wehrmeister U., Soldati A. L., Jacob D. E., Häger, T., Hofmeister, W. (2010), Ramanspectroscopy of synthetic, geological and biological vaterite: a Raman spectroscopic study. J.Raman Spectrosc., 41: 193-201. https://doi.org/10.1002/jrs.2438.[2] Ševčík R., Mácová P. Localized quantification of anhydrous calcium carbonatepolymorphs using micro-Raman spectroscopy. Vibrational Spectroscopy, volume 95, 2018,pp. 1-6. https://doi.org/10.1016/j.vibspec.2017.12.005

Gérer la déficience du rang matriciel, le bruit et les interférences dans les matrices d’émission-excitation de fluorescence grâce à la chimiométrie : un nouvel algorithme appliqué à l’analyse des composés aromatiques polycycliques

International audienceLa caractérisation de la contamination des matrices environnementales, par des composés organiques, via la spectroscopie de fluorescence 3D couplée aux algorithmes de chimiométrie constitue une alternative puissante aux techniques séparatives conventionnelles [1]. Cependant, la présence systématique des signaux de diffusion Rayleigh et/ou Raman dans les matrices d’émission-excitation de fluorescence (EEM) complique la décomposition spectrale via l’analyse PARAllel FACtor (PARAFAC) à cause de la structure non-trilinéaire de ces signaux. De plus, un problème spécifique de sélectivité en spectroscopie pour des composants chimiques inattendus dans un échantillon complexe peut rendre son interprétation chimique difficile à première vue. L’information chimique pertinente peut alors être compliquée à extraire, surtout si les données brutes sont bruitées. Il existe plusieurs stratégies pour pallier ces inconvénients mais des faiblesses subsistent [2].En conséquence, une nouvelle méthode alternative est proposée pour gérer ces interférences, le bruit et la déficience du rang matriciel dans les données. Elle est appliquée pour la caractérisation des mélanges de composés aromatiques polycycliques (CAP). Elle est basée sur une décomposition en valeurs singulières tronquées efficacement (MT-SVD) qui ne nécessite aucune connaissance préalable sur les données brutes. L'algorithme fournit une estimation précieuse du rang matriciel optimal à choisir sur des échantillons complexes où des problèmes de sélectivité sont observés. C'est une véritable alternative par rapport aux autres méthodes existantes appliquées sur les EEM de fluorescence pour filtrer le signal du bruit ou gérer les effets de diffusion de la lumière. Les premiers résultats exploratoires de l'algorithme sont prometteurs pour gérer la déficience du rang matriciel ainsi que les effets du bruit et de la diffusion de la lumière sur les mélanges complexes de CAP. Enfin, le MT-SVD est un algorithme flexible et il sera testé sur d'autres techniques instrumentales (par exemple l'imagerie Raman) et d'autres types d'échantillons

Exploratory study of infrared spectral signatures of a range of forest, agricultural and artificialized soils from the North-East of France

International audienceArtificialized soils encompass a large diversity depending both on the environmental conditions and the history of land uses. Their study requires to develop an approach to compare and classify them. Vibrational spectroscopies are used in soil science to collect rapid and cost-effective molecular information about inorganic and organic soil constituents [1]. Coupled with chemometrics approaches (e.g. [2]), they can be used to estimate some properties and/or to classify soils [3]. This study aims at exploring the potential of mid-infrared spectroscopy (i) to differentiate soils depending on past land uses, (ii) to propose a soil typology and (iii) to define markers of human activities.A set of 150 surface soil samples from the North-East of France was selected, including agricultural and forest soils developed on various bedrocks and soils impacted by iron and glass industry, mining, charcoal production or old human settlements. Mid-infrared analyses were run in diffuse reflectance mode. An exploratory study was performed on the preprocessed spectra using ascending hierarchical classification and principal component analysis. Studied soils can be distinguished based on their mineralogical composition (carbonates, clays) and, to a lesser extent, on the presence of organic compounds (Fig1). However, changes related to old settlements, mining or charcoal production were more difficult to discriminate. This could be improved by coupling several spectroscopic analyses providing complementary information on the samples

Handle matrix rank deficiency, noise and interferences in 3D emission-excitation matrices using chemometrics: a new algorithm applied to the analysis of polycyclic aromatic hydrocarbons

International audienceThe characterization of organic compounds in polluted matrices by eco-friendly 3D fluorescence spectroscopy coupled with chemometrics algorithms constitutes a powerful alternative to the separative techniques conventionally used1. However, the systematic presence of Rayleigh and/or Raman scattering signals in the Excitation Emission Matrices (EEMs) complicates the spectral decomposition via PARAllel FACtor analysis (PARAFAC) due to the non-trilinear structure of these signals. Likewise, specific problem of selectivity in spectroscopy for unexpected chemical components in a complex sample may render its chemical interpretation difficult at first glance. The chemical relevant information can then be complicated to extract, especially if the raw data is noisy. There are several strategies to overcome these drawbacks but weaknesses remain2. As a consequence, a new alternative method is proposed to handle these interferences, noise and rank deficiencies in the data and applied for the characterization of polycyclic aromatic compound (PAC) mixtures. It is based on an effective truncated singular value decomposition (MT-SVD) which does not require any prior knowledge on the raw data. The algorithm provides a valuable estimation of the optimal low-rank to choose on complex samples where selectivity problems are observed. It is a real alternative compared to other existing methods applied on fluorescence matrix to filter the signal from noise or light scattering effects. The first exploratory results of the proposed algorithm are promising to handle matrix rank deficiencies as well as the effects of noise and light scattering on complex PAC mixtures. Finally, the MT-SVD is a flexible algorithm and it will be tested on other instrumental techniques (e.g. Raman imaging) and other types of samples