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Hyperspectral imaging technology is developing in a very fast way. We find it today in many analytical developments using different spectroscopies for sample classification purposes. Instrumental developments allow us to acquire more and more data in shorter and shorter periods of time while improving their quality. Therefore, we are going in the right direction as far as the measure is concerned. On the other hand, we can make a more mixed assessment for the hyperspectral imaging data processing. Indeed, the data acquired in spectroscopic imaging have the particularity of encoding both spectral and spatial information. Unfortunately, in chemometrics, almost all classification approaches today only use spectral information from three-dimensional hyperspectral data arrays. To be more precise, an approach encompassing the unfolding/refolding of such arrays is often applied beforehand because the majority of algorithms for analysing these data are not capable of handling them in their original structure. Spatial information is therefore lost during the chemometric exploration. The study of the spectral part of the acquired data array alone is clearly a limitation that we propose to overcome in this work. 2-D Stationary Wavelet Transform will be used in the data preprocessing phase to ensure the joint use of spectral and spatial information. Two spectroscopic datasets will then be used to evaluate the potential of our approach in the context of supervised classification., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Laser-induced breakdown spectroscopy (LIBS) imaging is an innovative technique that associates the valuable atomic, ionic and molecular emission signals of the parent spectroscopy with spatial information. LIBS works using a powerful pulse laser as excitation source, to generate a plasma exhibiting emission lines of atoms, ions and molecules present in the ablated matter. The advantages of LIBS imaging are potential high sensitivity (in the order of ppm), easy sample preparation, fast acquisition rate (up to 1 kHz) and μm scale spatial resolution (weight of the ablated material in the order of ng). Despite these positive aspects, LIBS imaging easily provides datasets consisting of several million spectra, each containing several thousand spectral channels. Under these conditions, the current chemometric analyses of the raw data are still possible, but require too high computing resources. Therefore, the aim of this work is to propose a data compression strategy oriented to keep the most relevant spectral channel and pixel information to facilitate, fast and reliable signal unmixing for an exhaustive exploration of complex samples. This strategy will apply not only to the context of LIBS image analysis, but to the fusion of LIBS with other imaging technologies, a scenario where the data compression step becomes even more mandatory. The data fusion strategy will be applied to the analysis of a heterogeneous kyanite mineral sample containing several trace elements by LIBS imaging associated with plasma induced luminescence (PIL) imaging, these two signals being acquired simultaneously by the same microscope. The association of compression and spectral data fusion will allow extracting the compounds in the mineral sample associated with a fused LIBS/PIL fingerprint. This LIBS/PIL association will be essential to interpret the PIL spectral information, which is nowadays very complex due to the natural overlapped signals provided by this technique., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The molecular analysis of complex matrices such as vacuum gas oils require powerful instruments such as Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS). As this technique does not allow the separation of two isomers, ion mobility coupled to mass spectrometry (IMMS) can be used to target a structural detail. However, the resolving power of ion mobility is not sufficient to resolve isomers in such a complex mixture. In this paper, ion mobility-mass spectrometry coupled to separative methods such as Flash-HPLC and UHPLC has been used to characterize the neutral nitrogen compounds found in vacuum gas oils. One vacuum gas oil feedstock as well as different hydrotreated samples have been analyzed through a heart-cutted HPLC-UHPLC-IM-QqToF analysis to target specific compounds that have been found to be problematic within hydrotreatment context thanks to ESI(-)-FT-ICR MS analyses. The extraction of the macroscopic descriptors (mobility, full-width at half-maximum) allowed highlighting first trends about the samples. Then, the chromatographic peaks obtained for a given alkylation degree have been divided into several retention time segments and the corresponding mobilograms have been obtained. Bi-modal distributions have been obtained and the observed Collision Cross Sections and MS/MS spectra suggested the presence of compact and non-compact structures. The evolution of these structures has been followed throughout hydrotreatment to evaluate both the quantity and the reactivity of the groups of isomers. Moreover, this methodology helped giving clues whether the targeted compounds are refractory to the hydrotreatment process or reaction intermediates of the hydrotreatment process., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

We have all been confronted one day by saturated signals observed on acquired spectra, whatever the technique considered. A saturation, also known as clipping in signal processing, is a form of distortion that limits a signal once it exceeds a threshold. As a consequence, clipped or saturated bands with their characteristic plateau present numerical values that do not correspond to the analytical reality of the analyzed sample. Of course, analysts know that they cannot consider these erroneous values and therefore reconsider either sample preparation or instrument settings. Unfortunately, there are many experiments today (and this is the case in spectroscopic imaging) for which we will not be able to fight against the saturation effect that will undeniably be observed on the acquired spectra. The aim of this article is first to show why it is important to correct these saturation effects at the risk of having a biased view of the sample and more specifically in the context of multivariate data analysis. In a second step, we will look at strategies for managing saturated bands. An original concept will then be presented by considering saturated values as missing ones. A statistical imputation strategy will then be implemented in order to recover the information lost during the measurement., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Classification of high-dimensional spectroscopic data is a common task in analytical chemistry. Well-established procedures like support vector machines (SVMs) and partial least squares discriminant analysis (PLS-DA) are the most common methods for tackling this supervised learning problem. Nonetheless, interpretation of these models remains sometimes difficult, and solutions based on feature selection are often adopted as they lead to the automatic identification of the most informative wavelengths. Unfortunately, for some delicate applications like food authenticity, mislabeled and adulterated spectra occur both in the calibration and/or validation sets, with dramatic effects on the model development, its prediction accuracy and robustness. Motivated by these issues, the present paper proposes a robust model-based method that simultaneously performs variable selection, outliers and label noise detection. We demonstrate the effectiveness of our proposal in dealing with three agri-food spectroscopic studies, where several forms of perturbations are considered. Our approach succeeds in diminishing problem complexity, identifying anomalous spectra and attaining competitive predictive accuracy considering a very low number of selected wavelengths., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Today, Laser-Induced Breakdown Spectroscopy (LIBS) imaging is in full change. Indeed, always more stable instrumentations are developed, which significantly increases the signal quality and naturally the analytical potential of the technique for the characterization of complex and heterogeneous samples at the micro-scale level. Obviously, other intrinsic features such as a limit of detection in the order of ppm, a high field of view and high acquisition rate make it one of the most complete chemical imaging techniques to date. It is thus possible in these conditions to acquire several million spectra from one single sample in just hours. Managing big data in LIBS imaging is the challenge ahead. In this paper, we put forward a new spectral analysis strategy, called embedded k-means clustering, for simultaneous detection of major and minor compounds and the generation of associated localization maps. A complex rock section with different phases and traces will be explored to demonstrate the value of this approach., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Many plant tissues can be observed thanks to autofluorescence of their cell wall components. Hyperspectral autofluorescence imaging using confocal microscopy is a fast and efficient way of mapping fluorescent compounds in samples with a high spatial resolution. However a huge spectral overlap is observed between molecular species. As a consequence, a new data analysis approach is needed in order to fully exploit the potential of this spectroscopic technique and extract unbiased chemical information about complex biological samples. The objective of this work is to evaluate multi-excitation hyperspectral autofluorescence imaging to identify biological components in wheat grains during their development through their spectral profiles and corresponding contribution maps using Multivariate Curve Resolution - Alternating Least-Squares (MCR-ALS), a signal unmixing algorithm under proper constraints. For this purpose two different scenarios are used: 1) analyzing the total spectral domain of data sets using MCR-ALS under non negativity constraint in both spectral and spatial modes; 2) analyzing a reduced spectral domain of data sets using MCR-ALS under non negativity in both modes and trilinearity constraint in spectral mode. Considering the original instrumental setup and our data analysis approach, we will demonstrate that extracted contribution maps and spectral profiles of constituents can provide complementary information used to identify molecules in complex biological samples., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Hyperspectral imaging is a way to explore the spatial and spectral information of the different compounds in chemical or biological samples. In addition, multivariate curve resolution - alternating least squares (MCR-ALS) can be used to extract this information based on the bilinearity assumption. However, it is well-known that using proper constraints can reduce the amount of uncertainty in the results of MCR, which is called rotational ambiguity. In MCR-ALS analysis of hyperspectral images, different image processing techniques, such as model fitting, image segmentation or sparse image recovery can be applied as spatial constraints. In this contribution, we aim to investigate how the use of these spatial constraints limits the extent of rotational ambiguity of MCR-ALS solutions. For this purpose, we evaluate the extent of rotational ambiguity and use Borgen plots to visualize it. We show on simulations and real hyperspectral imaging data that accuracy of the results is improved when spatial constraints are applied., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Analytical chemistry is rapidly changing. Indeed we acquire always more data in order to go ever further in the exploration of complex samples. Hyperspectral imaging has not escaped this trend. It quickly became a tool of choice for molecular characterisation of complex samples in many scientific domains. The main reason is that it simultaneously provides spectral and spatial information. As a result, chemometrics has provided many exploration tools (PCA, clustering, MCR-ALS …) well-suited for such data structure at early stage. However we are today facing a new challenge considering the always increasing number of pixels in the data cubes we have to manage. The idea is therefore to introduce a new paradigm of Topological Data Analysis in order explore hyperspectral imaging data sets highlighting its nice properties and specific features. With this paper, we shall also point out the fact that conventional chemometric methods are often based on variance analysis or simply impose a data model which implicitly defines the geometry of the data set. Thus we will show that it is not always appropriate in the framework of hyperspectral imaging data sets exploration., (Copyright © 2017 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.)

Electron paramagnetic resonance (EPR) spectroscopy is a powerful technique that is able to characterize radicals formed in kinetic reactions. However, spectral characterization of individual chemical species is often limited or even unmanageable due to the severe kinetic and spectral overlap among species in kinetic processes. Therefore, we applied, for the first time, multivariate curve resolution-alternating least squares (MCR-ALS) method to EPR time evolving data sets to model and characterize the different constituents in a kinetic reaction. Here we demonstrate the advantage of multivariate analysis in the investigation of radicals formed along the kinetic process of hydroxycoumarin in alkaline medium. Multiset analysis of several EPR-monitored kinetic experiments performed in different conditions revealed the individual paramagnetic centres as well as their kinetic profiles. The results obtained by MCR-ALS method demonstrate its prominent potential in analysis of EPR time evolved spectra., (Copyright © 2016 Elsevier B.V. All rights reserved.)

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)., (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.)

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.)

Chemical imaging systems help to solve many challenges in various scientific fields. Able to deliver rapid spatial and chemical information, modern infrared spectrometers using Focal Plane Array detectors (FPA) are of great interest. Considering conventional infrared spectrometers with a single element detector, we can consider that the diffraction-limited spatial resolution is more or less equal to the wavelength of the light (i.e. 2.5-25 microm). Unfortunately, the spatial resolution of FPA spectroscopic setup is even lower due to the detector pixel size. This becomes a real constraint when micron-sized samples are analysed. New chemometrics methods are thus of great interest to overcome such resolution drawback, while keeping our far-field infrared imaging spectrometers. The aim of the present work is to evaluate the super-resolution concept in order to increase the spatial resolution of infrared imaging spectrometers using FPA detectors. The main idea of super-resolution is the fusion of several low-resolution images of the same sample to obtain a higher-resolution image. Applying the super-resolution concept on a relatively low number of FPA acquisitions, it was possible to observe a 30% decrease in spatial resolution., (2010 Elsevier B.V. All rights reserved.)

Imaging in Raman spectroscopy is a valuable tool for analytical chemistry. Although molecular characterization at micron level is achieved for many applications, it usually fails producing chemical images of micron size samples as expected in chemical, environmental and biological analysis. The aim of the work is to introduce the potential of super-resolution in vibrational spectroscopic imaging. This original chemometrics approach uses several low resolution images of the same sample in order to retrieve a higher resolution chemical image. It is thus possible to overcome in a certain way some physical and instrumentals limitations. To illustrate the methodology, sub-micronic details of a Si/Au sample are retrieved from low resolution images with different super-resolution algorithms. The better results are obtained with Iterative L2/Bilateral Total Variation regularization method. The use of a regularization procedure gives also better results since its first property is to preserve edges during the reconstruction of the super-resolved image. This concept of chemical image data processing should open new analytical opportunities.