Tooth enamel, primarily composed of bioapatite, is a promising archive of endogenous organic matter for studying ancient fauna. Despite its low organic content (~1%), protein residues have been identified in teeth up to 24 million years old. This study investigates the preservation of total hydrolysable amino acids (THAAs) in fossil enamel dating back as far as 48 million years. Modern and fossil enamel from large herbivorous mammals (Equidae, Rhinocerotidae, Proboscidea) across various taphonomic settings and Cenozoic periods reveal that AAs persist at least to the Eocene. The "intra-crystalline" organic fraction stabilizes after an initial rapid decline within the first 0.10 million years. Preservation appears independent of taphonomic context, and the relative abundance of amino acids is similarly variable in both modern and fossil samples. These findings demonstrate that enamel is a diagenetically robust substrate for long-term organic preservation, with significant potential for phylogenetic and ecological reconstructions in the fossil record.

The selective removal of polymeric coatings from multilayer films can be challenging when the outer layer is highly insoluble and chemically similar to the underlying layers. In this context, photothermal materials represent a promising strategy for locally activating polymer softening. In this paper, we studied heat delivery at solid-solid interfaces in a bioderived electrospun nonwoven incorporating melanin nanoparticles as a photothermal agent for the selective removal of polymeric coatings applied to a multilayer system. Through controlled solvent loading and light-induced photothermal heating, a localized temperature modulation at the material-substrate interface can be achieved. A dedicated experimental setup integrating thermal imaging and thermocouple measurements enables direct quantification of interface temperatures under operational conditions. As a proof of concept, the method has been applied to remove a highly insoluble cross-linked alkidic layer applied on an underlying acrylic or alkyd polymer coatings. The efficacy and selectivity of the proposed method have been assessed using a multianalytical protocol that combines imaging techniques with chemometric analysis, HPLC-DAD, and Brillouin microspectroscopy. The synergistic action of photothermally generated heat and the solvent allowed reducing both the amount of solvent used and the application time, while preserving the chemical composition and viscoelastic properties of the underlying layers. Additionally, the electrospun nonwovens remained effective after three cleaning cycles. These results demonstrate that photothermal electrospun nonwovens provide a versatile platform for interface-confined thermal activation, offering new opportunities for selectively removing organic coatings from multilayer systems.

The present review examines the fundamental mechanisms governing the penetration of X-ray and near-infrared (NIR) radiation under sample surface – a feature that is often disregarded in analytical applications, especially in the spectral imaging implementations, which are usually considered as surface analytical techniques. The impact of material composition and geometry, scattering effects, as well as instrumental factors are thoroughly described and critically discussed. A particular focus is placed on data processing techniques, from first-principle equations to data-driven multivariate models, implemented to estimate/assess the extent of penetration. Applications in several areas, including food, forensic, material and cultural heritage sciences, are comprehensively reviewed. The potential for exploiting penetration of electromagnetic radiation is highlighted, paving the way for the development of 3D-resolved X-ray fluorescence (XRF) and NIR imaging approaches able to characterize multilayer samples in a non-invasive way.

Trees are fundamental to human survival and progress, serving as essential resources throughout history. From early societies to modern civilizations, they have provided materials for shelter, tools, transportation, and fuel. The development of ancient societies was often closely tied to forests, which supplied wood for construction, shipbuilding, and daily implements. Beyond their practical uses, trees hold profound symbolic and spiritual significance in many cultures, representing life, wisdom, and resilience. Moreover, their preserved remains continue to shape historical and environmental research, offering invaluable insights into ancient timelines, climatic shifts, and human activity. Tree rings serve as natural archives of past environmental conditions, while their organic material provides a crucial foundation for radiocarbon dating, one of the most reliable methods for establishing absolute chronologies in archaeology. By analyzing the carbon isotopes in ancient wood, scientists can precisely date artifacts, settlements, and cultural transitions, refining our understanding of human history and the broader prehistoric world. A major challenge, however, is that radiocarbon dating is a destructive method, requiring the removal and chemical pre-treatment of a portion of the wood sample necessary for the 14C age determination. This process permanently alters or consumes the analyzed material, posing a significant dilemma for archaeologists, especially when working with rare or culturally significant wooden artifacts. Therefore, sampling must be minimized as much as possible while still ensuring accurate 14C measurement. To address this issue, this study explores the potential of Near Infrared (NIR) spectroscopy as a non-invasive diagnostic tool for assessing cellulose preservation in archaeological wood specimens before radiocarbon dating.The Near-Infrared (NIR) technique was applied to a set of well-characterized archaeological wood samples covering a wide range of chronological periods, provenances, and depositional environments. Short-Wave Infrared (SWIR) hyperspectral data were acquired from the specimens and analyzed through a combination of qualitative spectral interpretation and chemometric methods, including Principal Component Analysis (PCA) and single-band spectral mapping.Overall, the results indicate that NIR spectroscopy represents a rapid, reliable, and completely non-destructive approach for assessing the suitability of archaeological wood for radiocarbon dating. By guiding targeted and minimally invasive sampling, this method improves the efficiency and robustness of ¹⁴C analyses while reducing unnecessary material loss. The proposed workflow contributes to more sustainable radiocarbon practices and aligns analytical requirements with the principles of cultural heritage preservation. Furthermore, the integration of hyperspectral imaging and chemometric analysis offers promising perspectives for broader applications in archaeological science and conservation, including the non-invasive characterization and monitoring of wooden cultural heritage objects.

Reliable spectral quality is essential for extracting meaningful information from infrared reflectance data, particularly when using portable systems with limited scan numbers. This study presents a data-driven spectral enhancement workflow designed to improve the interpretability of portable macroscopic external reflection Fourier Transform Infrared (MA-rFT-IR) mapping systems developed by the Authors, operating in the near- and mid-infrared (NIR-MIR) ranges. Despite the growing use of reflectance imaging spectroscopy, limited attention has been devoted to the development of robust denoising strategies capable of minimizing noise and unwanted variability while preserving spectral quality and enabling more reliable and accurate data analysis. This study proposes a broadly applicable processing framework aimed at enhancing the efficiency and performance of reflectance-based spectral analysis. Denoising methods including Savitzky-Golay filtering and wavelet- and PCA-based denoising were tested and evaluated individually and in combination. Quantitative performance was assessed using arccosine similarity (ACOS) and derivative-based root-mean-square error (dRMSE) metrics across selected spectral regions of interest, with a derivative ACOS (dACOS) index applied to monitor band-shape variations. The evaluation results were integrated through Pareto analysis to identify the optimal trade-off between noise reduction and spectral-feature preservation. Application of the proposed approach to a multilayered painting mock-up demonstrated that the enhancing spectral data workflow preserves key diagnostic features revealing subtle spectral bands. Furthermore, applying multivariate curve resolution-alternating least-squares (MCR-ALS) to the denoised data enabled chemically meaningful separation of complex overlapping signals, improving the interpretability of compositional information compared with traditional denoising methods and data processing. The workflow strengthens the analytical reliability of low-scan reflection-mode data and provides a transferable framework for optimizing denoising strategies in portable infrared applications.

The present study proposes a new method for the digital restoration of color films affected by irregular color degradation. Color fading is a major problem affecting analog color films and while most existing digital methods address homogeneous dye fading, the correction of irregular color degradation remains a challenging and time-consuming process. The main objective of this work is to develop a scalable and minimally user-guided strategy capable of restoring such complex degradation patterns. The proposed approach combines visible-range hyperspectral imaging of degraded film frames to obtain detailed spectral information with a novel computational algorithm referred as the Cluster-Based Spectral Correction Algorithm (CBSCA). This algorithm is specifically designed to handle the large volume of data recorded by hyperspectral acquisition, enabling robust color restoration while ensuring reduced computational demand and limited subjective intervention. In comparison with conventional RGB scanners and color restoration software, this approach offers a methodology to perform accurate color acquisition and correction while effectively managing large hyperspectral datasets. The ultimate goal is to recover the film's original visual content, ensuring its accessibility to the public.

The detection and quantification of microplastics (MPs) in environmental samples remain a significant analytical challenge due to the heterogeneity of polymer mixtures and the presence of organic and inorganic interferents. While Near-infrared (NIR) spectroscopy has emerged as a rapid, cost-effective alternative, most studies have focused on qualitative detection or simplified systems, leaving the influence of environmental interferents largely unexplored. This study proposes a quantitative analytical strategy using a portable NIR spectrometer combined with multivariate regression for the determination of four target polymers (polypropylene, PP, polyethylene, PE, polystyrene, PS, and polyethylene terephthalate, PET) in complex mixtures. MPs were generated through a trueto-life protocol, ensuring realistic particle morphologies and surface conditions. Model robustness was systematically assessed against a wide range of environmental interferents, including non-target polymers (polyvinyl chloride, polylactic acid, and polyamide), natural fibres (cotton, silk), vegetal material, and mineral particles (CaCO3). Polymer quantification was performed through Partial Least Squares (PLS) regression, with each polymer modelled independently. The proposed modelling approach was subjected to a double cross-validation procedure, and their predictive ability was further estimated by external validation procedure. In particular, when external validation samples were spiked with interferents, prediction errors increased moderately due to added spectral complexity; however, the models maintained satisfactory performance, with PE and PET demonstrating the greatest resilience to matrix effects. Finally, the models were successfully applied for the quantification in real environmental samples, with a satisfactory accuracy considering the inherent complexity of "unknown" environmental matrices. These results demonstrate the potential of portable NIR spectroscopy and robust chemometric modelling for quantitative MP analysis in heterogeneous, environmentally realistic scenarios.

Plasma-generated atomic oxygen (AO) is a highly reactive and chemically selective species with strong potential for the precise removal of carbonaceous contaminants from heritage materials. However, its application to organic substrates is limited by competing oxidation processes that may induce chain scission, mechanical degradation, and mass loss. In this study, the parametric dependence of AO-induced soot removal from undyed silk model substrates was systematically investigated using a prototype developed within the MOXY project. The effects of power and total cleaning time were evaluated to quantify their influence on cleaning efficiency and thermal load. Cleaning performance was assessed using optical and morphological techniques, including 3D optical microscopy, profilometry, drop tests, and hyperspectral imaging, while infrared thermography enabled in situ temperature monitoring. Results show that increasing power or treatment time enhances soot removal, but excessive conditions lead to alterations in fiber morphology and mechanical properties. By correlating cleaning efficiency with thermo-mechanical responses, a safe operating window was identified. Tensile testing, thermogravimetric analysis, dynamic mechanical analysis, and dynamic vapor sorption revealed sub-critical damage thresholds. These findings demonstrate that AO plasma treatment can be optimized for fragile proteinaceous substrates, providing conservators with reliable and controlled cleaning parameters for textile heritage objects.

Microplastic (MP) pollution is currently detected in all the (Geyer et al. 2017) main environmental compartments, from terrestrial (VaraPrasad et al. 2022) to freshwater (Wang et al. 2022) and marine systems (Carbery et al. 2018), and the associated environmental and health hazards (Betts 2008) have been the focus of intense social, scientific, and political attention. However, MP detection is nowadays still one of the biggest technological challenges, representing a new analytical issue. There is an increased need for developing standardized protocols for the sampling, quantification, and characterization of MP. These include also data treatment and visualization, which are crucial to compare different studies. Several efforts have been devoted to establish harmonized and traceable procedures. Among others, hyperspectral imaging systems represent potential cost and time effective methods, enabling a direct detection of MP when applied directly on filters without heavy sample purification and manipulations, thus avoiding potential procedural bias related to particle pre-sorting steps. Hyperspectral imaging (HSI) integrates spectral and spatial information, generating chemical maps in which each pixel contains a full spectrum. This approach enables precise identification of chemical constituents, mapping of their spatial distribution, and detection of subtle compositional variations. The resulting multidimensional datasets, may provide robust tools for material characterization. Recently, near infrared hyperspectral imaging (NIR-HSI) has been evaluated as a technique for identifying MP directly on the filters (Piarulli et al. 2022; Vidal and Pasquini 2021). However, the use of HSI in monitoring campaigns may generate large volumes of data difficult to process and interpret. In this context, chemometric methods are emerging as essential tools for the processing and interpretation of chemical data obtained through spectroscopic techniques. These methods enable data dimensionality reduction and visualization, reducing time required for data processing and interpretation and producing graphical outcomes that can be compared among monitoring campaigns or for the integration of different results. Explorative and unmixing methods can be applied to enhance spectral features of MPs in complex matrices, identified diagnostic markers for their discrimination. To this aim, different explorative and unmixing methods have been tested and developed to determine the chemical composition to enhance the efficiency of polymer recognition. In particular, masking procedures for background removal and advanced algorithms based on multivariate data compression were explored, to enhance the spectral signal features of MP with respect to the filters used. This contribution reports the application of a HSI analysis exploiting Mid Infrared (4000–675 cm−1) applied on cellulose filter, in combination with different tailored chemometric methods for data processing. In particular, an automated normalised difference image (NDI) strategy and principal component analysis (PCA) were applied and compared.

Ancient bones are archives of information to reconstruct past life. However, detecting the organic content and the crystallinity changes of bone apatite post-mortem alteration becomes more challenging when burial conditions are coupled with thermal degradation. The present study proposed a non-invasive prescreening method to distinguish burnt bones based on diagnostic spectral features, using a reflectance portable FT-IR spectrometer (650-5500 cm-1) and a portable miniaturized near-infrared (MicroNIR) spectrometer (90 0-170 0 nm). Burnt bones from the Roman age (Modena, Italy) were analyzed and the prescreening approach was combined with a multivariate data analysis. Principal Component Analysis (PCA) was used to enhance spectral changes leading to a differentiation among the specimens, according to their chemical changes. The proposed methodology highlighted the potential of the two non-destructive and portable instruments, and of chemometric analysis to select the most suitable samples for forensic and archaeological studies, overcoming drawbacks related to the traditionally applied visual examination of bones colour. (c) 2025 The Author(s). Published by Elsevier Masson SAS. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

It is challenging to extend the species of deep eutectic solvent (DES) based gels, particularly in forming non-toxic polymer gels with good mechanical properties. In this study, we propose an eutectogel system for cleaning purposes in the field of culture heritage conservation. Green gels containing ChCl-EGGVL-PVA and ChCl-EG-PVA are produced by combining choline chloride (ChCl), ethylene glycol (EG) and polyvinyl alcohol (PVA) with or without gamma-valerolactone (GVL). The crosslinking of the gels is primarily formed by ChCl-EG and PVA through hydrogen bonding. The additional green solvent GVL is compatible with the gel composition and plays an important role in cleaning. The developed gels exhibit good mechanical properties and fine microstructures, making them easy to handle and suitable for cleaning cultural relics. Therefore, the gels have been tested on a mockup coated with Paraloid B72 (R) to examine their cleaning efficiency. Furthermore, the selected gel has been validated on a Yuan dynasty mural painting for the removal of aged acrylic coating. The results from both the mockup and the real case study demonstrate the effective cleaning efficiency of the ChCl-EG-GVL2 -PVA2 gel and highlight its potential usefulness in the field of cultural heritage conservation. (c) 2024 Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

This study is aimed at proposing an analytical protocol to study the in-depth effects of ion beams technique on protein-based heritage materials such as parchment and silk, exploiting the penetration of Near-Infrared (NIR) and of confocal micro-Raman (mu-Raman) spectroscopy. The objective is to verify if the two techniques are suitable to identify the main chemical modifications induced by varying proton beam fluences. As a proof of concept a series of parchment and silk samples were exposed to varying doses of proton beam irradiation (from 0.125, up to 20 mu C/cm2, provided by ATOMKI) and then submitted to micro-FTIR spectroscopy (mu-FTIR) in mapping mode (7000-675 cm-1) and mu-Raman spectroscopy. By collecting three-dimensional dataset of irradiated versus unirradiated regions, the extent of degradation in response to different levels of proton beam dosage was identified employing a multivariate statistical approach based on principal component analysis (PCA). PCA proved efficient in reducing the dimensionality of the large spectroscopic datasets and in highlighting the most relevant spectral features responsible for the irradiation-induced modifications across the near-infrared and mid-infrared regions. Results from this study indicate that mu-FTIR mapping in the NIR spectral region enables intuitive identification of modifications induced by proton beam fluence range from 0.5 to 20 mu C/cm2 for parchment and 1 to 20 mu C/cm2 for silk. In addition, the mu-Raman analysis revealed that deeper layers of the samples are susceptible to damage at doses as low as 0.125 mu C/cm2. This finding provides valuable insights into the vulnerability of protein-based materials when subjected to ion beam analysis (IBA), contributing to the determination of safe analytical conditions for performing such analyses.

Amelogenins (AMGs) are extracellular matrix proteins essential for enamel development. During this process, resident proteases cleave the full-length proteins into a heterogeneous mixture of peptides that is retained within mature enamel. Current enamel proteomics primarily focuses on detecting sexually dimorphic AMG peptides for sex identification in archaeological and forensic contexts. In parallel, AMG pattern analysis supports research into enamel-related pathologies and clinical applications. Furthermore, AMG expression in odontogenic epithelium is considered a potential indicator for the histological behavior of tumors. Although their detection is commonly achieved by mass spectrometry, no fast, low-cost, miniaturizable platforms with minimal sample preparation are currently available. To address the complexity of real enamel extracts, we developed synthetic receptors based on molecularly imprinted polynorepinephrine (MIPNE). We integrated them into Surface Plasmon Resonance (SPR) and Bio-Layer Interferometry (BLI) platforms. Leveraging the conventional Single-Epitope Imprinting (SEI) strategy, we introduced an original "Finger-Imprinting" (FI) approach that exploits the direct imprinting of human enamel extracts. SPR binding assays toward standard AMGX revealed superior affinity and capacity for FI-imprinted receptors (KD = 3.6 × 10-8 M, Rmax = 2286 ± 5 RU) compared with SEI-imprinted counterparts (KD = 4.9 × 10-7 M, Rmax = 157 ± 6 RU). BLI enabled the selective recognition of AMG-derived fragments in enamel extracts thanks to a higher representation of the binding cavities. We additionally achieved encouraging evidence of sex differentiation in real samples. This work introduces a novel imprinting concept that adapts the complexity of degraded/digested proteins in real samples to pattern analysis via biosensing.

In recent decades, scientific methodologies applied in theCultural Heritage field have been growing, due to their pivotal role in guiding informed decisions concerning conservation strategies and daily maintenance. To achieve this goal, minimally/non-invasive quantitative and qualitative analyses are needed. However, the non-invasive and selective identification of proteinaceous binders and coatings in artworks represent an open issue in Cultural Heritage science. Herein, a novel miniaturized system is introduced, which consists of a label-free electrochemical immunosensor integrated with biocompatible Gellan gel. This method is intended to selectively and minimally invasively identify ovalbumin (OVA) on-site in paintings. The label-free immunosensor is made up on screen-printed electrodes (SPEs) by functionalizing the working electrode (WE) with a primary antibody (anti-ovalbumin) for the specific recognition of OVA. The presence of OVA produces antigen-antibody reaction, which results in the development of a bulky immunocomplex on the WE. This complex is quantified using square wave voltammetry (SWV) and a reversible redox probe: the current measured is inversely proportional to the OVA concentrations. The developed immunosensors showed good analytical performances when applied directly to painted mock-ups, exhibiting a limit of detection (LOD) of 1.6 ng mL- 1, a limit of quantification (LOQ) equal to 16 ng mL- 1, a working range between 0.01 and 0.4 mu g mL- 1 and selectivity for OVA over other protein components commonly present in painted artworks, including bovine serum albumin (BSA), collagen, and casein. The outcomes highlighted the dependability of the immunosensor in detecting OVA and the efficacy of Gellan gel as a streamlined method for extracting the target protein while preventing residue accumulation on the painting surface. This advancement suggests the potential of Gellan gel-coupled immunosensor systems as viable diagnostic alternatives for artwork management and preservation.

BACKGROUND:This study explores the application of infrared scattering-type Scanning Near-field Optical Microscopy (IR s-SNOM) to analyze zinc white paint models at the nanoscale, focusing on the formation and crystallization of zinc carboxylates. Zinc carboxylates are critical degradation products in oil and tempera paintings, causing brittleness, delamination, and color changes. Our research applies IR s-SNOM to paint samples prepared with oil and egg binders, subjected to both natural and artificial aging. RESULTS:The impact of environmental conditions and binding mediums on inter-sample variability was highlighted using a chemometric approach. Notably, zinc carboxylates form more rapidly in oil binders under artificial aging, while natural ageing results in more ordered crystalline structures in egg binders. While the analysis of individual IR s-SNOM spectra did not reveal an unambiguous correlation between morphology and chemistry at the nanoscale, it allowed to disentangle the causes of intra-sample variability. The surface sensitivity guaranteed by IR s-SNOM was the key element for disclosing spectral features hidden by micro-approaches, such as the identification of the broad band in the 1700-1500 cm-1 spectral region in the egg medium, which had never been reported before. SIGNIFICANCE:This study highlights the critical role that nanoscale analysis may play in advancing the understanding of degradation mechanisms, offering valuable insights for art conservation. By transitioning from average, bulk analysis to nanoscale investigations, IR s-SNOM proved to be a powerful tool for developing targeted conservation strategies, ultimately enhancing the preservation of cultural heritage.

Over the last few decades, significant research efforts have been devoted to developing new cleaning systems aimed at preserving cultural heritage. One of the main objectives is to selectively remove aged or undesirable coatings from painted surfaces while preventing the cleaning solvent from permeating and engaging with the pictorial layers. In this work, we propose the use of electrospun polyamide 6,6 nonwovens in conjunction with a green solvent (dimethyl carbonate). By adjusting the electrospinning parameters, we produced three distinct nonwovens with varying average fiber diameters, ranging from 0.4 mu m to 2 mu m. These samples were characterized and tested for their efficacy in removing dammar varnish from painted surfaces. In particular, the cleaning process was monitored using macroscale PL (photoluminescence) imaging in real-time, while postapplication examination of the mats was performed through scanning electron microscopy. The solvent evaporation rate from the different nonwovens was evaluated using gravimetric analysis and Proton Transfer Reaction- Time -of -Flight. It was observed that the application of the nonwovens with small or intermediate pore sizes for the removal of the terpenic varnish resulted in the swollen resin being absorbed into the mats, showcasing a peel -off effect. Thus, this protocol eliminates the need for further potentially detrimental removal procedures involving cotton swabs. The experimental data suggests that the peel -off effect relates to the microporosity of the mats, which enhances the capillary rise of the swollen varnish. Furthermore, the application of these systems to historical paintings underwent preliminary validation using a real painting from the 20th century.

Hyperspectral imaging (HSI) has emerged as an effective tool to obtain spatially resolved spectral information of artworks by combining optical imaging with spectroscopy. This technique has proven its efficacy in providing valuable information both at the large and microscopic scale. Interestingly, the macro scale has yet to be thoroughly investigated using this technology. While standard HSI methods include the use of spatial or spectral filters, alternative methods based on Fourier-transform interferometry have also been utilised. Among these, a hyperspectral camera employing a birefringent common-path interferometer, named TWINS, has been developed, showing a high robustness and versatility. In this paper, we propose the combination of TWINS with a macro imaging system for the study of cultural heritage (CH). We will show how the macro-HSI system was designed, and we will demonstrate its efficient capabilities to collect interferometric images with high visibility and good signal of both reflectance and fluorescence on the same field of view, even on non-flat samples. Our hyperspectral camera for macro studies of both reflectance and fluorescence data is a completely new asset in the CH panorama and beyond. The relevance of the macro technology is demonstrated in two case studies, aiding in the analysis of biofilms on stone samples and of the degradation of dyed textiles.

Cleaning unwanted paint layers represents a significant challenge in cultural heritage restoration, requiring high effectiveness, spatial precision, and nontoxic techniques. Cleaning vandalic acts or street art paints is particularly challenging because of insoluble varnishes, which are very resistant to traditional removal treatments. Here, for the first time, we employ the photothermal effect for cleaning an artwork, using electrospun nonwovens incorporated with melanin nanoparticles (NPs). This material shows outstanding photothermal properties and photostability. The nonwoven incorporated with melanin NPs, in combination with a solvent, efficiently removes alkyd resin paint layers in a short time of application, with high spatial control. Moreover, an eco-compatible system is obtained by producing a nonwoven made up of a natural polymer electrospun in water, cuttlefish ink as a melanin source, and a green solvent. In summary, using the new pullulan-melanin nonwoven represents a novel and unusual application of the photothermal effect, and its fastness, effectiveness, and safety make it suitable for use in the artwork restoration field.

Simon Hantai (1922-2008) was a highly influential postwar painter in Paris whose innovative serial practice, creative curiosity and theoretical convictions inspired a number of his contemporaries. His art media are typical of the period, consisting of commercial artists' products, sold in tubes and cans, which were available to artists in Europe and beyond. We have studied a series of samples from the brands Lefebvre-Foinet, Lefranc & Bourgeois and Valor using a combination of optical and electron microscopy, accelerator-mass spectrometry carbon -14 dating, infrared spectroscopy, structural analysis, chromatographic and mass spectrometry techniques. Of particular interest is the rare access to a coherent artist's studio collection and its dating in relation to the painter's works. We gained precise information on paint formulations, including main binders and pigments, as well as additives, such as free metal soaps, beeswax and pine resin. This suggests the value of further research into paint formulations and their identification in paintings from the second half of the 20th century. These materials were studied for their capacity as possible references for future analysis of the painter's artworks. The high degree of hydrolysis of the oil binder and alteration, notably by saponification, leads us to question the significance of these materials and the handling of the data generated towards comparative studies. These samples have a history; considering them as pristine references for comparative studies with the works of artists of the period cannot be done at the expense of their own materiality - and in particular their physicochemical evolution over time in their specific environment (c) 2023 Consiglio Nazionale delle Ricerche (CNR). Published by Elsevier Masson SAS. All rights reserved.

The present study describes an innovative approach for the study of time-dependent alteration processes. It combines an advanced hyperspectral imaging (HSI) system, to collect visible reflectance and fluorescence spectral data sets sequentially, with a tailored multiblock data processing method. This enables the modeling of chemical degradation maps and the early, spatially resolved detection of dye alteration in textiles. A chemometric method based on data fusion and principal component analysis was employed to identify spectral features of dye degradation, combining and enhancing information from reflectance and fluorescence HSI data. The most significant spectral profiles extracted were used to develop an asymmetric Gaussian-based pixel-by-pixel fitting model applied to the HSI fluorescence data set, enabling the reconstruction of degradation maps for rapid and intuitive visualization. In particular, changes in intensities and horizontal shift of dye emission peaks were pixel-by-pixel evaluated and fitted for the reconstruction of the degradation maps. Artificially aged wool samples tinted with indigo carmine (IC) dye served as a case study. IC is extensively used in textiles, and it is notable for its light sensitive. The results show that this approach effectively identifies spatial variations and chemical changes in dyed wool fibers, offering potential for sustainable conservation of historical textiles and other types of time-dependent processes. Thus, by amplifying variation in spectral profiles induced over time by aging, even minimal changes at early stages can be easily detected and localized, offering powerful tools for future studies on food and drug shelf life and stability, as well as forensic trace analysis.