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•The t-SNE algorithm is introduced into forensic ink data analysis.•Created hyperspectra database of inks from 60 pens, from different manufactures, type and colour.•Compared the clustering quality of t-SNE against PCA on hyperspectral ink data.•Clustering quality compared using four different clustering quality indexes.•The t-SNE provided better visualization and clustering score. Ink analysis is an important tool in forensic science and document analysis. Hyperspectral imaging (HSI) captures large number of narrowband images across the electromagnetic spectrum. HSI is one of the non-invasive tools used in forensic document analysis, especially for ink analysis. The substantial information from multiple bands in HSI images empowers us to make non-destructive diagnosis and identification of forensic evidence in questioned documents. The presence of numerous band information in HSI data makes processing and storing becomes a computationally challenging task. Therefore, dimensionality reduction and visualization play a vital role in HSI data processing to achieve efficient processing and effortless understanding of the data. In this paper, an advanced approach known as t-Distributed Stochastic Neighbor embedding (t-SNE) algorithm is introduced into the ink analysis problem. t-SNE extracts the non-linear similarity features between spectra to scale them into a lower dimension. This capability of the t-SNE algorithm for ink spectral data is verified visually and quantitatively, the two-dimensional data generated by the t-SNE showed a better visualization and a greater improvement in clustering quality in comparison with Principal Component Analysis (PCA).

In contrast to its European counterpart, Islamic papermaking is still little researched, especially in scientific and conservation contexts. This study presents the first in‐depth material analysis of a unique collection of Islamic‐African amulets and talismans from the nineteenth and twentieth centuries, held at the Slovene Ethnographic Museum. This research employed a multi‐analytical approach that included pH measurements, analysis of fibrous materials, iodine test for the presence of starch, hyperspectral imaging (HSI), FTIR‐ATR, Raman spectroscopy, laser‐induced fluorescence (LIF), and laser‐induced breakdown spectroscopy (LIBS), as well as cultural interpretations. Twelve selected manuscripts were examined to characterize paper, inks, dyes, and calligraphic features. The results showed the use of iron gall inks, plant‐based dyes, and mixed paper fibers (straw and softwood pulp), suggesting a mixture of local and imported materials from the colonial period. The calligraphic and decorative styles reflect a synthesis of orthodox Qur’an and local West African Sufi traditions, often incorporating protective texts, magic squares, and regional variants of Kufic script. The findings shed light on technological aspects of Islamic manuscript production in West Africa and support the informed conservation, display, and interpretation of these culturally and spiritually significant objects. This research sets a precedent for comparative heritage studies and enhances the understanding of Islamic material culture in African contexts. The image illustrates a multi‐technique analysis of Islamic‐African manuscripts from the nineteenth to twentieth centuries. It highlights methods like microscopy, hyperspectral imaging, FTIR, LIF, Raman, LIBS, and furnish analysis used to study paper fibers, inks, and dyes—revealing a blend of local and trade materials and a rich fusion of Qur’anic and West African traditions.

Fraudulent activities often involve document manipulation, which poses a significant challenge to forensic science. To address this issue, a novel method was developed that combines intended artificial UV pre-degradation, digital color analysis (DCA) of stroke images, and various machine learning (ML) models. This method can cluster blue ballpoint pen inks and predict their photodegradation time. The results of the study indicate that the k-shape clustering method is highly effective in differentiating between inks based on their degradation curve patterns and HSV or RBS color features, aligning well with results from chromatography analyses. Furthermore, the random forest regression model demonstrated superior performance in predicting age, exhibiting the highest coefficients of determination. The DCA-ML method is a straightforward, cost-effective, and highly accurate solution for clustering blue pen inks. Using photodegradation curves to predict document age could eliminate the need for conventional physicochemical analysis techniques. [Display omitted] •Identified distinct aging patterns for different blue ballpoint pen ink samples.•Developed the workflow for processing the obtained images based on ML.•Found two best feature sets (HSV, RBS) for clustering pen inks.•Proposed clustering aligned well with the chemical-based approach involving DCA.•Shown the superiority of the random forest regression model (R2 0.993–0.996).

The methods for perpetrating forgery and alteration of documents are becoming increasingly more sophisticated. Forensic examinations of questioned documents routinely involve physical and chemical analysis of inks. Raman spectroscopy is a very attractive technique for ink analysis because it combines chemical selectivity with ease and fast analysis and it does not require sample preparation nor leads to destruction of the evidence. However, some limitations of this technique include low sensitivity and the overwhelming phenomenon of fluorescence, which can be solved by resonance Raman spectroscopy and surface-enhanced Raman spectroscopy. This article aims to demonstrate the great potential of the Raman-based techniques by providing an overview of their application to forensic examinations of ink evidence from pens and printers. Moreover, it is also addressed the chemistry of ink-paper interactions and the problematic of intersecting lines.

Printer source prediction is an important task when examining questioned documents. While some research has provided methods to predict the source printer of documents, with the advent of compatible consumables, printer prediction could become more complex and difficult. Predicting the source printer after replacing cartridges and identifying the source of printer cartridges are unresolved issues that are rarely addressed in current research. Herein, we introduce a novel technique to predict the manufacturer, model, and cartridges of laser printers (i.e., compatible, and original cartridges) used to produce a given document. Document samples produced using eight laser printers and 247 cartridges were collected to establish a dataset. Common manufacturers included HP, Canon, Lenovo, and Epson. After obtaining white-light images and three-dimensional profile images of printed characters, a morphological analysis was conducted by questioned document examiners (QDEs) using microscopy. Microscopic image features across a series of images were also extracted and analyzed using algorithms. Then, six high-dimensional reduction algorithms were used to obtain between- and within-printer variations as well as between- and within-cartridge variations. Finally, we conducted principal component analysis (PCA) and discriminant analysis. For 40 % of the samples, mixed discrimination analysis (MDA) and fixed discrimination analysis (FDA) were employed to predict the manufacturer, model and cartridge of laser printers used to produce the questioned printed document; the remaining 60 % samples comprised the training dataset. In the prediction of manufacturer, model and cartridge, our method achieved mean accuracies of 95.5 %, 97.5 %, and 90.2 %, respectively. Hence, this technique could reasonably aid in predicting the manufacturer, model, and cartridge of a laser printer, even if different cartridges are loaded into printers. [Display omitted] •A novel technique was provided to predict the manufacturer, model, and cartridges of laser printers.•Three-dimensional profiling combined with white-light microscopic images helps to improve the performance of the prediction.•This paper expands document examination from two dimensions to three dimensions.

The paper presents the possibilities of using the ToF-SIMS technique in the examination of a range of samples as forensic evidence. These include the analysis of documents, the examination of writing media, the analysis of crossing lines, the analysis of cosmetics, hair analysis, the examination of automobile paints, and the analysis of fingerprints and their contamination with exogenous substances. The advantages and disadvantages of this method were analysed with reference to the information that any forensic investigator would wish to obtain when examining highly significant evidence. •ToF-SIMS provides an important imaging capability for forensic science.•The ability to analyse a wide range of forensic trace types.•The non-destructive technique with high sensitivity and resolution.•The technique with new applications, including the analysis of evidences.

The examination of stamp impressions is a routine task for questioned document examiners (QDEs) in determining the authenticity of disputed documents. Some studies have provided methods for date determination of stamp impressions, but each single method has its own limitations, so date determination of stamp impressions remains a challenge in case work. This study reports on the real case of a forged contract, in which pre-inked stamp impressions were made on blank paper without the knowledge of the stamp owner. The combination of Raman and video spectroscopy is demonstrated to be a novel method for the date determination of these impressions. In addition to conventional morphological examination methods, video spectroscopy (VS) images were combined with Raman spectroscopy to identify ink components and the distribution of pre-inked impressions. These characteristics vary with the time of stamping, especially after re-inking, and can thus be used to date forged documents. Specifically, spectral analysis of the ink provided a regular representation of temporal variations based on reference stamp samples, which were mutually confirmed by morphological analysis. This approach combines multiple analysis methods to provide diverse evidence for characterizing the tampering and dating the impressions. The results suggest the included stamp impressions were genuine and made on a blank sheet of paper prior to printing the content of the agreement. The five questioned impressions were not entirely consistent in terms of generation time, and none of them were stamped on the nominated date. [Display omitted] •Multiple analysis methods (Video spectrocopy, Raman spectroscopy, and morphological method) were used to provide diverse evidence.•Reporting the variation in inks distribution of pre-inked stamp impressions before and after re-inking.•The combination of Video and Raman spectroscopy is a potential method beneficial to dating the impressions.

Raman spectroscopy, complemented by advanced techniques such as surface-enhanced Raman spectroscopy (SERS) and micro-Raman spectroscopy, is increasingly expanding its forensic applications in analysing trace materials. Forensic practitioners are adopting it more widely due to its simplicity, speed, non-destructive nature, and minimal or no sample preparation required. Despite certain limitations, such as relatively low sensitivity and significant fluorescence interference, Raman spectroscopy has been greatly enhanced by advanced techniques like SERS and Resonance Raman Spectroscopy, which effectively mitigate these issues and help unlock the full potential of Raman analysis. This review compiles extensive data from three major databases—Web of Science, Scopus, and Google Scholar—to demonstrate the utility of Raman spectroscopic techniques in the forensic analysis of trace samples, particularly in the examination of inks, dyes, gunshot residues, paints, and related substances. The study shows that Raman spectroscopy has significantly simplified the analysis of forensic traces, offering a promising approach for the on-site examination of a wide range of traces with improved efficiency and accuracy. [Display omitted] •Raman Spectroscopy offers rapid, non-destructive analysis with minimal sample preparation.•Advanced methods like Resonance Raman Spectroscopy and Surface-Enhanced Raman Spectroscopy enhance sensitivity and mitigate interference.•Raman advances help detect trace materials like inks, dyes, gunshot residue, paints, and soils.•Its versatility and robustness enable reliable and efficient trace analysis.•It offers a promising method for on-site analysis of various forensic trace materials

•Raman spectroscopy and chemometrics were combined to differentiate 14 pen inks.•Spectral preprocessing removed the fluorescence contribution to Raman signal.•All inks were successfully differentiated by exploration and classification tools.•The relevant spectroscopic ranges to differentiate among inks were determined.•We report a fast, non-destructive and statistically sound method for ink analysis. The objective of this work is assessing whether the combination of Raman spectroscopy and chemometric tools is appropriate to differentiate blue ballpoint pen inks. Fourteen commercial blue ballpoint pen inks from different brands and models were studied and Raman spectra were obtained on ink lines written on A4 sulfite paper. First, a study of the best Raman configurations, in terms of laser intensity used and acquisition mode, was carried out to ensure sufficient spectroscopic quality without damaging the sample. Chemometric methods were applied first to improve the definition of spectral bands and to suppress fluorescence contributions from the signal. Once the spectra were suitably preprocessed, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied to explore whether the different inks could be distinguished from their Raman spectra. Almost all inks could be gradually differentiated, through successive PCA analyses or looking at the different levels of the dendrogram structure provided by HCA. From these exploratory results, a tree structure was constructed based on PCA and HCA results in order to reflect the degree of similarity among ink classes. This tree structure was used as the basis to develop hierarchical classification models based on partial least squares-discriminant analysis (PLS-DA). Correct classification of inks was achieved by these PLS-DA models built and the most important regions to identify the ink classes were detected using the variable importance in projection plots (VIPs). The combination of Raman spectroscopy and chemometrics has been proven to be a promising fast non-destructive tool to differentiate among very similar ink types in documents.

Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the interpretation of obtained spectra and identification of observed spectral lines. This procedure is highly time-consuming since it is essentially based on the comparison of lines present in the spectrum with the literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of quasi-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages), 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (decision trees, random forest, k-nearest neighbor, support vector machine, probabilistic neural network, multi-layer perceptron, and generalized regression neural network), 5-fold stratified cross-validation, and a test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08%, obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate.