PAPER making! g! FROM THE PUBLISHERS OF PAPER TECHNOLOGY INTERNATIONAL ® Volume 11, Number 1, 2025
into LIBS analysis of common writing/printing papers, considering both the laser depth profiling and surface measurements. The sample set consisted in 14 papers (copy, notebook and envelope papers), some of them made of recycled materials. Examination of the laser induced craters showed a preferential ablation of paper fillers while the cellulose fibres were displaced at surface and swallowed around the centre of laser spot. Beside material inclusions, found both on the paper surface and in depth, the not uniform ablation also contributes to large intensity fluctuations of the element's line intensities, which standard deviations differ from one sample to another. By studying correlations between the line intensities from different elements at paper surface and in depth, it was possible to distinguish some top coatings (e.g., kaolinite) from bulk fillers (e.g., containing alumina- silicates), to hypothesize the use of NaOH in the industrial processing, as well as to exclude presence of certain types of fillers or coatings. Raman spectroscopy was performed both on sample surfaces and inside the laser induced craters (bulk material), showing the differences among papers regarding relative contents of CaCO 3 filler, lignin, and cellulose. We tested different chemometric models based on the LIBS measured composition of paper bulk or surface, obtaining up to 100% correct classification for six similar copy papers of Italian manufacture. “ Evaluating the performance of machine learning and variable selection methods to identify document paper using infrared spectral data ”, Yong Ju Lee, Soon Wan Kweon, Chang Woo Jeong & Hyoung Jin Kim, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy , Vol.327, 15 Feb. 2025, 125299. Infrared spectroscopy is a valuable tool for forensic examinations because it realizes nondestructive and rapid analysis. Recent advancements in machine learning have facilitated the development of chemometrics, extending to applications in questioned document examination. In this study, support vector machine (SVM), feedforward neural network (FNN), and random forest (RF) models were constructed using the infrared spectral data of document paper samples to identify the manufacturer of document paper products. For model training, the infrared (IR) spectral regions were selected based on their variable importance as determined by the RF models. Narrowing the IR spectral data within the range of 1500 – 800cm −1 (selected according to variable importance measures) proved effective in terms of enhancing model performance while minimizing computational costs. The FNN and RF models trained on the second-derivative IR spectra in this range obtained F1-scores of 0.978 and 1.000, respectively. The findings of this study confirm the potential of machine learning methods for extracting and examining forensic features in document paper, resulting in robust models with low computational overhead. TISSUE “Dimensional Analysis of Absorbency in Paper Towels: A Study of Three - and Two- Dimensional Mechanisms”, Soon Wan Kweon , Na Young Kang & Hyoung Jin Kim, BioResources , Vol 20(1), p683, 2025. The dimensional absorbency properties of paper towels were studied, focusing on three- and two-dimensional absorption mechanisms. Key factors affecting these absorption mechanisms were identified through a series of experiments and principal component analysis (PCA). The results showed that the water absorption capacity, driven by capillary action (porosity), exhibited differences between two- dimensional surface absorption (in the X and Y directions) and three-dimensional bulk absorption (including the Z direction, or thickness). Porosity analysis revealed that three- dimensional absorbency is highly correlated with porosity, whereas two-dimensional absorbency has a relatively low correlation and is influenced by fiber properties such as length and width, as well as mass-related characteristics including fines content and freeness. The findings highlight the need to balance these dimensional properties to achieve
Technical Abstracts
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