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Author / Affiliation / Email Article Menu Font Type: Arial Georgia Verdana Open AccessProceeding Paper by Daniela Eusébio 1,2, Vlad Tatarescu 1,3, Márcia Vieira 4, Carlos Família 1,2 and Alexandra Bernardo 1,2,* 1 Laboratório de Ciências Forenses e Psicológicas Egas Moniz, Monte de Caparica, 2829-511 Almada, Portugal 2 Egas Moniz Center for Interdisciplinary Research (CiiEM); Egas Moniz School of Health & Science, 2829-511 Almada, Portugal 3 Mestrado em Tecnologias Laboratoriais em Ciências Forenses, Egas Moniz School of Health and Science, Monte de Caparica, 2829-511 Almada, Portugal 4 Department of Conservation and Restoration and LAQV-REQUIMTE, Faculty of Sciences and Technology, NOVA University of Lisbon, 2829-511 Almada, Portugal * Author to whom correspondence should be addressed. † Presented at the 6th International Congress of CiiEM—Immediate and Future Challenges to Foster One Health, Almada, Portugal, 5–7 July 2023. Published: 14 August 2023 Abstract: The requirement for the preservation of evidence in court continues to pose a challenge to forensic document analysis, where the answer to the discrimination of writing utensils is recurrent in requested questions. This study aims to contribute to the identification of the blue and green inks of handwriting instruments using Raman spectroscopy. Based on the Raman spectra obtained and after processing with Spectragryph software, a predictive model was built using KNIME software, where a discriminative power of 72.7% was obtained. 1. IntroductionThe analysis of the ink of common writing instruments is one of the most relevant areas of modern criminalistics in document fraud since the specific characteristics of inks can answer identification, comparison, and/or dating questions in requested forensic examinations [1,2,3]. This work aims to contribute to the discrimination of blue and green inks from common writing instruments through a non-destructive technique in which the full analysis of Raman spectra is exploited with Knime software in order to build a predictive model. 2. Materials and MethodsEleven pens (five blue and six green) were selected from a collection of pens purchased on commercial surfaces. After signal Raman screening: pen 8: Unilakubo—SG100 Rubber Grip 0.7 mm; pen 10: Bic Crystal; pen 14: Bic—unidentifiable model; pen 32: Uni-ball Eye micro 0.5 mm; pen 39: Stabilo Sensor F 0.3 mm; pen 3: Papermate—Inkjoy 1.0 mm; pen 9: Bic Crystal; pen 51: Bic Crystal Soft Medium; pen 53: Papermate Inkjoy 1.0 mm; pen 54: Staples Stick Pens; pen 55: FlairZing—Color Flair 0.7 mm. The ink of each pen has been deposited in notebooks, created with

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CyberStitchers – An online chapter of the EGA

New patterns, in this case, for the “test” file. The Scorer, node 5, analyses the test and training files inserted in the previous nodes and provides the accuracy and error of the analysis. The following nodes (12, 13, 14, and 16) serve only to visualize the decision trees on an image.After creating the Decision Tree Learner, a second tree was generated with the same nodes, but with the objective of validation in real context. In node 1, the same training file was inserted (for each technique), and in node 3, the validation file containing the data of the one replicate that was not present in the training file was inserted.The predictive model created achieved a discriminatory power of 72.7% and an error of 27.3%. 4. DiscussionDespite the reduced number of replicates in each database, the present technique achieved values not much lower than those obtained in other studies with other techniques such as Micro-ATR-FTIR combined with chemometrics in which the discrimination power did not exceed 86.67% [6] and 89.99% [7], or in studies where multispectral imaging coupled with FCM was used and achieved discrimination percentages of 76% [8] and 85.7% [9]. 5. ConclusionsThe present pilot study allowed the development of a predictive model based on pen ink Raman spectra with a high discriminatory power of 72.7%. Author ContributionsConceptualization, A.B.; methodology, A.B. and M.V.; software, D.E. and C.F.; investigation, D.E. and V.T.; writing—original draft preparation, D.E.; writing—review and editing, A.B., M.V. and C.F.; supervision, A.B., M.V. and C.F. All authors have read and agreed to the published version of the manuscript.FundingThis research received no external funding.Institutional Review Board StatementNot applicable.Informed Consent StatementNot applicable.Data Availability StatementData are contained within the article.AcknowledgmentsLaboratório de Ciências Forenses e Criminais Egas Moniz and Departamento de Conservação e Restauro Faculdade de Ciências e tecnologias da Universidade Nova de Lisboa.Conflicts of InterestThe authors declare no conflict of interest.ReferencesCantu, A.; Ink Analysis, A. Wiley Encyclopedia of Forensic Sciences, 2nd ed.; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2009; pp. 375–379. [Google Scholar] [CrossRef]Ellen, D. The Scientific Examination of Documents, 2nd ed.; Taylor & Francis: Boca Raton, FL, USA, 1997. [Google Scholar]Ortiz-Herrero, L.; Bartolomé, L.; Durán, I.; Velasco, I.; Alonso, M.L.; Maguregui, M.I.; Ezcurra, M. DATUVINK pilot study: A potential non-invasive methodology for dating ballpoint pen inks using multivariate chemometrics based on their UV–vis-NIR reflectance spectra. Microchem. J. 2018, 140, 158–166. [Google Scholar] [CrossRef]Menges, F. Spectragryph. 2019. Available online:

CyberStitchers An online chapter of the EGA

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