Herramientas de Machine Learning para la clasificación de espectros visibles de reflectancia de pigmentos inorgánicos usados en pintura mural

Autores/as

DOI:

https://doi.org/10.37558/gec.v28i1.1444

Palabras clave:

clasificación, espectroscopía de reflectancia visible, identificación, máquinas de vector soporte, mínimos cuadrados parciales, pigmentos, quimiometría, redes neuronales artificiales

Resumen

Centrándose en la identificación no invasiva e in situ de pigmentos históricos mediante espectroscopía de reflectancia visible, se entrenan y evalúan clasificadores lineales y no lineales (Partial Least Squares – Discriminant Analysis (PLS-DA), Support Vector Machine (SVM-DA), y Artificial Neural Networks (ANN-DA)) aplicados al Patrimonio Cultural. Se consideran tres tonalidades (amarillo, azul y verde), con tres pigmentos por tonalidad: amarillo plomo-estaño, ocre amarillo, oropimente (amarillo); celadonita, malaquita verde, verde Verona (verde); y azul egipcio, azurita y lapislázuli (azul). Los tres métodos arrojaron alta capacidad predictiva (> 94,2 % en validación), con sensibilidades y especificidades por encima del 88 %. En casos en los que las características espectrales son más similares, PLS-DA se comportó ligeramente peor. Sin embargo, los modelos SVM-DA y ANN-DA fueron en todos los casos comparables con precisiones globales superiores al 98,8 %, sensibilidad superior al 88 % y especificidad superior al 98 %, respectivamente.

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Biografía del autor/a

Jordi Cruz, EUSS School of Engineering, Barcelona

Jordi Cruz is a University Associate Professor and lecturer at the EUSS School of Engineering in Barcelona since 2011. He holds an h-index of 11 and has authored over 30 scientific publications. He has supervised two PhD theses and one master’s student. His research focuses on multivariate analysis applied to rapid analytical techniques such as NIR, Raman, FTIR, and NIR chemical imaging spectroscopy. These methods are used to develop fast, reliable analytical tools across various industrial sectors, including pharmaceuticals, phytotherapy, petrochemicals, cultural heritage, and forensics.
Dr. Cruz conducts most of his research at the Agri-Food Technology Research Institute (IRTA) in Monells (Girona) and Forensics and Cultural Heritage at SmartChem Lab at the University of Valencia. He also maintains active collaborations with several international institutions: the Department of Pharmaceutical Chemistry at Mahidol University (Bangkok, Thailand), the Institute of Chemical Engineering at the Bulgarian Academy of Sciences (Sofia, Bulgaria), the University of the Basque Country, Teagasc in Ireland, and the Department of Forensic Chemistry at Florida International University (USA).

Roberto Sáez Hernández, Universitat de València

Roberto Sáez-Hernández is an Assistant Professor at the Analytical Chemistry department of the University of Valencia. His research focuses on the analytical treatment and interpretation of spectroscopic and imaging signals through chemometric exploratory and supervised methods. Particularly, he has focused his research on the fields of food science – delving into the investigation of food contamination and food fraud –, Cultural Heritage – investigating construction materials of relevant historical sites or ancient gold coinage –, and more recently Forensics. In this regard, he has carried out research stays in national and international universities and research facilities, like the Institute of Food Science and Technology (IATA – CSIC), and the Universities of Bologna, Autonomous of Barcelona, and Copenhagen

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Publicado

2025-12-15

Cómo citar

Cruz, J., & Sáez Hernández, R. (2025). Herramientas de Machine Learning para la clasificación de espectros visibles de reflectancia de pigmentos inorgánicos usados en pintura mural. Ge-conservacion, 28(1), 256–264. https://doi.org/10.37558/gec.v28i1.1444

Número

Vol. 28 Núm. 1 (2025): Ge-conservación Nº28

Sección

Monográfico

Licencia

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