19592a781ade98e6fab1ecf23f8fbedd chemosensors_a2025v13n7art232.pdf a1d1b0cfcaf65628ec886cd71c6e7c6c12750f7d chemosensors_a2025v13n7art232.pdf b37b267238bac140d7f1f1a2c3baf646b5c638de5d63d3185c9e3fcadfc554cf chemosensors_a2025v13n7art232.pdf Title: Predicting the Color of Archaeological Littorina obtusata/fabalis Shells Using Raman Spectroscopy and Clustering Algorithms Subject: Archaeological mollusk shells, such as those of Littorina obtusata/fabalis, hold valuable information about past human behavior and cultural practices. However, the original coloration of these shells, crucial for understanding their symbolic significance, is often lost due to taphonomic processes. Raman spectroscopy is a powerful technique for non-destructive analysis of archaeological samples, enabling the identification of pigments and mineralogical components. In this study, we present a methodology to predict, using Raman spectroscopy and k-means clustering, the original coloration of archaeological L. obtusata/fabalis shells which have lost their original coloration. Raman spectra were acquired from both modern shells, exhibiting a range of natural colors, and archaeological shell samples from La Chora cave (Cantabria, northern Spain). Spectral data were preprocessed to remove noise and baseline effects, and k-means clustering was applied to group the spectra based on their inherent spectral similarities. By comparing the spectral signatures of the archaeological samples with those of the modern shells within the generated clusters, we inferred the likely original coloration of the archaeological specimens. This approach provides a quantitative framework for predicting archaeological shell colors. Keywords: Raman spectroscopy; pigments; Littorina obtusata; Littorina fabalis; shell coloration; k-means Author: Andrea Perez-Asensio, María Gabriela Fernández-Manteca, David Cuenca-Solana, Igor Gutiérrez-Zugasti, Asier García-Escárzaga, Jesús Mirapeix, José Miguel López-Higuera, Luis Rodríguez-Cobo and Adolfo Cobo Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25 CreationDate: Wed Jun 25 13:55:51 2025 CEST ModDate: Wed Jun 25 14:04:10 2025 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 16 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 2473793 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- MIKRSI+VnURWPalladioL Type 1 Custom yes yes yes 10 0 VZFXIV+URWPalladioL-Roma Type 1 Custom yes yes yes 16 0 GZRLMR+URWPalladioL-Bold Type 1 Custom yes yes yes 22 0 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Identity-H yes yes yes 265 0 JFOLJF+PalatinoLinotype-Roman CID TrueType Identity-H yes yes yes 271 0 JFOLJG+PalatinoLinotype-Italic CID TrueType Identity-H yes yes yes 277 0 JFOLIF+PalatinoLinotype-Bold CID TrueType Identity-H yes yes yes 291 0 JFOLJF+PalatinoLinotype-Roman CID TrueType Identity-H yes yes yes 297 0 JFOLJG+PalatinoLinotype-Italic CID TrueType Identity-H yes yes yes 303 0 JFOLIF+PalatinoLinotype-Bold CID TrueType Identity-H yes yes yes 313 0 JFOLJF+PalatinoLinotype-Roman CID TrueType Identity-H yes yes yes 319 0 JFOLJG+PalatinoLinotype-Italic CID TrueType Identity-H yes yes yes 325 0 CRJHCU+PazoMath-Italic Type 1 Builtin yes yes yes 344 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2026-03-27 04:20:35 CET RepresentationInformation: chemosensors_a2025v13n7art232.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2026-03-26 19:35:29 CET Size: 2473793 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 482 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Materials and Methods Destination: section.2 Children: Item: Title: Sample Collection Destination: subsection.2.1 Item: Title: Sectioning, Polishing Procedure and Raman Spectra Acquisition for Cross-Sectional Analysis Destination: subsection.2.2 Item: Title: Raman Spectra Acquisition Destination: subsection.2.3 Item: Title: Spectral Data Analysis and Analysis Algorithms Destination: subsection.2.4 Children: Item: Title: Spectral Preprocessing Destination: subsubsection.2.4.1 Item: Title: Analysis Algorithms Destination: subsubsection.2.4.2 Item: Title: Results Destination: section.3 Children: Item: Title: Surface Preparation and Polishing Destination: subsection.3.1 Item: Title: Raman Spectra Analysis Destination: subsection.3.2 Item: Title: Pigment Distribution in Polished Shell Sections Destination: subsection.3.3 Item: Title: Clustering Analysis of Raman Spectra Destination: subsection.3.4 Item: Title: Discussion Destination: section.4 Item: Title: Conclusions Destination: section.5 Item: Title: References Destination: appendix.A. Info: Title: Predicting the Color of Archaeological Littorina obtusata/fabalis Shells Using Raman Spectroscopy and Clustering Algorithms Author: Andrea Perez-Asensio, María Gabriela Fernández-Manteca, David Cuenca-Solana, Igor Gutiérrez-Zugasti, Asier García-Escárzaga, Jesús Mirapeix, José Miguel López-Higuera, Luis Rodríguez-Cobo and Adolfo Cobo Subject: Archaeological mollusk shells, such as those of Littorina obtusata/fabalis, hold valuable information about past human behavior and cultural practices. However, the original coloration of these shells, crucial for understanding their symbolic significance, is often lost due to taphonomic processes. Raman spectroscopy is a powerful technique for non-destructive analysis of archaeological samples, enabling the identification of pigments and mineralogical components. In this study, we present a methodology to predict, using Raman spectroscopy and k-means clustering, the original coloration of archaeological L. obtusata/fabalis shells which have lost their original coloration. Raman spectra were acquired from both modern shells, exhibiting a range of natural colors, and archaeological shell samples from La Chora cave (Cantabria, northern Spain). Spectral data were preprocessed to remove noise and baseline effects, and k-means clustering was applied to group the spectra based on their inherent spectral similarities. By comparing the spectral signatures of the archaeological samples with those of the modern shells within the generated clusters, we inferred the likely original coloration of the archaeological specimens. This approach provides a quantitative framework for predicting archaeological shell colors. 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