Determination of charring conditions of archaeological grape seeds, towards machine-learning based classification

Author:

Reuveni Yuval1,Landa Vlad1,Shapira Yekaterina1,Behar Adi1,Ben-Arie Reut2,Weiss Ehud3,Drori Elyashiv4

Affiliation:

1. Ariel University

2. Civil Administration of Judea and Samaria

3. Bar Ilan University

4. Eastern regional R&D Center,

Abstract

Abstract This study investigates the chemical and morphological changes in grape pips resulting from various charring conditions. Employing Fourier Transform Infrared Spectroscopy (FTIR) for chemical analysis and high-resolution scanning combined with morphometric measurements for morphological analysis, we aimed to understand the effects of charring on grape pips. Our hypothesis regarding the potential of chemical composition for distinguishing charring temperatures was partially supported, as FTIR analysis revealed distinctive changes in chemical bonds at different temperatures. However, FTIR spectra of archaeological seeds showed limited utility for identification due to postdeposition alterations masking the original chemical fingerprint. In addition, morphometric analysis demonstrated significant alterations in seed shape above 250℃, corroborating FTIR findings. The length:width ratio and the occurrence of cracks notably changed, providing a basis for assessing charring conditions. Applying a machine learning classification method, we determined that accurate classification of grape varieties by the morphometric analysis method is feasible for seeds charred at up to 250℃ and 8 hours. Integrating the morphometric changes and temperature ranges suitable for classification, we developed a sorting model for archaeological seeds. By projecting length:width ratios onto a curve calculated from controlled conditions, we estimated charring temperatures. Approximately 50% of archaeological seeds deviated from the model, indicating drastic charring conditions. This sorting model facilitates a stringent selection of seeds fit for classification, enhancing the accuracy of our machine learning-based methodology. In conclusion, combining machine learning with morphometric sorting enables the identification of charred grape seeds suitable for identification by the morphometric method. This comprehensive approach provides a valuable tool for future research for the identification of charred grape seeds found in archaeological contexts, enhancing our understanding of ancient viticulture practices and grape cultivation.

Publisher

Research Square Platform LLC

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