Comparison of semiquantitative methodologies using Raman mapping for corrosion products on iron artifacts

Abstract

The quantitative determination of corrosion products is essential for evaluating the corrosion state of archeological iron artifacts. This study explored two semiquantitative approaches using Raman mapping technique combined with chemometrics to quantify a binary mixture of magnetite and goethite. The first approach involved establishing quantification models using Raman spectra treated with Principal Components Regression (PCR) and Partial Least Squares (PLS) algorithms. Both the PCR and PLS model showed good predictive ability as indicated by correlation coefficients and root mean square errors (PLS: Rc2=0.9979, Rp2=0.9970, RMSEC=1.93, RMSEP =2.68, RMSECV=3.25). The second approach was based on spectral fitting using non-negative least squares (NNLS) algorithm. This method demonstrated a fair accuracy between the calculated and actual compositions. The absolute value of relative errors was 0.99%∼10.08% for Fe3O4 and 0.81%∼9.71% for α-FeOOH respectively, for compositions greater than 20%. These methods were then applied to quantify the corrosion products on an iron bar excavated from the Nanhai (South China Sea) No. I shipwreck. Compared with XRD quantitative results, Raman results showed that the spectral fitting method was superior to the established PCA and PLS quantification models in both qualitative determination and quantitative accuracy. Compositional maps depicting the distribution of different phases were also generated using spectral fitting method. It was concluded that Raman mapping has significant potential as an accurate quantitative method for the detection of iron corrosion products, and that the spectral fitting method is more suitable for determining iron corrosion compared to the PCR and PLS quantification models in this study.