Evaluation of natural ageing responses on Burmese amber durability by FTIR spectroscopy with PLSR and ANN models

Amber ageing is an inevitable process, which is very important in precious organic gemstone relics protection. In order to explore the mechanism of amber ageing and estimate the durability of Burmese amber, this research investigates the changing spectral features of Burmese ageing amber via Fourier Transform Infrared Spectroscopy (FTIR) and solid 13C Nuclear Magnetic Resonance spectroscopy (NMR) and develops the regression models for its micro-hardness by micro-FTIR spectra. The Partial Least Squares Regression (PLSR) and Artificial Neural Net-works (ANN) methods as well as Competitive Adaptive Reweighted Sampling (CARS) algorithm for wavelength variables selection have been applied to predict and assess the Vickers hardness of amber samples with different ageing degrees. As a result, the FTIR and the solid 13C NMR spectra reveal that the contents of C--O groups (of esters) increase substantially, and which of the other oxygenic groups (C--O (of acids), C-O-C, C-O-C--C) increase modestly in amber ageing. When comparing with the results of four different models (PLSR, ANN, CARS-PLSR and CARS-ANN), the CARS-PLSR model obtained the optimal results as follows: the squared correlation coefficient of calibration(R2cal) is 0.9230 and the root mean square error of calibration (RMSEC) is 1.2977 HV; the squared correlation coefficient of prediction (R2pre) is 0.7762 and the root mean square error of prediction (RMSEP) is 2.2208 HV. The overall results sufficiently demonstrate that FTIR spectroscopy technique coupled with appropriate chemometrics methods are very promising tools to estimate and predict the hardness property of Burmese ageing amber.

Automated summary

This research investigates the changing spectral features of ageing Burmese amber and develops regression models to predict its micro-hardness. The results show an increase in the content of C-O groups (of esters) during amber ageing. The CARS-PLSR model achieved optimal results for predicting the Vickers hardness of amber samples.