Establishing Support Vector Machine SVM Recognition Model to Identify Jadeite Origin
LI Hao-dong1, 2, LI Ju-zi1*, CHEN Yan-lin1, HUANG Yu-jing1, Andy Hsitien Shen1*
1. Gemmological Institute，China University of Geosciences（Wuhan），Wuhan 430074，China
2. Hubei Gems and Jewelry Engineering Technology Research Center，Wuhan 430074，China
Abstract：To realize the rapid and non-destructive identification of jadeite origins and enrich the diversity of methods for the identification of precious jadeite origins, a support vector machine SVM recognition model was established to analyze jadeite of three origins based on the data obtained from infrared spectral analysis. The experiments collected a total of 106 infrared spectral data of three jadeite species from Myanmar, Russia and Guatemala in order to achieve better model identification, the original infrared spectral data were transformed from reflectance to absorbance before modeling, and then the spectra were pre-processed differently. The purpose of preprocessing is to reduce the effects of noise, baseline drift and scattering phenomena on the model recognition effect. The methods used for preprocessing in this experiment are SG smoothing, mean centering, normalization, trend correction, multivariate scattering correction, maximum-minimum normalization, standard normal transformation and standard normal transformation followed by trend correction. The experimental results show that the recognition accuracy of the models obtained by preprocessing the infrared spectra is higher than that of the original spectra by 73%; the recognition accuracy of the models obtained by multivariate scattering correction and maximum-minimum normalization of the infrared spectra of the three emerald origins separately is higher than that of the results obtained by mixing preprocessing; some preprocessing methods used in combination also improve the recognition accuracy of the models, such as standard normal transform and trend correction. The recognition accuracy obtained after maximum-minimum normalization of the infrared spectra of the three origins of jadeite separately reached the highest 95%, indicating that this support vector machine SVM recognition model built using infrared spectroscopy can achieve rapid recognition of jadeite origins.
李浩东，李举子，陈彦霖，黄钰静，沈锡田. 建立支持向量机SVM识别模型对翡翠产地进行识别[J]. 光谱学与光谱分析, 2023, 43(07): 2252-2257.
LI Hao-dong, LI Ju-zi, CHEN Yan-lin, HUANG Yu-jing, Andy Hsitien Shen. Establishing Support Vector Machine SVM Recognition Model to Identify Jadeite Origin. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2252-2257.
