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Gamma spectral analysis by artificial neural network coupled with Monte Carlo simulations
(Elsevier, 2020) Sahiner, Huseyin; Liu, Xin
Neutron activation analysis has been widely used for quantitative analysis. It can quantify elements in parts per million or billion. Artificial neural network is an attractive technique to analyze complex gamma spectra obtained from neutron activation. This study offers an improved methodology to analyze neutron activation gamma spectra using an artificial neural network. The methodology was demonstrated by quantifying five trace elements (Br, Na, Zn, K, Au) in common kidney stones. First, Monte Carlo simulations were used to create a large training data set. Then, an artificial neural network was employed for chemical elements identification analysis. For quantitative analysis, a Levenberg-Marquardt algorithm with 5 - 23 - 5 structure artificial neural network was used. The artificial neural network for analysis of simulated gamma spectra resulted in estimated element concentrations. The differences between true and estimated concentrations are 1.8% for Br, 3.4% for Na, 5.4% for Zn, 2.8% for K, and 1.6% for Au. For real gamma spectral analysis, the largest difference was found to be 28.2% for Zn in a calcium oxalate monohydrate type of kidney stones.