Analysis Techniques and Optimization Models for the Determination of Au in Ores by Atomic Absorption Spectroscopy
WANG Peng1, 2, MEN Qian-ni1, 2, GAN Li-ming1, 2, BAI Jin-feng4, WANG Xiao1, 2, JING Bin-qiang1, 2, KOU Shao-lei1, 2, LIU Hui-lan5, HE Tao1, 2*, LIU Jiu-fen3, 6*
1. Xi'an Center of Mineral Resources Survey，China Geological Survey, Xi'an 710100，China
2. Technology Innovation Center for Gold Ore Exploration, Xi'an Center of Mineral Resources Survey，China Geological Survey, Xi'an 710100，China
3. Natural Resources Comprehensive Survey Command Center, China Geological Survey, Beijing 100055，China
4. Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences, Langfang 065000，China
5. China Mining News Agency, Beijing 100037, China
6. China University of Geosciences (Beijing), Beijing 100083, China
Abstract：Gold (Au) in ores typically appears in the form ofgranular gold or lattice gold and occurs in different states in different types of gold ores. The particle effect caused by its unique malleability challenges the analysis technique for gold samples. The preparation process involves numerous difficult-to-control factors, directly impacting the accuracy and stability of gold analysis. Taking the gold element in ores as the research object, an analytical method for determining gold in ores using the foam plastic adsorption-atomic absorption spectroscopy technique is established. Qualitative judgments and quantitative calculations are made for the key factors in the sample testing process, and an optimization model based on grey relational analysis-response surface methodology is proposed. The important steps in the sample analysis technique, such as roasting, digestion, enrichment, and elution, are discussed. The optimization factors of the roasting method, digestion acidity, enrichment time, and thiourea concentration are determined. An orthogonal experiment is designed, and correlation analysis is carried out. Grey relational coefficients are calculated, and range analysis is used to qualitatively determine the significance of each optimization factor. A significant level table is constructed by combining central composite design with response surface methodology. A predictive model is established using a quadratic polynomial regression equation, and significance analysis is performed. Three-dimensional response surface plots and two-dimensional contour plots are used to fit and analyze the experimental data, determining the optimal parameters of the optimization model as follows: roasting method stepwise segmented roasting, digestion acidity-10.58%, enrichment time-40.00 min, and thiourea concentration-11.65 g·L-1. Experimental results indicate that under the conditions of the optimized model, when preparing and testing national first-class standard gold ore materials, the method has a detection limit of 0.021, a determination limit of 0.077, and spike recovery rates ranging from 91.6% to 104.5%. The accuracy and precision meet the requirements of GB/T 20899.2—2019 quality control. Furthermore, method validation and comparison are performed on external samples from the Xiqinling area in Shaanxi and the Meichuan area in Gansu. The relative deviation does not exceed 10% for all cases, and the evaluation results are excellent, indicating that the proposed detection optimization model remains accurate and reliable for practical samples, demonstrating correctness and scientific validity. This study presents a new method for the rapid, accurate, and convenient analysis of the Au element in geological and mineral laboratories, providing new ideas for optimizing multi-objective parameter combinations in inspection and testing. It also contributes to accurately testing the new round of strategic mineral exploration.
Key words：Gold; Atomic absorption spectroscopy; Grey correlation degree;Response surface methodology; Center combination design; Quality control
王 鹏，门倩妮，甘黎明，白金峰，王 啸，井斌强，寇少磊，刘慧蓝，何 涛，刘玖芬. 原子吸收光谱法测定矿石中Au的分析技术及最优化模型研究[J]. 光谱学与光谱分析, 2025, 45(02): 426-433.
WANG Peng, MEN Qian-ni, GAN Li-ming, BAI Jin-feng, WANG Xiao, JING Bin-qiang, KOU Shao-lei, LIU Hui-lan, HE Tao, LIU Jiu-fen. Analysis Techniques and Optimization Models for the Determination of Au in Ores by Atomic Absorption Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(02): 426-433.
[1] Fu S, Qiu M, Shi L Q, et al. Minerals, 2023, 13(9): 1125.
[2] Kjarsgaard B A, de Wit M, Heaman L M, et al. Reviews in Mineralogy and Geochemistry, 2022, 88(1): 1.
[3] Ni Wenshan, Zhang Hongli, Mao Xiangj, et al. Microchemical Journal, 2020, 158: 105197.
[4] Stachel T, Cartigny P, Chacko T, et al. Reviews in Mineralogy and Geochemistry, 2022, 88(1): 809.
[5] Boulemia S, Hadji R, Bouhlal S, et al. Mineralogia, 2023, 54(1): 89.
[6] HE Tao, WANG Peng, MEN Qian-ni, et al(何 涛，王 鹏，门倩妮，等). Chinese Journal of Inorganic Analytical Chemistry(中国无机分析化学), 2024, 14(4): 429.
[7] Xu Rui, Li Qian, Meng Feiyu, et al. Metals, 2020, 10(9): 1216.
[8] Mendoza D M, Ichinose H, Konadu K T. Journal of Environmental Chemical Engineering, 2021, 9(6): 106375.
[9] SHI Yong-peng, FAN Wen-xin, SANG Man-xian, et al(史永鹏，樊文欣，桑满仙，等). Foundry Technology(铸造技术), 2017, 38(9): 2261.
[10] WANG Yue, HAO Jin-ming, LIU Wei-ping(王 月，郝金明，刘伟平). Acta Electronica Sinica(电子学报), 2020, 48(12): 2352.
[11] Wang Jingshu, Chen Tao, Kong Dongdong. Mechanical Systems and Signal Processing, 2023, 194: 110282.
[12] Lin Wanju, Lo S H, Hung C L. Applied Sciences, 2019, 9(7): 1462.
[13] ZI Dan, WANG Fu-jun, YAO Zhi-feng, et al(资 丹，王福军，姚志峰，等). Journal of Hydraulic Engineering(水利学报), 2017, 48(5): 594.
[14] XING Yi, WANG Zhi-qiang, HONG Chen, et al(邢 奕，王志强，洪 晨，等). China Environmental Science(中国环境科学), 2014, 34(11): 2866.
[15] WANG Peng, GAO Yong-bao, KOU Shao-lei, et al(王 鹏，高永宝，寇少磊，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2023, 43(10): 3117.
[16] Hannachi Y, Hafidh A. Journal of Saudi Chemical Society, 2020, 24(7): 505.
[17] Aladpoosh R, Montazer M. Carbohydrate Polymers, 2016, 141(5): 116.
[18] Petrova K V, Baranovskaya V B, Korotkova N A. Arabian Journal of Chemistry, 2022, 15(1): 103501.
