Determination of Multi-components in Marine Sediments by Core Scanner Based on BP Neural Network of Genetic Algorithm
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摘要: 海洋沉积物样品成分复杂，由于基体效应的影响，利用岩心扫描仪开展X射线荧光光谱分析只能得到目标元素的强度信息，不利于该方法在成矿机制和古环境等研究领域更好地发挥作用。本文采用岩心扫描仪测定海洋沉积物中的铝硅钾钙钛锰铁钒铬铜锌铷锶钇和铅15种元素，尝试引入BP神经网络模型利用其非线性拟合能力校正基体效应。实验表明，以水系沉积物、海洋沉积物和岩石国家标准物质以及定值海洋沉积物样品为训练样本，采用遗传算法优化BP神经网络的初始权值和偏置，可以有效校正除硅之外的14种元素基体效应的影响，实现了岩心扫描仪XRF测量结果由强度到浓度的转化。本方法的精密度为0.6%～6.8%（RSD, n=11），国家标准物质和海洋沉积物实际样品中15种组分的预测值与参考值的相对偏差在0.5%～17.5%之间，适合于海洋沉积物中多种主次量组分的快速分析，拓展了岩心扫描仪的功能。Abstract: Marine sediments have complex components. Due to the influence of the matrix effect, intensities of elements can only be acquired when using a Core Scanner to carry out X-ray Fluorescence Spectrum analysis, which restricts its application in the fields of paleoecology and mineralization. A method has been introduced for the fast determination of Al2O3, K2O, CaO, TiO2, MnO, Fe2O3, V, Cr, Cu, Zn, Rb, Sr, Y and Pb in marine sediments by Core Scanner, the effects of back-propagation neural network on correcting the nonlinear matrix effects have been investigated and are presented in this paper. Experimental results show that using national certified reference materials of stream sediments, marine sediments and rocks as training samples, a genetic algorithm is used to optimize the initial weight and bias of BP neural network. The matrix effect of 14 elements except Si was corrected by the GA-BP neural network method, which converts the Core Scanner X-ray Fluorescence Spectrum output results from intensities to concentrations. The relative standard deviations of this method are 0.6%-6.8% (n=11). The relative deviations between the predicted values and the reference values of the 15 components of the national standard materials and marine sediment samples range from 0.5% to 17.5%. This indicates that the proposed method is suitable for fast analysis of multi-components in marine sediments, extending the functions of the Core Scanner.
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Key words:
- genetic algorithm /
- neural network /
- core scanner /
- marine sediments /
- matrix effect
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表 1 方法精密度
Table 1. Precision tests of the method
组分 各组分含量分次测定值(%) 平均值
(%)RSD
(%)Al2O3 10.46 10.73 10.76 10.66 10.48 10.55 10.66 10.61 1.1 SiO2 65.47 64.50 64.42 65.11 65.35 65.18 65.05 65.01 0.6 K2O 2.01 1.97 1.98 1.97 2.03 2.01 1.98 1.99 1.2 CaO 5.31 5.38 5.43 5.37 5.31 5.30 5.32 5.35 0.9 TiO2 0.90 0.90 0.94 0.90 0.91 0.93 0.92 0.91 1.8 MnO 0.082 0.079 0.080 0.078 0.079 0.078 0.080 0.079 1.8 Fe2O3 4.81 4.90 4.93 4.89 4.87 4.85 4.84 4.87 0.8 组分 各组分含量分次测定值(μg/g) 平均值
(μg/g)RSD
(%)V 97.7 102 95.2 99.6 101 96.6 98.4 98.6 2.4 Cr 84.5 82.1 81.9 87.2 83.6 85.5 82.3 83.9 2.4 Cu 31.1 29.9 30.7 31.6 33.4 34.0 31.1 31.7 4.7 Zn 78.9 81.8 76.0 77.7 77.5 78.2 80.4 78.6 2.5 Rb 80.9 80.1 81.3 82.3 79.5 78.4 79.5 80.3 1.6 Sr 169 170 168 167 165 157 166 166 2.6 Y 28.4 27.2 26.5 24.3 27.5 24.0 27.1 26.4 6.3 Pb 20.6 22.5 23.3 23.6 20.9 25.0 23.1 22.7 6.8 
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表 2 标准物质的预测结果
Table 2. The explication results of elements in national standard materials
组分 GBW07301a GBW07304a GBW07305 GBW07314 预测值
(%)标准值
(%)相对误差
(%)预测值
(%)标准值
(%)相对误差
(%)预测值
(%)标准值
(%)相对误差
(%)预测值
(%)标准值
(%)相对误差
(%)Al2O3 15.50 15.36 0.9 10.82 10.94 -1.1 15.24 15.37 -0.8 12.93 13.07 -1.1 SiO2 58.10 59.07 -1.6 71.56 73.85 -3.1 54.76 56.44 -3.0 60.22 61.91 -2.7 K2O 2.75 2.80 -1.8 1.54 1.51 2.0 2.1 2.11 -0.5 2.5 2.48 0.8 CaO 4.06 4.00 1.5 0.84 0.82 2.4 5.39 5.34 0.9 4.37 4.31 1.4 TiO2 0.93 0.90 3.3 0.86 0.90 -4.4 0.87 0.90 -3.3 0.84 0.83 1.2 MnO 0.13 0.12 8.3 0.14 0.13 7.7 0.14 0.15 -6.7 0.093 0.096 -3.1 Fe2O3 6.60 6.50 1.5 4.63 4.55 1.8 5.88 5.84 0.7 5.43 5.36 1.3 组分 预测值
(μg/g)标准值
(μg/g)相对误差
(%)预测值
(μg/g)标准值
(μg/g)相对误差
(%)预测值
(μg/g)标准值
(μg/g)相对误差
(%)预测值
(μg/g)标准值
(μg/g)相对误差
(%)V 121 115 5.2 94.7 99.0 -4.3 115 109 5.5 101 103 -1.9 Cr 123 128 -3.9 77.4 70.0 10.6 79.2 70.0 13.1 82.4 86.0 -4.2 Cu 30.8 28.0 10.0 30.3 33.0 -8.2 131 137 -4.4 28.6 31.0 -7.7 Zn 86.1 90.0 -4.3 136 139 -2.2 252 243 3.7 82.7 87.0 -4.9 Rb 122 126 -3.2 91.4 89.0 2.7 114 118 -3.4 113 109 3.7 Sr 510 486 4.9 148 143 3.5 205 204 0.5 156 150 4.0 Y 25.2 22.0 14.5 25.5 29.0 -12.1 23.4 26.0 -10.0 24.2 27.0 -10.4 Pb 27.6 31.0 -11.0 72.8 68.0 7.1 117 112 4.5 21.4 25.0 -14.4
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表 3 海洋沉积物实际样品的预测结果
Table 3. The explication results of elements in marine sediment samples
组分 样品ZJ-1 样品ZJ-2 样品ZJ-3 样品ZJ-4 预测值
(%)参考值
(%)相对偏差
(%)预测值
(%)参考值
(%)相对偏差
(%)预测值
(%)参考值
(%)相对偏差
(%)预测值
(%)参考值
(%)相对偏差
(%)Al2O3 13.75 13.92 -1.2 8.40 8.29 1.3 15.40 15.18 1.4 9.79 10.02 -2.3 SiO2 46.13 45.11 2.3 55.32 56.80 -2.6 50.74 52.07 -2.6 38.24 37.54 1.9 K2O 3.32 3.37 -1.6 1.05 1.11 -5.4 3.42 3.39 1.0 1.59 1.57 1.5 CaO 2.56 2.51 2.0 4.97 5.00 -0.6 3.37 3.42 -1.4 18.85 19.07 -1.2 TiO2 0.75 0.70 7.0 0.38 0.35 7.4 1.48 1.57 -5.7 0.40 0.44 -9.9 MnO 1.30 1.34 -2.8 1.07 1.01 5.7 0.76 0.73 4.1 0.13 0.12 10.2 Fe2O3 8.20 8.09 1.3 4.14 4.19 -1.2 9.61 9.84 -2.4 3.88 3.71 4.5 组分 预测值
(μg/g)参考值
(μg/g)相对偏差
(%)预测值
(μg/g)参考值
(μg/g)相对偏差
(%)预测值
(μg/g)参考值
(μg/g)相对偏差
(%)预测值
(μg/g)参考值
(μg/g)相对偏差
(%)V 132 123 7.3 64 68 -5.9 193 200 -3.5 92.4 98.6 -6.3 Cr 48.6 54.4 -10.7 96.2 99.7 -3.5 38.2 32.5 17.5 68.4 76.6 -10.7 Cu 313 316 -1.0 281 287 -2.1 413 402 2.7 64.0 57.7 10.9 Zn 121 125 -3.2 160 154 3.9 231 228 1.3 95.0 90.6 4.9 Rb 103 111 -7.2 68.3 76.5 -10.7 90.4 94.6 -4.4 64.0 60.2 6.3 Sr 366 382 -4.2 428 409 4.6 319 303 5.3 622 640 -2.8 Y 333 340 -2.1 231 246 -6.1 181 169 7.1 39.0 34.7 12.4 Pb 46.2 52.1 -11.3 61.7 56.4 9.4 37.4 32.6 14.7 23.20 26.6 -12.8
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