基于模式识别的X射线荧光光谱法用于土壤重金属快速检测

doi:10.3964/j.issn.1000-0593(2024)09-2692-09

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Abstract The rapid and precise detection of heavy metals in soil is the key to the efficacious prevention and remediation of soil contamination. Employing a portable X-ray fluorescence spectrometer facilitates the in-situ, non-destructive, and rapid detection of typical heavy metals. This advanced analytical technique also obviates the need for elaborate sample digestion procedures. However, the accuracy of the XRF-based heavy metal detection technique is significantly influenced by the soil matrix effects, which considerably limits the accuracy of such measurements. Calibration against standard soil with a similar matrix is imperative. As a result, this study combined pattern recognition and the standard curve method to achieve a precise analysis of typical heavy metals in various soils. The dataset comprises the X-ray fluorescence spectra and heavy metal contents across six characteristic soil types collected within China: humid-thermo ferritic, paddy soils, black soils, flavor-aquic soils, yellow-brown earth, and yellow-red earth. The spectral data is refined using a five-point, three-times window movement smoothing algorithm and a min-max normalization approach, followed by principal component analysis (PCA). Post-PCA dimensionality reduction's first five principal components are employed as input feature variables, with soil types serving as labels. A predictive model based on a Radial Basis Function (RBF) kernel for Support Vector Machine (SVM) is constructed to categorize soils by matrix similarity. The model's hyperparameters are optimized using the Horned lizard optimizer algorithm, yielding an optimized kernel function (g) of 0.038 1 and a penalty factor (c) of 7.852 9, with a correct classification rate of 100% under a five-fold cross-validation. The quantitative analysis utilizes the standard curve method. For the six soil types, the correlation coefficients for Chromium (Cr) ranged from 0.994 7 to 0.999 3, for Nickel (Ni) from 0.986 8 to 0.999 4, for Copper (Cu) from 0.992 9 to 0.999 9, for Zinc (Zn) from 0.984 1 to 0.999 8, and for Lead (Pb) from 0.987 7 to 0.999 6. Furthermore, the correlation coefficients of Arsenic and Lead (As & Pb) ranged from 0.961 3 to 0.999 5. The above results indicate a favorable linearity for heavy metals within the same matrix. Subsequently, the established RBF-SVM model and standard curves are applied to a prediction set of 24 samples. The predictive outcome corroborates a 100% classification accuracy for the six soil types. Upon classification, corresponding standard curves are utilized for quantitative analysis. The results show that the average relative prediction errors for Cr, Ni, Cu, Zn, Pb, and As are 2.24%, 3.66%, 2.72%, 2.15%, 2.13%, and 5.55%, respectively, below 6%. These findings prove the excellent applicability of the RBF-SVM model in combination with the standard curve method for the rapid detection of typical heavy metals in soil. This algorithm will facilitate the rapid quantitative detection of typical heavy metals in natural soil.

Key words：Soil;Heavy metals;Support vector machines;Pattern recognition;Rapid detection

Received: 2024-04-11 Accepted: 2024-07-09

ZTFLH:

X833

Corresponding Authors: ZHANG Chang-bo, TANG Xiao-yong E-mail: cbzhang2007@163.com; txy@ghs.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2024)09-2692-09 OR https://www.gpxygpfx.com/EN/Y2024/V44/I09/2692

[1] ZHANG Yi-shuo, ZHOU Zhong-kui, YANG Shun-jing, et al(张益硕, 周仲魁, 杨顺景, 等). Nonferrous Metals(Extractive Metallurgy)[有色金属(冶炼部分)], 2022, (10): 124.
[2] CHEN Si-long, YANG Yun-hua, MA Jing, et al(陈思龙, 杨运华, 马静, 等). Chinese Journal of Inorganic Analytical Chemistry(中国无机分析化学), 2023, 13(5): 433.
[3] CHEN Yun, YING Rong-rong, KONG Ling-ya, et al(陈云, 应蓉蓉, 孔令雅, 等). Soils(土壤), 2022, 54(3): 586.
[4] Romzaykina O N, Slukovskaya M V, Paltseva A A, et al. Journal of Soils and Sediments, 2024, 3: 20.
[5] TANG Xiao-yong, NI Xiao-fang, SHANG Zhao-cong, et al(唐晓勇, 倪晓芳, 商照聪, 等). Metallurgical Analysis(冶金分析), 2021, 41(1): 69.
[6] TANG Xiao-yong, NI Xiao-fang, SHANG Zhao-cong(唐晓勇, 倪晓芳, 商照聪). Rock and Mineral Analysis(岩矿测试), 2020, 39(3): 467.
[7] HUANG Qiu-xin, SUN Xiu-min(黄秋鑫, 孙秀敏). Environmental Science & Technology(环境科学与技术), 2014, 37(9): 92.
[8] CHENG Hui-zhu, YANG Wan-qi, LI Fu-sheng, et al(程惠珠, 杨婉琪, 李福生, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2023, 43(12): 3742.
[9] Zhang Q L, Li F S, Yang W Q, et al. Journal of Analytical Atomic Spectrometry, 2024, 39(2)：478.
[10] LI Fu-sheng, ZENG Xiao-long(李福生, 曾小龙). Laser & Optoelectronics Progress(激光与光电子学进展), 2023, 60(5): 381.
[11] YANG Gui-lan, NI Xiao-fang, TANG Xiao-yong(杨桂兰, 倪晓芳, 唐晓勇). Chinese Journal of Inorganic Analytical Chemistry(中国无机分析化学), 2023, 13(6): 530.
[12] JIANG Xiao-yu, LI Fu-sheng, WANG Qing-ya, et al(江晓宇, 李福生, 王清亚, 等). Metallurgical Analysis(冶金分析), 2021, 41(8): 7.
[13] KONG Wei-heng, ZENG Ling-wei, RAO Yu, et al(孔维恒, 曾令伟, 饶宇, 等). Rock and Mineral Analysis(岩矿测试), 2023, 42(4): 760.
[14] YANG Gui-lan, NI Xiao-fang, ZHANG Chang-bo(杨桂兰, 倪晓芳, 张长波). Acta Agriculturae Zhejiangensis(浙江农业学报), 2019, 31(11): 1903.
[15] YANG Gui-lan, SHANG Zhao-cong, LI Liang-jun, et al(杨桂兰, 商照聪, 李良君, 等). Acta Agriculturae Zhejiangensis(浙江农业学报), 2016, 28(12): 2123.
[16] Peraza-Vázquez H, Peña-Delgado A, Merino-Treviño M, et al. Artificial Intelligence Review, 2024, 57：59.