LIBS结合VDPSO-CMW算法的高粱Na和Fe定量方法研究

根的金属元素含量对高粱生长过程有重要影响。激光诱导击穿光谱(LIBS)是快速检测作物金属元素的理想技术。建立了一套基于激光诱导击穿光谱与变维粒子群优化和组合移动窗口(VDPSO-CMW)的波长选择算法相结合的高粱根部金属元素定量分析方法。获得不同Na和Fe浓度积累的高粱样本27份。针对高粱根部的LIBS光谱, 利用VDPSO-CMW算法筛选与Na和Fe元素相关的特征波段, 并构建PLS定量分析模型。经VDPSO-CMW算法优化后, 高粱根部Na元素的PLS模型的建模结果交叉验证决定系数(R2CV)为0.962, 相比优化前的模型上升了6.5%, 交叉验证均方根误差(RMSECV)为1.261, 相比优化前模型下降了37.7%, 预测决定系数(R2P)为0.988, 相比优化前的模型上升了16.8%, 预测均方根误差(RMSEP)为1.063, 相比优化前的模型下降了72.1%; 经VDPSO-CMW算法优化后的高粱根部Fe校正模型的R2CV为0.956, 相比优化前的模型上升了7.4%, RMSECV为5.095, 相比优化前的模型下降了37.1%, R2P为0.955, 相比优化前的模型上升了4.3%, RMSEP为6.438, 相比优化前的模型下降了27.3%。结果表明, VDPSO-CMW波长选择算法能够剔除LIBS受自吸收、谱线干扰等因素的波段, 提高定量分析准确度。该算法和LIBS技术的结合不仅能够实现高粱根部Na和Fe元素的快速精确测定, 也适用于其他样本和元素的定量分析。

Abstract

The content of metal elements in the root influences the growth of sorghum. Laser-Induced Breakdown Spectroscopy (LIBS) is an ideal technology for rapidly detecting metal elements in crops.In this paper, a quantitative analysis method of metal elements in sorghum roots was established based on laser-induced breakdown spectroscopy and wavelength selection algorithm based on variable dimension particle swarm optimization-combined moving window (VDPSO-CMW). We collected 27 sorghum samples with different Na and Fe concentrations under sodium salt stress. For LIBS spectra of sorghum roots, the VDPSO-CMW algorithm was used to screen the characteristic bands related to Na and Fe, and PLS quantitative analysis model was constructed. After VDPSO-CMW algorithm optimization, the determination coefficient of cross validation (R2CV) of the PLS model for Na in sorghum root was 0.962, which was 6.5% higher than that before optimization. The root means square error of cross validation (RMSECV) was 1.261, which was 37.7% lower than thatbefore optimization; the determination coefficient of prediction (R2P) was 0.988, which was 16.8% greater than that before optimization. While the root means square error of prediction (RMSEP) was 1.063, which was 72.1% lower than that before optimization. After VDPSO-CMW algorithm optimization, the R2CV of the PLS model for Fe in sorghum root was 0.956, which was 7.4% higher than that before optimization; the RMSECV was 5.095, which was 37.1% lower than that before optimization; the R2P was 0.955, which was 4.3% higher than that before optimization; while the RMSEP was 6.438, which was 27.3% lower than that before optimization. The results show that the VDPSO-CMW wavelength selection algorithm can eliminate the LIBS bands affected by self-absorption, spectral line interference, and other factors and improve the accuracy of quantitative analysis. The combination of this algorithm and LIBS technology can not only realize the rapid and accurate determination of Na and Fe in sorghum roots but may also apply to the quantitative analysis of other samples and elements.

参考文献

[1] Khoddami Alli, Messina Valeria, Venkata Komala Vadabalija, et al. Critical Reviews in Food Science and Nutrition, 2021, 6: 1.

[2] Chugool Jiraporn, Naito Hitoshi, Kasuga Shigemitsu, et al. Plant Production Science, 2013, 16(3): 261.

[3] Millar S, Gottlieb C, Günther T, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2018, 147: 1.

[4] Cui Minchao, Deguchi Yoshihiro, Yao Changfeng, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2020, 167: 105839.

[5] KhumaeniAli, Akaoka Katsuaki, Miyabe Masabumi, et al. Optics Communications, 2021, 479: 126457.

[6] Wu Jian, Qin Yan, Li Xingwen, et al. Journal of Physics D: Applied Physics, 2020, 53: 023001.

[7] Zhang Ying, Zhang Tianlong, Li Hua. Spectrochimica Acta Part B: Atomic Spectroscopy, 2021, 181: 106218.

[8] Mei Yaguang, Cheng Shusen, Hao Zhongqi, et al. Plasma Science and Technology, 2019, 21: 034020.

[9] Yu Keqiang, Ren Jie, Zhao Yanru, et al. Artificial Intelligence in Agriculture, 2020, 4: 127.

[10] Sun D, Su M, Dong C, et al. Plasma Science and Technology, 2010, 12(4): 478.

[11] Kumar R, Tripathi D K, Devanathan A, et al. Spectroscopy Letters, 2014, 47(7): 554.

[12] ZHANG Da-cheng, MA Xin-wen, ZHU Xiao-long, et al (张大成, 马新文, 朱小龙, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2009, 29(5): 1189.

[13] Sharma N, Singh V K, Lee Y, et al. Atomic Spectroscopy, 2020, 41(6): 234.

[14] Han W, Su M, Sun D, et al. Plasma Science and Technology, 2020, 22(8): 085501.

[15] David W Hahn, Nicoló Omenetto. Applied Spectroscopy, 2012, 66: 347.

[16] Peng Jiyu, He Yong, Zhao Zhangfeng, et al. Environmental Pollution, 2019, 252: 1125.

[17] Guo Guangmeng, Niu Guanghui, Shi Qi, et al. Analytical Methods, 2019, 11: 3006.

[18] YAO Shun-chun, LU Ji-dong, XIE Cheng-li, et al(姚顺春, 陆继东, 谢承利, 等). High Power Laser and Particle Beams(强激光与粒子束), 2008, (7): 1089.

[19] HAO Xiao-jian, REN Long, YANG Yan-wei, et al(郝晓剑, 任龙, 杨彦伟, 等). Laser Technology(激光技术), 2020, 44(2): 232.

[20] Zhang Pengfei, Xu Zhuopin, Wang Qi, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2021, 246: 118986.

[21] Ke Yikan, Dong Huiru. Spectral Analysis(光谱分析). Translated by ZHOU Tong-hui(周同辉, 释). Manual of Analytical Chemistry, 2nd Ed(分析化学手册·第二版). Beijing: Chemical Industry Press(北京化学工业出版社), 1988. 84.

[22] Kennard R W, Stone L A. Technometrics, 2012, 11(1): 137.

[23] Bulajic D, Corsi M, Cristoforetti G, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2002, 57(2): 339.

[24] Bai Kaijie, Yao Shunchun, Lu Jidong, et al. Journal of Analytical Atomic Spectrometry, 2016, 12: 2418.

王海萍, 张鹏飞, 徐琢频, 程维民, 李晓红, 詹玥, 吴跃进, 王琦. LIBS结合VDPSO-CMW算法的高粱Na和Fe定量方法研究[J]. 光谱学与光谱分析, 2023, 43(3): 823. WANG Hai-ping, ZHANG Peng-fei, XU Zhuo-pin, CHENG Wei-min, LI Xiao-hong, ZHAN Yue, WU Yue-jin, WANG Qi. Quantitative Determination of Na and Fe in Sorghum by LIBS Combined With VDPSO-CMW Algorithm[J]. Spectroscopy and Spectral Analysis, 2023, 43(3): 823.

LIBS结合VDPSO-CMW算法的高粱Na和Fe定量方法研究

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