基于均匀插值的浮游藻类规范光谱库构建方法及应用

doi:10.3964/j.issn.1000-0593(2025)05-1225-11

摘要
参考文献
相关文章 (15)

全文: PDF (16178 KB)
输出: BibTeX | EndNote (RIS)

摘要：浮游藻类是海洋生态系统中最重要的生产者。离散三维荧光光谱法解决了连续三维荧光光谱仪运行条件要求高的问题，能够满足浮游藻类的在线与原位监测需求。但活体荧光不稳定性以及色素组成相似性导致的不同藻类光谱之间离散三维荧光光谱差异降低的问题，限制了浮游藻离散三维荧光光谱法的实际应用。本工作研究了不同生境条件下硅藻和甲藻三维荧光光谱特征，分析了常见的浓度归一化平均值（以下称平均法）规范光谱库构建方式的局限性，在此基础上提出了一种基于均匀插值的规范光谱库构建方法。通过实验分别研究了浮游藻类规范光谱库的最佳插值范围和最优插值数目，结果表明，M±2S范围的平均覆盖率达88%，平均相关系数达0.98，是光谱库的最优插值的范围。当插值数目为10条时，测量准确性比平均法提高了近13%，运算时间为0.494 8 s，是兼顾准确性和运算效率的最优插值数目。基于此，构建基于均匀插值的硅藻和甲藻类规范光谱库，结合最小二乘多元线性回归分析，对实验室配置的浓度已知藻类样品进行三维荧光光谱解析，并与平均值法解析结果进行比较。结果表明，采用均匀插值法构建的规范光谱库，对5种不同浓度梯度藻样本的测量值与标准叶绿素a浓度之间的相关系数（k值）范围为0.828~1.149，决定系数（R²）在0.616~0.953之间。对硅藻门识别失败的样本数量从平均值法的21个显著减少至仅2个，对硅藻和甲藻的解析相对误差平均值分别为36.9%和30.7%，较平均值法分别降低了20.6%和19.0%，表明基于均匀插值的规范光谱库构建方法非常有效地提高了硅藻和甲藻的定量解析精度。将此建库方式分别扩展至蓝藻、绿藻和隐藻，并应用于海水叶绿素原位监测仪（AFA）的南海试验。本研究为浮游藻类快速准确测量提供了一种有效的技术方法，也为未来海洋生态监测和环境保护提供了有力的科学支撑。

关键词：浮游藻类；荧光法；光谱库构建；均匀插值；生态安全

Abstract：Phytoplankton are crucial producers in marine ecosystems. The discrete three-dimensional fluorescence spectroscopy method addresses the high operational requirements of continuous three-dimensional fluorescence spectrophotometers, thus meeting the needs for online and in-situ phytoplankton monitoring. However, challenges persist due to the instability of living fluorescence and the similarity in pigment composition among different algal species. This reduces the spectral differentiation between algal groups in discrete three-dimensional fluorescence spectroscopy and limits its practical application. This study investigates the three-dimensional fluorescence spectral characteristics of Bacillariophyta and Dinophyta under various environmental conditions. It analyzes the limitations of the commonly used average concentration-normalized standardized spectral library construction methods (hereinafter referred to as the “average method”). Based on this, a standardized phytoplankton spectral library construction method is proposed using uniform interpolation (hereinafter referred to as the “interpolation method”). The optimal interpolation range and number of interpolations for the phytoplankton standardized spectral library were determined through experiments. The results show that an average coverage rate of 88% and an average correlation coefficient of 0.98 are achieved within the range of M±2S, which is identified as the optimal interpolation range. With 10 interpolations, measurement accuracy improves by nearly 13% compared to the average method, and the computation time is only 0.494 8 seconds, marking it as the optimal number of interpolations considering both accuracy and computational efficiency. Accordingly, a standardized spectral library for Bacillariophyta and Dinophyta was constructed based on uniform interpolation. Combined with non-negative least squares linear regression analysis, this method was used to interpret the three-dimensional fluorescence spectra of a series of algal samples with known concentrations, and the interpretation results were compared with those obtained using the average method. The results indicated that the interpolation method showed a correlation coefficient (k value) ranging from 0.828 to 1.149 and a determination coefficient (R²) ranging from 0.616 to 0.953 for five pure algal samples. The number of Bacillariophyta samples that failed to be identified decreased significantly from 21 (with the average method) to only 2, and the average absolute relative errors in the analysis of Bacillariophyta and Dinophyta were 36.9% and 30.7%, respectively, representing reductions of 20.6% and 19.0% compared to the average method. These results indicate the effectiveness of the uniform interpolation-based method in improving the accuracy of quantitative analysis of Bacillariophyta and Dinophyta. After validating the method's effectiveness in the laboratory, this uniform interpolation-based standardized spectral library construction method was extended to Cyanophyta, Chlorophyta, and Cryptophyta and applied to AFA during a cruise in the South China Sea. This research provides an effective technical method for the rapid and accurate classification and measurement of phytoplankton, offering strong scientific support for future marine ecological monitoring and environmental protection.

Key words：Phytoplankton; Fluorometry; Standardized spectral library construction; Uniform interpolation; Ecologicals Safety

收稿日期: 2024-02-15 修订日期: 2024-12-04

中图分类号:

O433.4

基金资助: 国家重点研发计划项目(2022YFC3103901，2021YFC3200102)，国家自然科学基金项目（62005001，62375270，42206198），安徽省生态环境科研项目（2023hb0012，2023hb0011），安徽省科技创新平台重大科技项目（S202305a12020004），中国仪器仪表学会科学仪器托举计划项目（CISTJ2024）资助

通讯作者: 赵南京 E-mail: njzhao@aiofm.ac.cn

作者简介: 张小玲，女，1989年生，安徽大学讲师 e-mail: xlzhang@ahu.edu.cn

引用本文:

张小玲，王思琦，赵南京，殷高方，董鸣，王翔，张胜俊，陈玮杰. 基于均匀插值的浮游藻类规范光谱库构建方法及应用[J]. 光谱学与光谱分析, 2025, 45(05): 1225-1235.
ZHANG Xiao-ling, WANG Si-qi, ZHAO Nan-jing, YIN Gao-fang, DONG Ming, WANG Xiang, ZHANG Sheng-jun, CHEN Wei-jie. Construction Method and Application of a Standardized Phytoplankton Spectral Library Based on Uniform Interpolation. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(05): 1225-1235.

链接本文:

https://www.gpxygpfx.com/CN/10.3964/j.issn.1000-0593(2025)05-1225-11 或 https://www.gpxygpfx.com/CN/Y2025/V45/I05/1225

[1] Pinkerton M H, Boyd P W, Deppeler S, et al. Frontiers in Ecology and Evolution, 2021, 9: 592027.
[2] Gong W, Browne J, Hall N, et al. The ISME Journal, 2017, 11(2): 439.
[3] Barsanti L, Birindelli L, Gualtieri P. Environmetanl Science: Processes & Impacts, 2021, 23(10): 1443.
[4] Pu S, Zhang F, Shu Y, et al. Journal of Phycology, 2023, 59(6): 1166.
[5] Yentsch C S, Phinney D A. Journal of Plankton Research, 1985, 7(5): 617.
[6] Chen Ying, Duan Weiliang, Yang Ying, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2022, 272: 120967.
[7] Bruckman L S, Richardson T L, Swanstrom J A, et al. Applied Spectroscopy, 2012, 66(1): 60.
[8] Beutler M, Wiltshire K H, Meyer B, et al. Photosynthesis Research, 2002, 72: 39.
[9] Zhao Nanjing, Zhang Xiaoling, Yin Gaofang, et al. Optics Express, 2018, 26(6): A251.
[10] Kramer S J, Siegel D A, Graff J R. Frontiers in Marine Science, 2020, 7: 215.
[11] Spilling K, Seppälä J. Methods in Molecular Biology, Vol 1980, Humana, New York, NY. 2017. 41.
[12] Khan S I, Zamyadi A, Rao N R H, et al. Environmental Science: Water Research & Technolgy, 2019, 5: 417.
[13] SU Rong-guo, LIANG Sheng-kang, ZHU Chen-jian, et al(苏荣国, 梁生康, 祝陈坚, 等). Environmental Science & Technology(环境科学与技术), 2008, 31(3): 52.
[14] SU Rong-guo, HU Xu-peng, ZHANG Chuan-song, et al(苏荣国, 胡序朋, 张传松, 等). Environmental Science(环境科学), 2007, 28(7): 1529.
[15] DUAN Ya-li, SU Rong-guo, SHI Xiao-yong, et al(段亚丽, 苏荣国, 石晓勇, 等). Chinese Journal of Lasers(中国激光), 2012, 39(7): 220.
[16] ZHANG Xiao-ling, YIN Gao-fang, ZHAO Nan-jing, et al(张小玲, 殷高方, 赵南京, 等). Acta Optics Sinica(光学学报), 2018, 38(7): 360.
[17] Shan Shihan, Xu Lei, Chen Ke, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2022, 276: 121216.
[18] Li Ruizhuo, Gao Limin, Wu Guojun, et al. Spectrochim. Acta, Part A, 2024, 311: 123938.
[19] Bi R, Cao Z, Ismar-Rebitz S M H, et al. Frontiers in Microbiology, 2021, 12: 731786.
[20] Oshiro N, Gago-Martínez A, Tubaro A. Journal of Marine Science and Engineering, 2024, 12(2): 236.
[21] Hall R I, Smol J P. The Diatoms: Applications for the Environmental and Earth Sciences, USA: Cambridge University Press, 1999. 23.
[22] Tréguer P J, Sutton J N, Brzezinski M, et al. Biogeosciences, 2021, 18(4): 1269.
[23] Tréguer P, Bowler C, Moriceau B, et al. Nature Geoscience, 2018, 11(1): 27.
[24] Xia R, Wang G, Zhang Y, et al. Water Res., 2020, 185: 116221.
[25] General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, Standardization Administration of the People's Republic of China(中华人民共和国国家质量监督检验检疫总局，中国国家标准化管理委员会). GB/T 21805—2008 Chemical—Alga Growth Inhibition Test(化学品藻类生长抑制试验)，2008.
[26] Ministry of Environmental Protection of the People's Republic of China(中华人民共和国环境保护部). HJ 897—2017. Water Quality—Determination of Chlorophyll a—Spectrophotometric Method(水质叶绿素a的测定分光光度法). 2017.