Study on fluorometric discrimination of phytoplankton based on time-series vectors of wavelet transform.
Study on fluorometric discrimination of phytoplankton based on time-series vectors of wavelet transform.
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DOI：
10.1016/j.saa.2009.11.020
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发表时间：
2010-02
期刊：
影响因子：
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通讯作者：
Fang Zhang;R. Su;Jian-feng He;Minghong Cai;W. Luo;Xiulin Wang
中科院分区：
文献类型：
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作者：
Fang Zhang;R. Su;Jian-feng He;Minghong Cai;W. Luo;Xiulin Wang
探讨了利用小波变换的时域特性建立浮游植物荧光识别方法的可行性。研究了浮游植物5门9属12种。提出了五个步骤：首先，探讨了利用三维荧光光谱（3D-FS）识别浮游植物的可行性，并以相对标准偏差（RSD）和夹角余弦（IAC）作为检验标准。3D-FS具有这种潜力，对于大多数RSD为<5% and most IAC were >0.990。其次，采用db 7小波对三维流场进行分解，生成时间序列矢量（TSV）;第三，通过贝叶斯线性判别分析（BLDA）从TSV中选择最佳特征光谱（OCS）。测试了OCS对浮游植物的分类能力，得到了不同层次的正确分类率（CCR）。在物种水平上，大多数CCR为90-100%。在属级水平上，它们的比例均大于98%，在级级水平上，它们的比例均大于99%。第四，对OCS的生长和光稳定性进行了研究。与3D-FS相比，两种方法的相对标准偏差（RSD）均小于3%，IAC均大于0.999。第五，通过系统聚类分析（HCA），建立了由46个参考光谱组成的“参考光谱数据库”。在此基础上，建立了非负最小二乘（NNLS）浮游植物种类判别方法。大部分参考光谱具有代表性，抗噪声能力中等，在噪声≤ 10%时，属级和级级级的正确分辨率分别>98%和>99%。20%的噪声干扰较大，使属水平的CDR下降到85%，分裂水平的CDR下降到99%。基于小波变换的TSV可以建立浮游植物的荧光识别方法。
The feasibility of using time domain of wavelet transform as characteristics to establish a fluorometric discrimination method of phytoplankton was discussed. Twelve phytoplankton species belonging to nine genera of five divisions were studied. Five steps were introduced: firstly, the feasibility of utilizing 3D fluorescence spectra (3D-FS) to discriminate phytoplankton was discussed; the relative standard deviation (RSD) and included angle cosine (IAC) were used as the test criterion. 3D-FS had such potentials, for most RSD were <5% and most IAC were >0.990. Secondly, the 3D-FS were decomposed by db7 wavelet and time-series vectors (TSVs) were generated. Thirdly, the optimal characteristic spectra (OCS) were selected from the TSV by Bayesian linear discriminant analysis (BLDA). The ability of OCS to classify phytoplankton was tested, and the correct classification ratios (CCRs) at different levels were obtained. Most CCRs were 90–100% at the species level. They were >98% at the genus level, and >99% at the division level. Fourthly, the growth and light stability of the OCS were tested. Both stabilities were high with lower RSD (<3%) and higher IAC (>0.999) compared with 3D-FS. Fifthly, a “database of reference spectra” consisting of 46 reference spectra was established by hierarchical cluster analysis (HCA). Based on this, the discrimination method of phytoplankton species was established by nonnegative least squares (NNLSs). Most reference spectra were representative to phytoplankton species; and had moderate anti-noise ability: With noise ≤10%, the correct discrimination ratios (CDRs) were >98% at the genus level and >99% at the division level. 20% noise was a larger interference which made CDRs down to 85% at the genus level and to 99% at the division level. A fluorometric discrimination method of phytoplankton could be established based on TSV of wavelet transform.