SOA Key Laboratory for Polar Science, Polar Research Institute of China, Shanghai 200136, PR China.
Spectrochim Acta A Mol Biomol Spectrosc. 2010 Feb;75(2):578-84. doi: 10.1016/j.saa.2009.11.020. Epub 2009 Nov 18.
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 <or=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.
利用小波变换的时域特征建立荧光区分浮游植物方法的可行性进行了探讨。研究了属于五个门的九个属的十二种浮游植物。介绍了五个步骤：首先，讨论了利用三维荧光光谱（3D-FS）区分浮游植物的可行性；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，因为大多数 RSD 小于 5%，大多数 IAC 大于 0.990。其次，用 db7 小波分解 3D-FS，生成时间序列向量（TSV）。第三，用贝叶斯线性判别分析（BLDA）从 TSV 中选择最佳特征谱（OCS）。测试 OCS 对浮游植物分类的能力，得到不同水平的正确分类率（CCR）。在种水平上，大多数 CCR 为 90-100%。在属水平上，它们>98%，在门水平上，它们>99%。第四，测试 OCS 的生长和光稳定性。与 3D-FS 相比，其稳定性较高，RSD（<3%）较低，IAC（>0.999）较高。第五，通过层次聚类分析（HCA）建立了一个由 46 个参考光谱组成的“参考光谱数据库”。在此基础上，通过非负最小二乘法（NNLSs）建立了浮游植物种的判别方法。大多数参考光谱对浮游植物种类具有代表性，并且具有中等的抗噪能力：噪声<或=10%时，属水平的正确判别率（CDR）>98%，门水平的正确判别率>99%。20%的噪声干扰较大，使属水平的 CDR 下降到 85%，门水平的 CDR 下降到 99%。可以建立基于小波变换的 TSV 的浮游植物荧光判别方法。
