Physics Department, Sichuan University, Chengdu 610064, PR China; Chongqing Key Laboratory of Multi-scale Manufacturing Technology, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, PR China.
Chongqing Key Laboratory of Multi-scale Manufacturing Technology, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, PR China.
Spectrochim Acta A Mol Biomol Spectrosc. 2018 Nov 5;204:287-294. doi: 10.1016/j.saa.2018.06.060. Epub 2018 Jun 20.
The assessment for cell physiology and growth phases of microalgae plays important roles in ecological and environmental fields since it can be used to forecast water eutrophication level worldwidely. Herein, growth phases and environmental conditions of microalgae were assessed by combining resonance Raman mapping spectroscopy with multivariate analysis methods. And, primary Raman characteristic peaks of microalgae were mined with two-dimensional synchronous spectra. Thereafter, algal growth phases and environmental conditions of microalgae were preliminary classified with different tendencies of characteristic Raman peaks by unsupervised principal component analysis (PCA) and support vector machine (SVM) methods. Our results demonstrated that resonance Raman mapping spectroscopy with PCA and SVM classification models can be used to assess algal growth phases and preliminary predict environmental conditions with characteristic Raman spectra of microalgae in water bodies.
微藻的细胞生理和生长阶段的评估在生态和环境领域中起着重要作用，因为它可以用于预测全球范围内的水体富营养化水平。在此，通过将共振拉曼映射光谱与多元分析方法相结合，评估了微藻的生长阶段和环境条件。并且，通过二维同步光谱挖掘了微藻的主要拉曼特征峰。此后，通过无监督主成分分析（PCA）和支持向量机（SVM）方法，根据特征拉曼峰的不同趋势，初步对微藻的生长阶段和环境条件进行了分类。我们的结果表明，利用共振拉曼映射光谱结合 PCA 和 SVM 分类模型，可以根据水体中微藻的特征拉曼光谱来评估藻类的生长阶段，并初步预测环境条件。
