基于显微拉曼对氮胁迫下微藻油脂变化的研究

摘要

The study investigated the algae growth and lipid change by using confocal Raman microscope,and the Raman spectra were obtained from the Chlorella pyrenoidosa growing under three nitrogen conditions (nitrogen deficiency,normal and ex-cess).Bubble diagrams of the ratio of lipid characteristic peak were presented as an intuitive expression of lipid accumulation, which was corresponding to the NR fluorescence image in some way.The preprocessed Raman signals were analyzed by using principal components analysis.Linear discriminant analysis (LDA)was used to establish a classification model by using appro-priate principal component variables.The prediction accuracy obtained from LDA prediction model established by the three nitro-gen conditions were 80%,93.3% and 86.7%,respectively.The LDA classification model was established by lipid-related Ra-man shift (RS)at the three nitrogen conditions,and its prediction accuracy reached to 86.7%.The results showed that the iden-tification of different nitrogen stress on microalgae grown using Raman technology was feasible,and with time passing by,the difference in the accumulation of lipid became greater.%利用共聚焦显微拉曼光谱仪获取生长在三种氮营养条件下(氮胁迫、氮正常、氮饱和)培养的蛋白核小球藻(Chlorella pyrenoidosa)的拉曼光谱,通过拉曼散射光谱信息对微藻在不同氮胁迫下生长情况及油脂变化进行研究.对油脂拉曼特征峰值比值作气泡图以直观表达油脂积累量,该气泡图与尼罗红荧光图像具有良好的相关性.光谱信号经预处理后,利用主成分分析(PCA)对全波段进行分析,获得相应的主成分变量,通过线性判别分析(LDA)建立分类模型.利用PCA获取的主成分变量建立的LDA预测模型对三种氮营养条件的预测正确率分别是80%,93.3%,86.7%.基于油脂特征位移(RS)处的比率建立的LDA分类模型对三种氮营养条件的分类正确率最高达到86.7%.研究结果表明,利用拉曼技术对微藻生长的不同氮胁迫条件鉴别是可行的,且随着氮胁迫影响的时间增加,油脂的积累差异就越大.