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利用近红外光谱法快速表征产油微藻中的脂肪酸

Rapid Characterization of Fatty Acids in Oleaginous Microalgae by Near-Infrared Spectroscopy.

作者信息

Liu Bin, Liu Jin, Chen Tianpeng, Yang Bo, Jiang Yue, Wei Dong, Chen Feng

机构信息

School of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510640, China.

Institute for Food and Bioresource Engineering, College of Engineering, Peking University, Beijing 100871, China.

出版信息

Int J Mol Sci. 2015 Mar 27;16(4):7045-56. doi: 10.3390/ijms16047045.

Abstract

The key properties of microalgal biodiesel are largely determined by the composition of its fatty acid methyl esters (FAMEs). The gas chromatography (GC) based techniques for fatty acid analysis involve energy-intensive and time-consuming procedures and thus are less suitable for high-throughput screening applications. In the present study, a novel quantification method for microalgal fatty acids was established based on the near-infrared spectroscopy (NIRS) technique. The lyophilized cells of oleaginous Chlorella containing different contents of lipids were scanned by NIRS and their fatty acid profiles were determined by GC-MS. NIRS models were developed based on the chemometric correlation of the near-infrared spectra with fatty acid profiles in algal biomass. The optimized NIRS models showed excellent performances for predicting the contents of total fatty acids, C16:0, C18:0, C18:1 and C18:3, with the coefficient of determination (R2) being 0.998, 0.997, 0.989, 0.991 and 0.997, respectively. Taken together, the NIRS method established here bypasses the procedures of cell disruption, oil extraction and transesterification, is rapid, reliable, and of great potential for high-throughput applications, and will facilitate the screening of microalgal mutants and optimization of their growth conditions for biodiesel production.

摘要

微藻生物柴油的关键特性在很大程度上由其脂肪酸甲酯(FAMEs)的组成所决定。基于气相色谱(GC)的脂肪酸分析技术涉及能源密集型和耗时的程序,因此不太适合高通量筛选应用。在本研究中,基于近红外光谱(NIRS)技术建立了一种新的微藻脂肪酸定量方法。用NIRS扫描含不同脂质含量的产油小球藻的冻干细胞,并通过GC-MS测定其脂肪酸谱。基于近红外光谱与藻类生物质中脂肪酸谱的化学计量学相关性建立了NIRS模型。优化后的NIRS模型在预测总脂肪酸、C16:0、C18:0、C18:1和C18:3的含量方面表现出色,决定系数(R2)分别为0.998、0.997、0.989、0.991和0.997。综上所述,这里建立的NIRS方法绕过了细胞破碎、油提取和酯交换过程,快速、可靠,具有高通量应用的巨大潜力,将有助于微藻突变体的筛选及其生物柴油生产生长条件的优化。

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