Research on Microalgae Lipid Change under Nitrogen-Based Stress by Raman Microspectroscopy
FANG Hui1, JIANG Lin-jun1, PAN Jian1, HE Yong1, GONG Ai-ping2, SHAO Yong-ni1*
1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
2. School of Mechanical and Electronic Engineering, Shenzhen Institute of Information Technology, Shenzhen 518172, China
Abstract: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 excess). 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 appropriate principal component variables. The prediction accuracy obtained from LDA prediction model established by the three nitrogen conditions were 80%, 93.3% and 86.7%, respectively. The LDA classification model was established by lipid-related Raman shift (RS) at the three nitrogen conditions, and its prediction accuracy reached to 86.7%. The results showed that the identification 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.
方 蕙,蒋林军,潘 健,何 勇,龚爱平,邵咏妮. 基于显微拉曼对氮胁迫下微藻油脂变化的研究[J]. 光谱学与光谱分析, 2017, 37(10): 3108-3111.
FANG Hui, JIANG Lin-jun, PAN Jian, HE Yong, GONG Ai-ping, SHAO Yong-ni. Research on Microalgae Lipid Change under Nitrogen-Based Stress by Raman Microspectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(10): 3108-3111.
[1] Wijffems R H, Barbosa M J. Science, 2010, 329(5993): 796.
[2] Georgianna D R, Mayfield S P. Nature, 2012, 488(7411): 329.
[3] Wu H, Volponi J V, Oliver A E, et al. Proceedings of the National Academy of Sciences of the United States of America, 2011, 108(9): 3809.
[4] Chen W, Sommerfeld M, Hu Q. Bioresource Technology, 2011, 102(1): 135.
[5] Huang W E, Li M, Jarvis R M, et al. Advances in Applied Microbiology, 2010, 70(70): 153.
[6] Ravindranath K, Rajeswara Rao N, Satyavathi N. Journal of Molecular Liquids, 2005, 121(2-3): 69.
[7] Wei X, Jie D, Cuello J J, et al. Trac-Trend Anal. Chem., 2014, 53(53): 33.
[8] Meksiarun P, Spegazzini N, Matsui H, et al. Applied Spectroscopy, 2015, 69(1): 45.
[9] Brandt N N, Brovko O O, Chikishev A Y, et al. Applied Spectroscopy, 2006, 60(3): 288.
[10] Manolakis D, Marden D. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, 2004, 5425:393.
[11] Shu X, Lu H T. Appl. Intell., 2014, 40(4): 724.
[12] Zhao J H, Yu P L H, Shi L, et al. Comput. Stat. Data An., 2012, 56(12): 4290.
[13] Hosokawa M, Ando M, Mukai S, et al. Analytical Chemistry, 2014, 86(16): 8224.