光谱学与光谱分析 |
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Study on the Nondestructive Detection Methods for Dynamica Change of Lipid Content in Chlorella sp. |
WEI Xuan1, 2,JIANG Lu-lu3,ZHAO Yan-ru2,SHAO Yong-ni2,QIU Zheng- jun2,HE Yong2* |
1. College of Mechanical and Electronic Engineering,Fujian Agriculture and Forestry University,Fuzhou 350002, China 2. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China 3. Zhejiang Technology Institute of Economy, Hangzhou 310018, China |
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Abstract Microalgae based biodiesel production requires a large amount of lipid accumulation in the cells, and the accumulation is greatly influenced by the environment. Therefore, it is necessary to find fast and non-destructive methods for lipid change detection. In this paper, Chlorella sp. was adopted as the objective, which was cultured under different light condition consisted of red and blue lights with different proportion. We applied the visible near-infrared spectroscopy (Vis/NIRs) technique to detect the dynamic change of lipid during the microalgae growth processes and utilized hyperspectral imaging technology for visualization of lipid distribution in the suspension. The transmittance and reflectance spectra of microalgae were acquired with Vis/NIRs and hyper-spectroscopy, respectively. In the comparison of the transmittance and reflectance spectra, they showed some different characteristics. Meanwhile it also varied in terms of the number and the area of feature wavelengths obtained by successive projections algorithm (SPA) based on the different spectra. But the established multiple linear regression (MLR) model for lipid content prediction had similar results with rpre of 0.940, RMSEP of 0.003 56 and rpre of 0.932, RMSEP of 0.004 23, respectively. Based on the predictive model, we obtained the spectra and analyzed the lipid dynamic change in microalgae in one life cycle. In the life cycle, the lipid content in Chlorella sp. was relatively stable from the beginning of inoculation to exponential phase, the increase and accumulation of lipid phenomenon occurred in the late exponential phase. Combined with the MLR model and the hypersepctral images, we studied the visualization result of microalgae suspension in the steady phase. The stimulated images showed that the microalgae with higher lipid content appeared gathering. This study compared the difference and the feasibility of the Vis/NIRs and hyperspectral imaging technique in lipid content detection applied in microalgae growing microalgae. The results are meaningful for the fast and non-destructive detection of the growth information of microalgae. It has boththeoretical and practical significance for developing microalgal culture and harvest strategy in practice.
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Received: 2014-12-19
Accepted: 2015-04-12
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Corresponding Authors:
HE Yong
E-mail: yhe@zju.edu.cn
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[1] Schenk P, Thomas-Hall S, Stephens E, et al. Bioenerg Res.,2008, 1(1): 20. [2] Yusuf C. Biotechnol Adv.,2007, 25(3): 294. [3] Velasquez-Orta S B, Lee J G M, Harvey A P. Biochem. Eng. J.,2013, 76: 83. [4] Bahadar A, Bilal Khan M. Renewable and Sustainable Energy Reviews,2013, 27(0): 128. [5] Shu C H, Tsai C C, Liao W H, et al. J. Chem. Technol. Biotechnol., 2012, 87(5): 601. [6] Tang H, Abunasser N, Garcia M E D, et al. Appl. Energ.,2011, 88(10): 3324. [7] Chen C Y, Yeh K L, Aisyah R, et al. Bioresource Technol.,2011, 102(1): 71. [8] Seo Y H, Cho C, Lee J Y, et al. Bioresource Technol.,2014, 173: 193. [9] Jie Dengfei, Xie Lijuan, Rao Xiuqin, et al. Postharvest Biol. Tec., 2014, 90: 1. [10] Wu Di, Nie Pengcheng, He Yong, et al. Int. J. Food Prop., 2013, 16(5): 1002. [11] Elmasry G, Kamruzzaman M, Sun D-W, et al. Crit. Rev. Food Sci., 2012, 52(11): 999. [12] Wei Xuan, Liu Fei, Qiu Zengjun, et al. Food Bioprocess Technol., 2013: 1. [13] Jie Dengfei, Xie Lijuan, Fu Xiaping, et al. J. Food Eng., 2013, 118(4): 387. [14] Klok A J, Martens D E, Wijffels R H, et al. Bioresource Technol., 2013, 134: 233. [15] Packer A, Li Y, Andersen T, et al. Bioresource Technol.,2011, 102(1): 111. [16] Solovchenko A E. Russ. J. Plant Physiol., 2012, 59(2): 167. [17] Li Yuqin, Han Fangxin, Xu Hua, et al. Bioresource Technol., 2014, 174: 24. |
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