Identification of Microalgae Species Using Visible/Near Infrared Transmission Spectroscopy
ZHU Hong-yan1, SHAO Yong-ni1, JIANG Lu-lu2, GUO An-que3, PAN Jian1, HE Yong1*
1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China 2. Zhejiang Technology Institute of Economy, Hangzhou 310018, China 3. College of Enology, Northwest A&F University, Yangling 712100, China
Abstract：At present, the identification and classification of the microalgae and its biochemical analysis have become one of the hot spots on marine biology research. Four microalgae species, including Chlorella vulgaris, Chlorella pyrenoidosa, Nannochloropsis oculata, Chlamydomonas reinhardtii, were chosen as the experimental materials. Using an established spectral acquisition system,which consists of a portable USB 4000 spectrometer having transmitting and receiving fiber bundles connected by a fiber optic probe, a halogen light source, and a computer, the Vis/NIR transmission spectral data of 120 different samples of the microalgae with different concentration gradients were collected, and the spectral curves of fourmicroalgae species were pre-processed by different pre-treatment methods (baseline filtering, convolution smoothing, etc.). Based on the pre-treated effects, SPA was applied to select effective wavelengths (EWs), and the selected EWs were introduced as inputs to develop and compare PLS, Least Square Support Vector Machines (LS-SVM), Extreme Learning Machine (ELM)models, so as to explore the feasibility of using Vis/NIR transmission spectroscopy technology for the rapid identification of four microalgae species in situ. The results showed that: the effect of Savitzky-Golay smoothing was much better than the other pre-treatment methods. Six EWs selected in the spectraby SPA were possibly relevant to the content of carotenoids, chlorophyll in the microalgae. Moreover, the SPA-PLS model obtained better performance than the Full-Spectral-PLS model. The average prediction accuracy of three methods including SPA-LV-SVM, SPA-ELM, and SPA-PLS were 80%, 85% and 65%. The established method in this study may identify four microalgae species effectively, which provides a new way for the identification and classification of the microalgae species. The methodology using Vis/NIR spectroscopy with a portable optic probe would be applicable to a diverse range of microalgae species and proves to be a rapid, real-time, non-destructive, precise method for the physiological and biochemical detection for microalgae.
[1] Wei X, Jie D, Cuello J J, et al. Trac Trends in Analytical Chemistry, 2014, 53: 33. [2] Chisti Y. Biotechnology Advances, 2007, 25(3): 294. [3] Spolaore P, Joannis-Cassan C, Duran E, et al. Journal of Bioscience and Bioengineering, 2006, 101(2): 87. [4] Hu Q, Sommerfeld M, Jarvis E, et al. The Plant Journal, 2008, 54(4): 621. [5] GAO Yang, LIANG Jun-rong, GAO Ya-hui, et al(高 杨，梁君荣，高亚辉，等). Marine Sciences(海洋科学), 2005, 29(1): 67. [6] YAO Peng, YU Zhi-gang(姚 鹏，于志刚). Marine Environmental Science(海洋环境科学), 2003, 22(1): 75. [7] Reid L M, Woodcock T, O’Donnell C P, et al. Food Research International, 2005, 38(10): 1109. [8] He Y, Li X, Deng X. Journal of Food Engineering, 2007, 79(4): 1238. [9] Ahmad A L, Yasin N H M, Derek C J C, et al. Environmental Technology, 2014, 35(17): 2244. [10] HU Kai-hui, WANG Shi-hua(胡开辉，汪世华). Journal of Wuhan Polytechnic University(武汉工业学院学报), 2006, 24(3): 27. [11] ZHANG Chi, HU Hong-jun, LI Zhong-kui, et al(张 弛，胡鸿钧，李中奎，等). Journal of Wuhan Botanical Research(武汉植物学研究), 2000, 18(3): 189. [12] Umdu E S, Tuncer M, Seker E. Bioresource Technology, 2009, 100(11): 2828. [13] Araújo M C U, Saldanha T C B, Galvo R K H, et al. Chemometrics and Intelligent Laboratory Systems, 2001, 57(2): 65. [14] Huang G B, Zhu Q Y, Siew C K. Neural Networks, Proc. IEEE International Joint Conference on, 2004, 2: 985. [15] GUO Wen-chuan, WANG Ming-hai, GU Jing-si, et al(郭文川, 王铭海, 谷静思, 等). Optics and Precision Engineering(光学精密工程), 2013, 21(10): 2720. [16] Langeron Y, Doussot M, Hewson D J, et al. Engineering Applications of Artificial Intelligence, 2007, 20(3): 415. [17] HUI Bo-di(惠伯棣). Carotenoid Chemistry and Biochemistry(类胡萝卜素化学及生物化学). Beijing: China Light Industry Press(北京: 中国轻工业出版社), 2005. 152. [18] Workman Jr J, Weyer L. Practical Guide to Interpretive Near-Infrared Spectroscopy. CRC Press, 2007.
