Discrimination of marine algal taxonomic groups based on fluorescence excitation emission matrix, parallel factor analysis and CHEMTAX

CHEN Xiaona; SU Rongguo; BAI Ying; SHI Xiaoyong; YANG Rujun

doi:10.1007/s13131-014-0576-5

基于三维荧光光谱-平行因子技术联用的浮游藻化学分类学技术研究

doi: 10.1007/s13131-014-0576-5

中国海洋大学化学化工学院, 青岛 266100

基金项目: The National Natural Science Foundation of China under contract Nos 41376106 and 41276069.

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- 收稿日期: 2013-09-14
- 修回日期: 2014-01-09

Discrimination of marine algal taxonomic groups based on fluorescence excitation emission matrix, parallel factor analysis and CHEMTAX

College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao 266100, China

摘要

摘要: 利用平行因子 (PARAFAC) 和CHEMTAX发展区分浮游藻群落组成的活体三维荧光分析技术.将PARAFAC分解模型应用于分属5个门60种浮游藻的三维荧光光谱(EEM),通过残差分析和荧光成分谱形分析确定浮游藻EEM由11种荧光成分组成;然后,利用Bayesian判别分析(BDA)表明浮游藻的11个荧光成分的组成具有明显的门类特征性;将获得的11个荧光成分构建适合CHEMTAX要求的浮游藻 “荧光成分比值矩阵”,结合CHEMTAX建立浮游藻荧光识别分析技术(EEM-PARAFAC-CHEMTAX). 对浮游藻样品进行门类水平上的识别分析,对单种藻样品,硅藻的识别正确率是 95.6%,其余藻的识别正确率为100%;对于实验室混合样品,优势藻和次优势藻的平均识别正确率分别高于94.0%和87.0%,然而,当估算的次优势藻的相对含量低于15%时,对次优势藻的识别结果不可靠;对于2007年从胶州湾和小麦岛围隔实验现场采集的水样,优势藻群和相对丰度高于15.0%的次优势藻群的识别结果与镜检结果相一致.此荧光技术可以用于现场大批量浮游藻样品的快速、低成本分析,能够实现浮游植物群落组成的现场识别测定.
- CHEMTAX /
- 三维荧光光谱 /
- 活体 /
- 荧光成分 /
- 平行因子 /
- 浮游植物
Abstract: An in vivo three-dimensional fluorescence method for the determination of algae community structure was developed by parallel factor analysis (PARAFAC) and CHEMTAX. The PARAFAC model was applied to fluorescence excitation-emission matrix (EEM) of 60 algae species belonging to five divisions and 11 fluorescent components were identified according to the residual sum of squares and specificity of the composition profiles of fluorescent. By the 11 fluorescent components, the algae species at different growth stages were classified correctly at the division level using Bayesian discriminant analysis (BDA). Then the reference fluorescent component ratio matrix was constructed for CHEMTAX, and the EEM-PARAFAC-CHEMTAX method was developed to differentiate algae taxonomic groups. The correct discrimination ratios (CDRs) when the fluorometric method was used for single-species samples were 100% at the division level, except for Bacillariophyta with a CDR of 95.6%. The CDRs for the mixtures were above 94.0% for the dominant algae species and above 87.0% for the subdominant algae species. However, the CDRs of the subdominant algae species were too low to be unreliable when the relative abundance estimated was less than 15.0%. The fluorometric method was tested using the samples from the Jiaozhou Bay and the mesocosm experiments in the Xiaomai Island Bay in August 2007. The discrimination results of the dominant algae groups agreed with microscopy cell counts, as well as the subdominant algae groups of which the estimated relative abundance was above 15.0%. This technique would be of great aid when low-cost and rapid analysis is needed for samples in a large batch. The fluorometric technique has the ability to correctly identify dominant species with proper abundance both in vivo and in situ.
- CHEMTAX /
- fluorescence excitation-emission matrices /
- in vivo /
- fluorescent components /
- parallel factor analysis /
- phytoplankton

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参考文献(41)

Andersen C M, Bro R. 2003. Practical aspects of PARAFAC modeling of fluorescence excitation-emission data. J Chemom, 17(4): 200- 215

Arrigo K R. 2005. Marine microorganisms and global nutrient cycles. Nature, 437(7057): 349-355

Barber C B, Dobkin D P, Huhdanpaa H. 1996. The Quickhull algorithm for convex hulls. ACM Transactions on Mathematical Software, 22(4): 469-483

Beutler M, Wiltshire K H, Arp M, et al. 2003. A reduced model of the fluorescence from the cyanobacterial photosynthetic apparatus designed for the in situ detection of cyanobacteria. Biochimica et Biophysica Acta (BBA)-Bioenergetics, 1604(1): 33-46

Bosco M V, Larrechi M S. 2007. PARAFAC and MCR-ALS applied to the quantitative monitoring of the photodegradation process of polycyclic aromatic hydrocarbons using three-dimensional excitation emission fluorescent spectra Comparative results with HPLC. Talanta, 71(4): 1703-1709

Bro R. 1997. PARAFAC tutorial and applications. Chemometrics and Intelligent Laboratory Systems, 38(2): 149-171

Bro R. 1999. Exploratory study of sugar production using fluorescence spectroscopy and multi-way analysis. Chemometrics and Intelligent Laboratory Systems, 46(2): 133-147

Chen J Q, Guo R X. 2012. Access the toxic effect of the antibiotic cefradine and its UV light degradation products on two freshwater algae. Journal of Hazardous Materials, 209-210: 520-523

Drinovec L, Flander-Putrle V, Knez M, et al. 2011. Discrimination of marine algal taxonomic groups using delayed fluorescence spectroscopy. Environmental and Experimental Botany, 73: 42-48

Fellman J B, Miller M P, Cory R M, et al. 2009. Characterizing dissolved organic matter using PARAFAC modeling of fluorescence spectroscopy: A comparison of two models. Environmental Science & Technology, 43(16): 6228-6234

Gameiro C, Cartaxana P, Brotas V. 2007. Environmental drivers of phytoplankton distribution and composition in Tagus Estuary, Portugal. Estuarine, Coastal and Shelf Science, 75(1-2): 21-34

Guillard R R L. 1975. Culture of phytoplankton for feeding marine invertebrates. In: Culture of Marine Invertebrate Animals. New York: Springer, 29-60

Harshman R A. 1970. Foundations of the PARAFAC procedure: Models and conditions for an “explanatory” multimodal factor analysis. UCLA Working Papers in Phonetics, 16: 1-84

Havskum H, Schlüter L, Scharek R, et al. 2004. Routine quantification of phytoplankton groups-microscopy or pigment analyses. Marine Ecology Progress Series, 273: 31-42

Harwati T U, Willke T, Vorlop K D. 2012. Characterization of the lipid accumulation in a tropical freshwater microalgae Chlorococcum sp. Bioresource Technology, 121: 54-60

Hu Yuxi, Li Xibing. 2012. Bayes discriminant analysis method to identify risky of complicated goaf in mines and its application. Transactions of Nonferrous Metals Society of China, 22(2): 425-431

Jeffrey S W, Hallegraeff G M. 1980. Studies of phytoplankton species and photosynthetic pigments in a warm core eddy of East Australian Current: I. Summer populations. Marine Ecology Progress Series, 3: 285-294

Khullar S, Michael A, Correa N, et al. 2011. Wavelet-based fMRI analysis: 3-D denoising, signal separation, and validation metrics. Neuroimage, 54(4): 2867-2884

Latasa M. 2007. Improving estimations of phytoplankton class abundances using CHEMTAX. Marine Ecology Progress Series, 329: 13-21

Lee T, Tsuzuki M, Takeuchi T, et al. 1995. Quantitative determination of cyanobacteria in mixed phytoplankton assemblages by an in vivo fluorimetric method. Analytica Chimica Acta, 302(1): 81-87

Li Yumei, Anderson-Sprecher R. 2006. Facies identification from well logs: A comparison of discriminant analysis and naïve Bayes classifier. Journal of Petroleum Science and Engineering, 53(3- 4): 149-157

Liu Xianli, Tao Shu, Deng Nansheng. 2005. Synchronous-scan fluorescence spectra of Chlorella vulgaris solution. Chemosphere, 60(11): 1550-1554

Mackey M D, Mackey D J, Higgins H W, et al. 1996. CHEMTAX—a program for estimating class abundances from chemical markers: application to HPLC measurements of phytoplankton. Marine Ecology Progress Series, 144: 265-283

Nie Jinfang, Wu Hailong, Zhang Shurong, et al. 2010. Self-weighted alternating normalized residue fitting algorithm with application to quantitative analysis of excitation-emission matrix fluorescence data. Analytical Methods, 2: 1918-1926

Oldham P B, Zillioux E J, Walker I M. 1985. Spectral “fingerprinting” of phytoplankton populations by two-dimensional fluorescence and Fourier-transform-based pattern recognition. Journal of Marine Research, 43:893-906

Paerl H W, Huisman J. 2009. Climate change: A catalyst for global expansion of harmful cyanobacterial blooms. Environmental Microbiology Reports, 1(1): 27-37

Rodriguez F, Varela M, Zapata M. 2002. Phytoplankton assemblages in the Gerlache and Bransfield Straits (Antarctic Peninsula) determined by light microscopy and CHEMTAX analysis of HPLC pigment data. Deep-Sea Research Part II: Topical Studies in Oceanography, 49(4-5): 723-747

Ruivo M, Amorim A, Cartaxana P. 2011. Effects of growth phase and irradiance on phytoplankton pigment ratios: implications for chemotaxonomy in coastal waters. Journal of Plankton Research, 33(7): 1012-1022

Schlüter L, Lauridsen T L, Krogh G, et al. 2006. Identification and quantification of phytoplankton groups in lakes using new pigment ratios—a comparison between pigment analysis by HPLC and microscopy. Freshwater Biology, 51(8): 1474-1485

Schlüter L, Møhlenberg F, Havskum H, et al. 2000. The use of phytoplankton pigments for identifying and quantifying phytoplankton groups in coastal areas: testing the influence of light and nutrients on pigment/chlorophyll a ratios. Marine Ecology Progress Series, 192: 49-63

Sheng Guoping, Yu Hanqing. 2006. Characterization of extracellular polymeric substances of aerobic and anaerobic sludge using three-dimensional excitation and emission matrix fluorescence spectroscopy. Water Research, 40(6): 1233-1239

Stedmon C A, Bro R. 2008. Characterizing dissolved organic matter fluorescence with parallel factor analysis: a tutorial. Limnology and Oceanography: Methods, 6: 572-579

Stedmon C A, Markager S. 2005. Resolving the variability in dissolved organic matter fluorescence in a temperate estuary and its catchment using PARAFAC analysis. Limnology and Oceanography, 50(2): 686-697

Stedmon C A, Markager S, Bro R. 2003. Tracing dissolved organic matter in aquatic environments using a new approach to fluorescence spectroscopy. Marine Chemistry, 82(3-4): 239-254

Wang Zhiwei, Wu Zhichao, Tang Shujuan. 2009. Characterization of dissolved organic matter in a submerged membrane bioreactor by using three-dimensional excitation and emission matrix fluorescence spectroscopy. Water Research, 43(6): 1533-1540

Wright S W, Thomas D P, Marchant H J, et al. 1996. Analysis of phytoplankton of the Australian sector of the Southern Ocean: comparisons of microscopy and size frequency data with interpretations of pigment HPLC data using the 'CHEMTAX' matrix factorisation program. Marine Ecology Progress Series, 144: 285-298

Wright S W, van den Enden R L, Pearce I, et al. 2010. Phytoplankton community structure and stocks in the Southern Ocean (30- 801E) determined by CHEMTAX analysis of HPLC pigment signatures. Deep-Sea Research Part II, 57: 758-778

Zelen M, Severo N C. 1970. Probability functions. In: Abramowitz M, Stegun I A, eds. Handbook of Mathematical Functions. New York: Dover Publications, 925-995

Zepp R G, Sheldon W M, Moran M A. 2004. Dissolved organic fluorophores in southeastern US coastal waters: correction method for eliminating Rayleigh and Raman scattering peaks in excitationemission matrices. Marine Chemistry, 89(1-4): 15-36

Zhang Fang, Su Rongguo, He Jianfeng, et al. 2010. Identifying phytoplankton in seawater based on discrete excitation-emission fluorescence spectra. Journal of Phycology, 46(2): 403-411

Zhang Fang, Su Rongguo, Wang Xiulin, et al. 2009. A fluorometric method for the discrimination of harmful algal bloom species developed by wavelet analysis. Journal of Experimental Marine Biology and Ecology, 368(1): 37-43

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基于三维荧光光谱-平行因子技术联用的浮游藻化学分类学技术研究

doi: 10.1007/s13131-014-0576-5

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出版历程

Discrimination of marine algal taxonomic groups based on fluorescence excitation emission matrix, parallel factor analysis and CHEMTAX

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