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2018 | 27 | 1 |

Tytuł artykułu

The possibility of applying the EM-PCA procedure to lake water

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
Missing elements in experimental data often occur in ecological and biological sciences. In this case, it is difficult to carry out any data analysis and their evaluation. This paper presents one of the chemometric techniques – principal component analysis (PCA) – used to classify water quality indices on data that contain missing elements. The surface water of Czajcze Lake in Wolin National Park (northwestern Poland) was investigated. Sixteen water-quality indices were appointed in a period from April to October during 1983- 2013. Conducted analysis of experimental data by EM-PCA grouped the presented water quality indices in natural clusters, including several principal components (PCs) about similar features. EM-PCA applied in the present work shows that this method can be used to analyze experimental data with missing data on considerable seasonal changes.

Słowa kluczowe

Wydawca

-

Rocznik

Tom

27

Numer

1

Opis fizyczny

p.19-30,fig.,ref.

Twórcy

  • Department of Chemistry and Natural Waters Management, Institute for Research on Biodiversity, Faculty of Biology, Szczecin University, Felczaka 3c, 71-412 Szczecin, Poland

Bibliografia

  • 1. DASZYKOWSKI M., KACZMAREK K., VANDER HEYDEN Y, WALCZAK B. Robust statistics in data analysis - a review basic concepts. Chemometrics and Intelligent Laboratory Systems, 85 (2), 203, 2007. DOI: 10.1016/j.chemolab.2006.06.016
  • 2. STANIMIROVA I., ZEHL K., MASSART D.L., VANDER HEYDEN Y., EINAX J.W. Chemometric analysis of soil pollution data applying Tucker N-way method. Analytical and Bioanalytical Chemistry, 385 (4), 771, 2006. DOI: 10.1007/s00216-006-0445-y
  • 3. STANIMIROVA I., DASZYKOWSKI M., WALCZAK B. Dealing with missing values and outliers in principal component analysis. Talanta, 72 (1), 172, 2007. DOI: 10.1016/j.talanta.2006.10.011
  • 4. WU T., ZHAO W., GUO H., LIM H., YANG Z. A streaming PCA based VLSI chip for neural data compression. In: IEEE Biomedical Circuits and Systems Conference, Shanghai, China 17-19 October 2016, 192, 2017. DOI: 10.1109/BioCAS.2016.7833764
  • 5. KAYA I.E., PEHLIVANLI A.Ç., SEKIZKARDEŞ E.G., IBRIKCI T. PCA based clustering for brain tumor segmentation of T1w MRI images. Computer Methods and Programs in Biomedicine, 140, 19-28, 2017. DOI: https://doi.org/10.1016/j.cmpb.2016.11.011
  • 6. SMOLIŃSKI A. ,FALKOWSKA L., PRYPUTNIEWICZ D. Chemometric exploration of sea water chemical component data sets with missing elements. Oceanological and Hydrobiological Studies, 3, 49, 2008. DOI: 10.2478/v10009-008-0005-1
  • 7. BAILEY S. Principal Component Analysis with Noisy and/or Missing Data. Publications of the Astronomical Society of the Pacific, 124 (919), 1015, 2012. DOI: 10.1086/668105
  • 8. SERNEELS S., VERDONCK T. Principal component analysis for data containing outliers and missing elements. Copmutational Statistics and Data Analysis, 52 (3), 1712, 2008. DOI: 10.1016/j.csda.2007.05.024
  • 9. WALCZAK B., MASSART D.L. Dealing with missing data: Part I. Chemometrics and Intelligent Laboratory Systems, 58, 15–27, 2001a. http://doi.org/10.1016/S0169-7439(01)00131-9
  • 10. WALCZAK B., MASSART D.L. Dealing with missing data: Part II. Chemometrics and Intelligent Laboratory Systems, 58, 29, 2001b. http://doi.org/10.1016/S0169-7439(01)00132-0
  • 11. SMOLIŃSKI A., WALCZAK B. Exploratory analysis of chromatographic data-sets with missing elements. Initialization of the expectation-maximization algorithm. Acta Chromatographica, 12, 30, 2002.
  • 12. STRAUSS R.E., ATANASSOV M.N., ALVES DE OLIVEIRA J. Evaluation of the principle-component and expectation-maximization methods for estimation of missing data in morphometric studies. Journal of Vertebrate Paleontology , 23 (2), 284, 2003.
  • 13. NAKAGAWA S., FRECKLETON R.P. Missing inaction: the dangers of ignoring missing data. Trends Ecology and Evolution, 23 (11), 592, 2008. DOI: 10.1016/j.tree.2008.06.014
  • 14. NEESER E., ACKERMANN R.R., GAIN J. Comparing the accuracy and precision of three techniques used for estimating missing landmarks when reconstructing fossil hominin crania. American Journal of Physical Anthropology, 140 (1), 1, 2009. DOI: 10.1002/ajpa.21023.
  • 15. COUETTE S., WHITE J. 3D geometric morphometrics and missing data. Can extant taxa give clues for the analysis of fossil primates? C. R. Palevol., 9, 423, 2010. DOI:10.1016/j.crpv.2010.07.002
  • 16. BENTLER P.M., YUAN K.H. Positive Definiteness via Off-diagonal Scaling of a Symmetric Indefinite Matrix. Psychometrika, 76 (1), 119, 2011. DOI: 10.1007/s11336-010-9191-3
  • 17. JOSSE J., PAGÈS J., HUSSON F. Multiple imputation in principal component analysis. Adv Data Anal Classif, 5, 231-246, 2011. DOI 10.1007/s11634-011-0086-7
  • 18. BROWN C. M., ARBOUR J. H., JACKSON D. A. Testing of the Effect of Missing Data Estimation and Distribution in Morphometric Multivariate Data Analyses. Syst. Biol., 61 (6), 941, 2012. DOI:10.1093/sysbio/sys047
  • 19. JOSSE J., HUSSON F. Handling missing values in exploratory multivariate data analysis methods. Journal de la Société Française de Statistique, 153 (2), 79, 2012.
  • 20. CLAVEL J., MERCERON G., ESCARGUEL G. Missing Data Estimation in Morphometrics: How Much is Too Much? Syst. Biol., 63 (2), 203, 2014. DOI:10.1093/sysbio/syt100
  • 21. HOU D., LIU S.,ZHANG J., CHEN F., HUANG P., ZHANG G. Online Monitoring of Water-Quality Anomaly in Water Distribution Systems Based on Probabilistic Principal Component Analysis by UV-Vis Absorption Spectroscopy. Journal of Spectroscopy, Article ID 150636, 9 pages, 2014. DOI: http://dx.doi.org/10.1155/2014/150636
  • 22. DRAY S., JOSSE J. Principal component analysis with missing values: a comparative survey of methods. Plant Ecology, 216 (5), 657, 2015. DOI: 10.1007/s11258-014-0406-z
  • 23. POLESZCZUK G., SADOWSKA B., KARPOWICZ K., GRZEGORCZYK K. Open water ecosystems of Wolin National Park – natural characterization. Baltic Coastal Zone, 7, 37, 2002/2003.
  • 24. BUCIOR A., POLESZCZUK G. What happens in the waters of the Warnowo, Rabiąż, Czajcze and Domysłowskie Lakes in the Wolin National Park during summer stagnation?. Ecological Chemistry and Engineering, Series A, 20 (1), 7, 2013. DOI:10.2428/ecea.2013.20(01)001
  • 25. WAWRZYNIAK W., POLESZCZUK G., BUCIOR A., PIERWIENIECKI J., LASKOWSKI F., TYMANOWSKI Ł., GRYNFELDER K., RUTKOWSKA J. Wody powierzchniowe jezior Pojezierza Warnowsko-Kołczewskiego w Wolińskim Parku Narodowym – status troficzny wiosną 2012 roku. W: Zaborowski T (red.), Satori w publicznym bezpieczeństwie. Wyd. Inst. Badań i Ekspertyz Nauk. w Gorzowie Wlkp., Gorzów Wlkp.-Poznań, 350, 2012.
  • 26. JAŃCZAK J. (red.) Atlas jezior Polski – tom II. Jeziora zlewni rzek Przymorza i dorzecza dolnej Wisły. IMGW, Bogucki Wydawnictwo Naukowe, Poznań, 256, 1997. ISBN: 83-86001-43-7
  • 27. ISO 5667-4:2016. Water quality -- Sampling -- Part 4: Guidance on sampling from lakes, natural and man-made, 2016.
  • 28. HERMANOWICZ W., DOJLIDO J., DOŻAŃSKA W., KOZIOROWSKI B., ZERBE J. Fizyczno-chemiczne badanie wody i ścieków. Wyd. Arkady, 555, 1999.
  • 29. APHA, AWWA, WEF. Standard methods for the examination of water and wastewater. 20th ed. Washington, D.C.: APHAAWWA-WEF, 1268, 1998.
  • 30. APHA. Standard methods for the examination of water and wastewater. 21st ed. Washington, D.C.: APHA, 1368, 2005.
  • 31. ISO 5667-3:2012. Water quality – Sampling – Part 3: Preservation and handling of water samples, 2012.
  • 32. WOLD S., ESBENSEN K., GELADI P. Principal Component Analysis. Chemometrics and Intelligent Laboratory Systems, 2, 37, 1987.
  • 33. DASZYKOWSKI M., WALCZAK B., MASSART D.L. Projection methods in chemistry. Chemometrics and Intelligent Laboratory Systems, 65, 97-112, 2003. http://doi.org/10.1016/S0169-7439(02)00107-7
  • 34. CRUZ A.G., CADENA R.S., ALVARO M.B.V.B., SANT’ANA A.S., OLIVEIRA C.A.F., FARIA J.A.F., BOLINI H.M.A., FERREIRA M.M.C. Assessing the use of different chemometric techniques to discriminate low-fat and full-fat yogurts. LWT – Food Science and Technology,50, 210, 2013. http://dx.doi.org/10.1016/j.lwt.2012.05.023
  • 35. KANNEL P.R., LEE S., KANEL S.R., KHAN S.P. Chemometric application in classification and assesment of monitoring locations of an urban river system. Analytica Chimica Acta, 582 (2), 390, 2007. DOI: 10.1016/j. aca.2006.09.006
  • 36. DEMPSTER A.P., LAIRD N.M., RUBIN D.B. Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society, Series B (Methodological), 39, 1, 1977.
  • 37. BOUKOUVALA F., MUZZIO F.J., IERAPETRITOU M.G. Predictive Modeling of Pharmaceutical Processes with Missing and Noisy Data. AIChE Journal, 56 (11), 2860, 2010. DOI: 10.1002/aic.12203
  • 38. SANTOS A., SANTOS R., SILVA M., FIGUEIREDO E., SALES C., COSTA J. C. W. A. A global Expectation-Maximization approach based on memetic algorithm for vibration-based structural damage detection. IEEE Transactions on Instrumentation and Measurement, 66 (4), 661, 2017. DOI: 10.1109/TIM.2017.2663478
  • 39. WOYANN L.G., BENIN G., STORCK L., TREVIZAN D. M., MENEGUZZI C., MARCHIORO V.S., TONATTO M., MADUREIRA A. Estimation of missing values affects important aspects of GGE biplot analysis. Crop Science, 57 (1), 40, 2016. DOI: 10.2135/cropsci2016.02.0100
  • 40. LI G., SHEN H. HUANG J.Z. Supervised sparse and functional principal component analysis. Journal of Computational and Graphical Statistics, 25 (3), 859-878, 2016. DOI: http://dx.doi.org/10.1080/10618600.2015.1064434
  • 41. STANIMIROVA I., WALCZAK B. Classification of data with missing elements and outliers. Talanta, 76 (3), 602, 2008. DOI: 10.1016/j.talanta.2008.03.049
  • 42. LIEW A.W.C., LAW N.F., YAN H. Missing value imputation for gene expression data: computational techniques to recover missing data from available information. Briefings in bioinformatics, 12 (5), 498, 2011. DOI: https://doi.org/10.1093/bib/bbq080
  • 43. ANDERSEN T., CARSTENSEN J., HERNANDEZ-GARCIA E., DUARTE C. M. Ecological thresholds and regime shifts: approaches to identification. Trends in Ecology and Evolution, 24 (1), 49, 2009. DOI: 10.1016/j. tree.2008.07.014
  • 44. HRON K., TEMPL M., FILZMOSER P. Imputation of missing values for compositional data using classical and robust methods. Computational Statistics and Data Analysis, 54 (12), 3095, 2010. DOI:10.1016/j.csda.2009.11.023
  • 45. SCHLOMER G. L., BAUMAN S., CARD N. A. Best practices for missing data management in counseling psychology. Journal of Counseling psychology, 57 (1), 1, 2010. DOI: 10.1037/a0018082
  • 46. BAŃBURA M., MODUGNO M. Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data. Journal of Applied Econometrics, 29 (1), 133, 2014.
  • 47. ILIN A., RAIKO T. Practical approaches to principal component analysis in the presence of missing values. Journal of Machine Learning Research, 11, 1957, 2010.
  • 48. JOLLIFFE I.T. Principal Component Analysis. 2rd Ed. Springer Verlag, New York, 488, 2002.
  • 49. LITTLE R.J., RUBIN D.B. Statistical analysis with missing data. John Wiley and Sons, 2014.
  • 50. KOWALKOWSKI T., ZBYTNIEWSKI R., SZPEJNA J., BUSZEWSKI B. Application of chemometrics in river water classification. Water Research, 40 (4), 744, 2006. DOI: 10.1016/j.watres.2005.11.042
  • 51. UKALSKI K., ŚMIAŁOWSKI T. Multivariate analysis of data from preliminary trials with winter rye, Biul. IHAR, 260/261, 251, 2011.
  • 52. HOWANIEC N., SMOLIŃSKI A. Influence of fuel blend ash components on stream co-gasification of coal and biomass – Chemometric study. Energy, 78, 814, 2014. DOI: https://doi.org/10.1016/j.energy.2014.10.076
  • 53. SMOLIŃSKI A., DROBEK L., DOMBEK V., BĄK A. Modeling of experimental data on trace elements and organic compounds content in industrial waste dumps. Chemosphere, 162, 189-198, 2016. DOI: https://doi. org/10.1016/j.chemosphere.2016.07.086
  • 54. REN H., HOU Z., HUANG M., BAO J., SUN Y., TESFA T., LEUNG L. R. Classifi cation of hydrological parameter sensitivity and evaluation of parameter transferability across 431 US MOPEX basins. Journal of Hydrology, 536, 92, 2016. DOI: https://doi.org/10.1016/j.jhydrol.2016.02.042
  • 55. HOU Z., HUANG M., LEUNG L.R., LIN G., RICCIUTO D.M. Sensitivity of surface flux simulations to hydrologic parameters based on an uncertainty quantifi cation framework applied to the Community Land Model. Journal of Geophysical Research, 117 (15), D15108, 2012. DOI: 10.1029/2012JD017521
  • 56. RAY J., HOU Z., HUANG M., SARGSYAN K., SWILER L. Bayesian calibration of the Community Land Model using surrogates, SIAM/ASA J. Uncertainty Quantification, 3 (1), 199, 2015. DOI: http://dx.doi.org/10.1137/140957998
  • 57. GONG W., DUAN Q., LI J., WANG C., DI Z., DAI Y., YE A., MIAO C. Multi-objective parameter optimization of common land model using adaptive surrogate modeling, Hydrology and Earth System Sciences, 19 (5), 2409, 2015. DOI: 10.5194/hess-19-2409-2015
  • 58. BAO J., HOU Z., HUANG M., LIU Y. On approaches to analyze the sensitivity of simulated hydrologic fluxes to model parameters in the community land model. Water., 7, 6810, 2015. DOI: http://dx.doi.org/10.3390/w7126662
  • 59. DRAY S., PETTORELLI N., CHESSEL D. Multivariate analysis of incomplete mapped data. Trans GIS, 7, 411-422, 2003. DOI: 10.1111/1467-9671.00153
  • 60. LOURENÇO N.D., PAIXÃO F., PINHEIRO H. M., SOUSA A. Use of Spectra in the Visible and Near-Mid-Ultraviolet Range with Principal Component Analysis and Partial Least Squares Processing for Monitoring of Suspended Solids in Municipal Wastewater Treatment Plants. Applied Spectroscopy, 64 (9), 1061, 2010. DOI: 10.1366/000370210792434332
  • 61. DĄBROWSKI K. DĄBROWSKI K. Program ochrony środowiska dla gminy Wolin. W: BIP UM w Wolinie. Wyd. Związek Gmin Wyspy Wolin, Międzyzdroje, 169, 2005.
  • 62. JOSSE J., HUSSON F., PAGÈS J. Handling missing values in Principal Component Analysis, Journal de la Société Française de Statistique 150 (2), 28, 2009.
  • 63. GRAHAM J.W. Missing data analysis: Making it work in the real world. Annual review of psychology, 60, 549, 2009. DOI: 10.1146/annurev.psych.58.110405.085530
  • 64. BRO R.A., SMILDE A.K. Principal component analysis, Anal. Methods, 6, 2812, 2014. DOI: 10.1039/c3ay41907j

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Bibliografia

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