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Classification of spectral signatures in biological aerosols
Umeå University, Faculty of Science and Technology, Department of Physics.
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In this thesis multivariate methods were used to evaluate pretreatment methods, such as normalization, as well as classification possibilities of data collected with Laser Induced Breakdown Spectroscopy (LIBS). The LIBS system that FOI is currently developing for the purpose of classifying biological airborne threats was used to collect data from ten different samples in a laboratory environment. Principal component analysis (PCA) shows that it is possible to observe differences between samples using the two types of data acquired from the LIBS system, i.e., 2D CCD camera images and 1D spectra extracted from the image. Further results using partial least squares discriminant analysis (PLS-DA) show that normalization of the data only has visual effects in the PCA score-plots and do not affect the models predictability. Results also show that cropping and binning the pixels in the image is possible to some extent without losing significant predictability.

Abstract [sv]

I det här examensarbetet har multivariat dataanalys använts för att utvärdera förbehandlings-metoder, t.ex. normalisering, och klassificeringsmöjligheter för spektralt data insamlat med Laser Induced Breakdown Spectroscopy (LIBS). Systemet som FOI utvecklat under de senaste åren i syfte att analysera enstaka partiklar i luft har använts i laboratoriemiljö för att samla data från tio olika prover. En principalkomponentanalys (PCA) visar att det går att se skillnader mellan proverna utifrån de två datatyperna som erhålls från LIBS, 2D CCD bilder och spektra extraherat från dessa bilder. Vidare så visar resultat från partiell minstakvadrat-diskriminantanalys (PLS-DA) att normalisering av data endast har en visuell effekt i PCA score-plottar och inte har någon inverkan på modellernas prediktionsfömåga. Resultat visar även på möjligheter att beskära och binna pixlar i bilderna i viss utstreckning utan att förlora signifikant prediktionsförmåga.

Place, publisher, year, edition, pages
2013. , 30 p.
National Category
Mathematical Analysis
URN: urn:nbn:se:umu:diva-64002OAI: diva2:586115
Subject / course
Examensarbete i teknisk fysik
Educational program
Master of Science Programme in Engineering Physics
Available from: 2013-01-19 Created: 2013-01-11 Last updated: 2013-01-19Bibliographically approved

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