• Título:  APPLICATION OF CHEMOMETRICS TO HYPERSPECTRAL IMAGING ANALYSIS OF ENVIRONMENTAL AND AGRICULTURAL SAMPLES
• Autor:  Zhang, Xin
• Universidad:  Universidad de Barcelona
• Departamento:  C- QUIMICA ANALITICA
• Fecha de Lectura:  07/07/2015
• Programa de doctorado:  Química Analítica del Medio Ambiente y la Polución
• Dirección: 
  • TAULER FERRÉ, ROMÀ    (Director)
  • DE JUAN CAPDEVILA, ANNA MARIA    (Tutor/Ponente)
• Tribunal: 
  • ALCALA BERNARDEZ, MANEL    (presidente)
  • JAUMOT SOLER, JOAQUIM    (secretario)
  • Marini, Federico    (vocal)
• Descriptores: 
  • QUIMICA ANALITICA
  • ANALISIS MULTIVARIANTE
• Resumen:
  Hyperspectral imaging is an active research area in food and environment analysis. Hyperspectral images allow obtaining accurate and reliable knowledge about the chemical composition and distribution of the chemical components on the investigated sample surface. Results of hyperspectral image analysis can be used to acquire fundamental understanding of complex chemical systems for research and development, for commercial testing and adulteration studies, in particular in food and environment analysis and in industrial process analysis and control. Hyperspectral imaging datasets are challenging because of their very large size and complexity. Chemometric methods are proposed to reveal the information contained in the analyzed images as much as possible. This Thesis deals with the resolution of hyperspectral imaging data by using chemometric methods, in particular by using appropriate data pretreatment methods and by using Multivariate Curve Resolution (MCR) methods. The main contribution of the present Thesis is the study and implementation of the MCR-ALS (Multivariate Curve Resolution Alternating Least Squares) method for the resolution of hyperspectral images, collected by remote sensing (airborne or space borne Earth observation instrument) and by micro-spectroscopy imaging. Specifically, in this Thesis work, we explore the combination of chemometric and hyperspectral imaging methods for the resolution of spectra (signatures) and spatial distribution maps of the chemical constituents of a sample. The ultimate goal of this study is to improve the analysis and interpretation of hyperspectral imaging data by taking advantage of different chemometric powerful tools. Local rank/selectivity properties describing the spatial information of spectroscopic images can be used as a constraint to increase the performance of MCR methods significantly, decreasing rotation ambiguity uncertainties. Different multivariate resolution methods were compared, such as MCR-ALS, Independent Component Analysis (ICA), Principal Component Analysis (PCA), and Minimum Volume Simplex Analysis (MVSA), Multivariate Curve Resolution-Function Minimization (MCR-FMIN), MCR-BANDS and FAC-PACK. All these approaches have been used for the evaluation of the extension of rotation ambiguities remaining in the results after their application. Several hyperspectral images provided by standard and widely used instruments such as NASA¿s Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS), Raman hyperspectral imaging Spectrometer, and Infrared hyperspectral imaging Spectrometer have been used as example of data sets to test the different methods, in particular to test the MCR-ALS method. The effectiveness of MCR-ALS is illustrated by providing exhaustive comparisons with state-of-the-art methods for spectral unmixing using both simulated and real hyperspectral data sets.