Detecting and Identifying Spectral Anomalies Using Wavelet Processing
Autor: | David J. Veltkamp, Chris L. Stork, Bruce R. Kowalski |
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Rok vydání: | 1998 |
Předmět: |
Chemical process
business.industry Computer science 010401 analytical chemistry Wavelet transform Pattern recognition Function (mathematics) 01 natural sciences 0104 chemical sciences Domain (software engineering) 010309 optics Identification (information) symbols.namesake Wavelet Fourier transform 0103 physical sciences Principal component analysis symbols Artificial intelligence business Instrumentation Spectroscopy |
Zdroj: | Applied Spectroscopy. 52:1348-1352 |
ISSN: | 1943-3530 0003-7028 |
Popis: | An automated method integrating wavelet processing and techniques from multivariate statistical process control (MSPC) is presented, providing a means for the simultaneous localization, detection, and identification of disturbances in spectral data. A defining property of the wavelet transform is its ability to map a one-dimensional chemical spectrum into a two-dimensional function of wavelength and scale. Therefore, unlike the traditional MSPC approach where disturbance detection is carried out in the original wavelength domain by using a single principal component analysis (PCA) model, detection employing wavelet transform processing results in the generation of multiple models within the wavelength-scale domain. Provided that the spectral disturbance can be localized within a subregion of the wavelength-scale domain through an advantageous choice of basis set, the method described allows the identification of the underlying disturbance. The utility of the proposed method in localizing, detecting, and identifying spectral disturbances is demonstrated by using real near-infrared measurements, suggesting its general applicability in spectroscopic monitoring of chemical processes. |
Databáze: | OpenAIRE |
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