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Scale-based normalization of spectral data.

Timothy W Randolph
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Abstract

Classification of data that arise as signals or images often requires a standardization step so that information extracted from biologically equivalent signals can be quantified for comparison across classes. Differences in global trend, total energy, high-frequency noise and/or local background can arise from variabilities due to instrumentation or conditions during data collection. This article considers some common ways in which such variation is adjusted for and introduces a generalization of the popular "standard normal variate" transformation. Based on a wavelet decomposition this generalization provides increased flexibility for normalizing spectral data affected by local background noise. Examples from three types of spectroscopy data illustrate the method and its properties.

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Diagnosis, Computer-AssistedHumansImage Processing, Computer-AssistedModels, TheoreticalReference ValuesRelative Value ScalesSignal Processing, Computer-AssistedSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationSpectroscopy, Near-InfraredSpectrum AnalysisSpectrum Analysis, RamanJournal ArticleResearch Support, N.I.H., Extramural

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Timothy W Randolph. 2006. Scale-based normalization of spectral data.. https://doi.org/10.3233/cbm-2006-23-405

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