Replacement Orthogonal Wavelengths Selection as a new method for multivariate calibration in spectroscopy
| Autor: | Mohammad Goodarzi, Silvina E. Fioressi, Pablo R. Duchowicz, Daniel E. Bacelo |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
NEAR-INFRARED SPECTROSCOPY
Computer science ORTHOGONALIZATION Feature selection 02 engineering and technology 01 natural sciences Analytical Chemistry ROWS-MLR Range (statistics) Spectroscopy Selection (genetic algorithm) 010401 analytical chemistry Near-infrared spectroscopy Ciencias Químicas 021001 nanoscience & nanotechnology 0104 chemical sciences Wavelength Química Analítica 0210 nano-technology REPLACEMENT METHOD Row Orthogonalization Algorithm FCAM-PLS CIENCIAS NATURALES Y EXACTAS |
| Zdroj: | Microchemical Journal. 145:872-882 |
| ISSN: | 0026-265X |
| DOI: | 10.1016/j.microc.2018.11.054 |
| Popis: | Wavelength selection is a critical step in multivariate calibration. Variable selection methods are used to find the most relevant variables, leading to improved prediction accuracy, while simplifying both the built models and their interpretation. In addition, different spectrophotometer designs and measurement principles result in non-destructive technologies applied in many fields, such as agriculture, food chemistry and pharmaceutics. However, an on-chip or portable device does not allow acquiring data from a large number of wavelengths. Therefore, the most informative combination of a limited number of variables should be selected. The Replacement Orthogonal Wavelengths Selection (ROWS) method is described here as a new method. This algorithm aims at selecting as few wavelengths as possible, while keeping or improving the prediction performance of the model, compared to when no variable selection is applied. The ROWS is applied to several near infrared spectroscopic data sets leading to improved analytical figures of merits upon wavelength selection in comparison to a built PLS model using entire spectral range. The performance of the ROWS-MLR method was compared to the FCAM-PLS method. The resulting models are not significantly different from those of FCAM-PLS; however, it involves a significantly smaller amount of variables. Fil: Goodarzi, Mohammad. University of Texas; Estados Unidos Fil: Bacelo, Daniel Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Belgrano; Argentina Fil: Fioressi, Silvina Ethel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Belgrano; Argentina Fil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina |
| Databáze: | OpenAIRE |
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