An improved partial least-squares regression method for Raman spectroscopy
| Autor: | Hanan Anis, Ali Momenpour Tehran Monfared |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Chemistry 010401 analytical chemistry Sorting Feature selection Regression analysis 01 natural sciences Atomic and Molecular Physics and Optics Regression 0104 chemical sciences Analytical Chemistry Root mean square 03 medical and health sciences 030104 developmental biology Partial least squares regression Instrumentation Unit-weighted regression Algorithm Spectroscopy Selection (genetic algorithm) |
| Zdroj: | Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 185:98-103 |
| ISSN: | 1386-1425 |
| DOI: | 10.1016/j.saa.2017.05.038 |
| Popis: | It is known that the performance of partial least-squares (PLS) regression analysis can be improved using the backward variable selection method (BVSPLS). In this paper, we further improve the BVSPLS based on a novel selection mechanism. The proposed method is based on sorting the weighted regression coefficients, and then the importance of each variable of the sorted list is evaluated using root mean square errors of prediction (RMSEP) criterion in each iteration step. Our Improved BVSPLS (IBVSPLS) method has been applied to leukemia and heparin data sets and led to an improvement in limit of detection of Raman biosensing ranged from 10% to 43% compared to PLS. Our IBVSPLS was also compared to the jack-knifing (simpler) and Genetic Algorithm (more complex) methods. Our method was consistently better than the jack-knifing method and showed either a similar or a better performance compared to the genetic algorithm. |
| Databáze: | OpenAIRE |
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