.png){kind=icon}
.png){kind=icon}
Return
Characteristic wavelength optimization for partial least squares regression using improved flower pollination algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.saa.2023.123095.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Wavelength selection is crucial to the success of near-infrared (NIR) spectroscopy analysis as it considerably improves the generalization of the multivariate model and reduces model complexity. This study proposes a new wavelength selection method, interval flower pollination algorithm (iFPA), for spectral variable selection in the partial least squares regression (PLSR) model. The proposed iFPA consists of three phases. First, the flower pollination algorithm is applied to search for informative spectral variables, followed by variable elimination. Subsequently, the iFPA performs a local search to determine the best continuous interval spectral variables. The interpretability of the selected variables is assessed on three public NIR datasets (corn, diesel and soil datasets). Performance comparison with other competing wavelength selection methods shows that the iFPA used in conjunction with the PLSR model gives better prediction performance, with the root mean square error of prediction values of 0.0096-0.0727, 0.0015-3.9717 and 1.3388-29.1144 are obtained for various responses in corn, diesel and soil datasets, respectively.
Keywords:
Flower pollination algorithm
Near-infrared spectroscopy
Partial least squares regression
Wavelength selection
Journal
IF:
4.6
Papers:
2.4W
Citations:
5.5W