Abstract

A genetic algorithm (GA) for variable selection in partial least squares (PLS) regression that incorporates adaptive boosting to identify informative wavelengths in near-infrared (NIR) spectra has been developed. Three studies demonstrating the advantages of incorporating an adaptive boosting routine into a GA that employs the root mean square error of calibration as its fitness function are highlighted: (1) prediction of hydroxyl number of terpolymers from NIR diffuse reflectance spectra; (2) calibration of acetone from NIR transmission spectra of mixtures of water, acetone, t-butyl alcohol and isopropyl alcohol; and (3) determination of the active pharmaceutical ingredients in drug tablets from NIR diffuse reflectance spectra. The performance of the GA with adaptive boosting to select wavelengths was compared with one without adaptive boosting. For all three NIR data sets, variable selected PLS models developed by a GA with adaptive boosting performed better. Analysis of the wavelengths selected by the GA with adaptive boosting also demonstrate that chemical information indicative of the analyte was captured by the selected wavelengths.

© 2017 The Author(s)

High accuracy determination of copper in copper concentrate with double genetic algorithm and partial least square in laser-induced breakdown spectroscopy

Haochen Li, Meizhen Huang, and Huidi Xu
Opt. Express 28(2) 2142-2155 (2020)

Fourier based partial least squares algorithm: new insight into influence of spectral shift in “frequency domain”

H. Y. Bian, Y. L. Zhang, W. R. Gao, and J. Gao
Opt. Express 27(3) 2926-2936 (2019)

Error analysis of the spectral shift for partial least squares models in Raman spectroscopy

Haiyi Bian and Jing Gao
Opt. Express 26(7) 8016-8027 (2018)

Rapid detection of talc content in flour based on near-infrared spectroscopy combined with feature wavelength selection

Changhao Bao, Changhao Zeng, Jinming Liu, and Dongjie Zhang
Appl. Opt. 61(19) 5790-5798 (2022)

Pattern recognition of visible and near-infrared spectroscopy from bayberry juice by use of partial least squares and a backpropagation neural network

Haiyan Cen, Yidan Bao, and Yong He
Appl. Opt. 45(29) 7679-7683 (2006)