Abstract

Raman spectroscopy and pattern recognition techniques are used to develop a potential method to characterize wood by type. The test data consists of 98 Raman spectra of temperate softwoods and hardwoods, and Brazilian and Honduran tropical woods. A genetic algorithm (GA) is used to extract features (i.e., line intensities at specific wavelengths) characteristic of the Raman profile of each wood-type. The spectral features identified by the pattern recognition GA allow the wood samples to cluster by type in a plot of the two largest principal components of the data. Because principal components maximize variance, the bulk of the information encoded by these spectral features is about differences between wood types. The predictive ability of the descriptors identified by the pattern recognition GA and the principal component map associated with them is validated using an external prediction set consisting of tropical woods and temperate hard and softwoods.

Efficient use of hybrid Genetic Algorithms in the gain optimization of distributed Raman amplifiers

B. Neto, A. L. J Teixeira, N. Wada, and P. S. André
Opt. Express 15(26) 17520-17528 (2007)

Analysis and classification of kidney stones based on Raman spectroscopy

Xiaoyu Cui, Zeyin Zhao, Gejun Zhang, Shuo Chen, Yue Zhao, and Jiao Lu
Biomed. Opt. Express 9(9) 4175-4183 (2018)

Extreme learning machine and genetic algorithm in quantitative analysis of sulfur hexafluoride by infrared spectroscopy

Huan Liu, Jun Zhu, Huan Yin, Qiangqiang Yan, Hong Liu, Shouxin Guan, Qisheng Cai, Jiawen Sun, Shun Yao, and Ruyi Wei
Appl. Opt. 61(10) 2834-2841 (2022)

Detection of captan residues in apple juice using fluorescence spectroscopy combined with a genetic algorithm and support vector machines

Rendong Ji, Zhezhen Jiang, Xiaoyan Wang, Yue Han, Haiyi Bian, Yudong Yang, Liyun Zhuang, and Yulin Zhang
Appl. Opt. 61(12) 3455-3462 (2022)

Highly sensitive Fourier-transform coherent anti-Stokes Raman scattering spectroscopy via genetic algorithm pulse shaping

Matthew Lindley, Julia Gala de Pablo, Ryo Kinegawa, Kotaro Hiramatsu, and Keisuke Goda
Opt. Lett. 46(17) 4320-4323 (2021)