Choose optimal wavelengths for calibration by combining the combination-making algorithm with multivariate linear regression

	Choose optimal wavelengths for calibration by combining the combination-making algorithm with multivariate linear regression
其他题名	论文其他题名
	Lu Y. J.; Zhang J.; Piao R. G.; Chen X. D.
	2004
发表期刊	Chinese Journal of Analytical Chemistry
ISSN	0253-3820
卷号	32 期号:8 页码:1116-1119
摘要	Fixed-filter spectrometer, diode arrays spectrometer and so on are very common spectrometers which dont operate in the consistently scan mode. The key to this kind of spectrometer is how to find a optimal wavelength combination rapidly and accurately for getting a fine calibration. The conventional methods to choose the optimal wavelengths for calibration are forward stepwise multiple linear regression in combination with correlation chart, which is based on the t test result of all the calibration wavelengths. However, the wavelengths chosen by conventional method are sometimes not so powerful, as a result it contains uncertainty and is undependable. In this paper the combination-making algorithm in combinatorics is introduced in near infrared spectroscopy, the work of finding out the optimum wavelength combination is realized efficiently and successfully, the final result of prediction of the optimum calibration model is satisfied.
收录类别	SCI
语种	中文
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/25797
专题	中科院长春光机所知识产出
推荐引用方式 GB/T 7714	Lu Y. J.,Zhang J.,Piao R. G.,et al. Choose optimal wavelengths for calibration by combining the combination-making algorithm with multivariate linear regression[J]. Chinese Journal of Analytical Chemistry,2004,32(8):1116-1119.
APA	Lu Y. J.,Zhang J.,Piao R. G.,&Chen X. D..(2004).Choose optimal wavelengths for calibration by combining the combination-making algorithm with multivariate linear regression.Chinese Journal of Analytical Chemistry,32(8),1116-1119.
MLA	Lu Y. J.,et al."Choose optimal wavelengths for calibration by combining the combination-making algorithm with multivariate linear regression".Chinese Journal of Analytical Chemistry 32.8(2004):1116-1119.

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