Flexible optimization of variables based on exponential and linear attenuation elimination-binary dragonfly algorithm in near infrared spectroscopic analysis

•Proposing a flexible optimization of variables based on exponential and linear attenuation elimination-binary dragonfly algorithm.•The exponential and linear attenuation functions are used to iteratively eliminate the variables, and the variable space is gradually reduced.•Compared with other metho...

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Veröffentlicht in:Infrared physics & technology Jg. 140; S. 105374
Hauptverfasser: Wang, Honghong, Zhang, Mingjin, Xiong, Zhixin, Yang, Wuye, Wu, Ting, Du, Yiping
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.08.2024
Schlagworte:
Exponential and linear attenuation elimination-binary dragonfly algorithm
Near infrared spectroscopy
Quantitative analysis
Variable selection
Near infrared spectroscopy
Exponential and linear attenuation elimination-binary dragonfly algorithm
Variable selection
Quantitative analysis
ISSN:1350-4495
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Zusammenfassung:•Proposing a flexible optimization of variables based on exponential and linear attenuation elimination-binary dragonfly algorithm.•The exponential and linear attenuation functions are used to iteratively eliminate the variables, and the variable space is gradually reduced.•Compared with other methods, ELAE-BDA algorithm significantly improves the analytical ability of the model. Single Binary Dragonfly Algorithm (Single-BDA) which is an intelligent optimization algorithm, normally needs more calculation time and often obtains unrobust variables. In this study, an Exponential and Linear Attenuation Elimination-Binary Dragonfly Algorithm (ELAE-BDA) is proposed to overcome the problems by combining exponential and linear attenuation functions. The algorithm can quickly eliminate a part of the variables with large RMSECV in the iterative process through the exponential attenuation function first, and use the linear attenuation function at the end of the iteration for meticulous elimination of invalid variables. It avoids the important variables being deleted unexpectedly, greatly improves the running speed of the algorithm, reduces the randomness of results, and significantly improves the analysis ability of the model. Three near-infrared spectral datasets are used as research objects to evaluate the performance of the ELAE-BDA algorithm, and 6 wavelength selection algorithms including Single-BDA, GA, CARS, MC-UVE, SCARS, and fiPLS are used for comparison. The results show that the ELAE-BDA algorithm can obtain results with almost the smallest number of wavelengths and the highest stability. The established PLSR models have the best prediction with the lowest RMSECV and RMSEP, indicating that the proposed algorithm can provide a more accurate and effective results of wavelength optimization for near-infrared spectral analysis.
ISSN:1350-4495
DOI:10.1016/j.infrared.2024.105374