A fast determination of insecticide deltamethrin by spectral data fusion of UV-vis and NIR based on extreme learning machine

Qianqian Li; Yue Huang; Jixiong Zhang; Shungeng Min

doi:10.1016/j.saa.2020.119119

A fast determination of insecticide deltamethrin by spectral data fusion of UV-vis and NIR based on extreme learning machine

Spectrochim Acta A Mol Biomol Spectrosc. 2021 Feb 15:247:119119. doi: 10.1016/j.saa.2020.119119. Epub 2020 Oct 27.

Authors

Qianqian Li¹, Yue Huang², Jixiong Zhang³, Shungeng Min³

Affiliations

¹ School of Chinese Material Medica, Beijing University of Chinese Medicine, Beijing 100029, China.
² College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China. Electronic address: huangyue@cau.edu.cn.
³ College of Science, China Agricultural University, Beijing 100193, China.

PMID: 33157400
DOI: 10.1016/j.saa.2020.119119

Abstract

Spectral data fusion strategies combined with the extreme learning machine (ELM) algorithm was applied to determine the active ingredient in deltamethrin formulation. Ultraviolet-visible spectroscopy (UV-vis) is a rapid and sensitive detection method for specific components that are sensitive to ultraviolet irradiation. Alternatively, near-infrared spectroscopy (NIR) technology can be applied over a broader range. To determine a feasible method with a higher sensitivity and broader application range, the active ingredient of deltamethrin formulation was comprehensively investigated by combining the spectral data fusion strategy with ELM by employing UV-vis, NIR and fusion strategies, individually. Consequently, the results demonstrated that the low-level fusion strategy exhibited better predictive ability (lower RMSEP of 0.0645% and higher R² of 0.9978) than mid-level fusion and individual methods. ELM combined with data fusion is proved to be an efficient method for the rapid analysis of deltamethrin formulations. Furthermore, this study provides a potential approach for pesticide quality control as well as on-site monitoring.

Keywords: Data fusion; Extreme learning machine; Near-infrared; Quality control; Ultraviolet–visible.