Intelligent Identification of Two Pyrethriod Pesticide in Tea by Flurescence Spectroscopy

Ji-hua, 张吉华 Zhang; Zhimin, 赵志敏 Zhao; Wenjie, 张文杰 Zhang

doi:10.3788/fgxb20163708.1023

Cited by 4 publications

(1 citation statement)

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“…Gu et al [26] proposed a new detection algorithm for infrared dim and small targets using saliency and grayscale morphological reconstruction. Zhang et al [27] applied a morphological method to obtain the location of peaks in 2D fluorescence, which made the identification of two pesticides in tea liquor via peak point matching possible.…”

Section: Introductionmentioning

confidence: 99%

Morphological Grayscale Reconstruction and ATLD for Recognition of Organic Pollutants in Drinking Water Based on Fluorescence Spectroscopy

Shi

Mao

Cao

et al. 2019

Water

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This paper proposes a morphological grayscale reconstruction method combined with an alternating trilinear decomposition (ATLD) and threshold method based on 3D fluorescence spectroscopy to detect pollutants present at low concentrations in drinking water. First, the morphological grayscale reconstruction method was used to locate the fluorescence peaks of pollutants by comparing the original and reconstructed spectra obtained through expansion. The signal in the characteristic spectral region was then enhanced using an amplification factor. Feature extraction was subsequently performed by ATLD, and the threshold method was used to qualitatively distinguish water quality. By comparing the proposed method with the direct use of the ATLD and threshold method—which is a commonly used feature-extraction method—this study found that the application of the morphological grayscale reconstruction method can extrude characteristics of 3D fluorescence spectra. Given the typical spectral characteristics of phenol, salicylic acid, and rhodamine B, they were selected as experimental organic pollutants. Results illustrated that the morphological grayscale reconstruction with ATLD improved the spectral signal-to-noise ratio of pollutants and can effectively identify organic pollutants, especially those present at low concentrations.

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Section: Introductionmentioning

confidence: 99%