Mr. Yan Li Profile

Yan Li

at China Agricultural Univ

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Publications (2)

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Proceedings Article | 30 April 2019 Paper

Nondestructive rapid detection of benzoyl peroxide in flour based on Raman hyperspectral technique

Yan Li, Yankun Peng, Kuanglin Chao, Jianwei Qin, Sagar Dhakal

Proceedings Volume 11016, 110160G (2019) https://doi.org/10.1117/12.2517454

KEYWORDS: Raman spectroscopy, Quantitative analysis, Visualization, Nondestructive evaluation, Imaging systems, Statistical analysis, Image acquisition, Luminescence, Hyperspectral imaging, Light sources

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In recent years, the quality and safety issues related to flour and pasta products have attracted great attention from the society. The quality of flour will directly affect the quality of downstream pasta products, as well as the physical and mental health and economic benefits of consumers. In this study, the illegal additive benzoyl peroxide in flour was the research object, and the rapid real-time non-destructive detection of benzoyl peroxide in flour was realized by Raman hyperspectral technique. By comparing the Raman spectra of pure benzoyl peroxide and pure flour, several Raman spectral characteristic peaks of benzoyl peroxide and their assignments were found. Characteristic peaks with strong signal at 1001 cm^-1 and 1777 cm^-1 were extracted for quantitative analysis. A gradient concentration of benzoyl peroxide-doped flour samples from 1% to 0.05% was prepared. And a series of pretreatment including S-G 5-point smoothing and background removal were performed to extract the number of effective benzoyl peroxide pixels in the mixed sample. And the proportion of benzoyl peroxide pixel points in total pixel points with different benzoyl peroxide concentrations was acquired. By comparing the relationship between the proportion and the concentration of benzoyl peroxide, a quantitative analysis model for the benzoyl peroxide doping in flour was established. The verification results show that there was good correlation between the proportion and the concentration of benzoyl peroxide. Both the averaged benzoyl peroxide signal intensities of effective pixel points and the number of effective pixels were combined for quantitative analysis. The research provided a methodological support for the detection of additives in flour by hyperspectral techniques and was a reference for the detection of dopants in food.

Proceedings Article | 1 May 2017 Paper

Improvement of the Raman detection system for pesticide residues on/in fruits and vegetables

Yan Li, Yankun Peng, Chen Zhai, Kuanglin Chao, Jianwei Qin

Proceedings Volume 10217, 102170U (2017) https://doi.org/10.1117/12.2262534

KEYWORDS: Agriculture, Imaging systems, Statistical analysis, Chaos, Spherical lenses, Detector development, Control systems, Switches, Raman scattering, Surface enhanced Raman spectroscopy

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Pesticide residue is one of the major challenges to fruits safety, while the traditional detection methods of pesticide residue on fruits and vegetables can’t afford the demand of rapid detection in actual production because of timeconsuming. Thus rapid identification and detection methods for pesticide residue are urgently needed at present. While most Raman detection systems in the market are spot detection systems, which limits the range of application. In the study, our lab develops a Raman detection system to achieve area-scan thorough the self-developed spot detection Raman system with a control software and two devices. In the system, the scanning area is composed of many scanning spots, which means every spot needs to be detected and more time will be taken than area-scan Raman system. But lower detection limit will be achieved in this method. And some detection device is needed towards fruits and vegetables in different shape. Two detection devices are developed to detect spherical fruits and leaf vegetables. During the detection, the device will make spherical fruit rotate along its axis of symmetry, and leaf vegetables will be pressed in the test surface smoothly. The detection probe will be set to keep a proper distance to the surface of fruits and vegetables. It should make sure the laser shins on the surface of spherical fruit vertically. And two software are used to detect spherical fruits and leaf vegetables will be integrated to one, which make the operator easier to switch. Accordingly two detection devices for spherical fruits and leaf vegetables will also be portable devices to make it easier to change. In the study, a new way is developed to achieve area-scan result by spot-scan Raman detection system.

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