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Improvement of the Raman detection system for pesticide residues on/in fruits and vegetables
Author(s): Yan Li; Yankun Peng; Chen Zhai; Kuanglin Chao; Jianwei Qin
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Paper Abstract

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.

Paper Details

Date Published: 1 May 2017
PDF: 6 pages
Proc. SPIE 10217, Sensing for Agriculture and Food Quality and Safety IX, 102170U (1 May 2017); doi: 10.1117/12.2262534
Show Author Affiliations
Yan Li, China Agricultural Univ. (China)
Yankun Peng, China Agricultural Univ. (China)
Chen Zhai, China Agricultural Univ. (China)
Kuanglin Chao, Agricultural Research Service (United States)
Jianwei Qin, Agricultural Research Service (United States)


Published in SPIE Proceedings Vol. 10217:
Sensing for Agriculture and Food Quality and Safety IX
Moon S. Kim; Kuanglin Chao; Bryan A. Chin; Byoung-Kwan Cho, Editor(s)

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