DocumentCode :
3190745
Title :
Nondestructive Detection of Pesticide Residue on Longan Surface Based on Near Infrared Spectroscopy
Author :
Fen, Dai ; Tiansheng, Hong ; Kun, Zhang ; Ya, Hong
Author_Institution :
Key Lab. of Key Technol. on Agric. Machine & Equip., South China Agric. Univ., Guangzhou, China
Volume :
2
fYear :
2010
fDate :
11-12 May 2010
Firstpage :
781
Lastpage :
783
Abstract :
In this research, nondestructive detection of a slathered pesticides trichlorfon on longan surface based on near infrared spectroscopy was studied. Firstly, principal component analysis (PCA) was applied to analyze the spectra data within the wavelength range from 500 nm to 1000 nm. Satisfied results were achieved from clustering based on the scores of PC1 and PC2. Then a BP artificial neural network (ANN) was built to detect the pesticide residue with the scores of PC1 and PC2 as inputs. The results indicated that the ratio of correct detection was 93%. This study may provide a novel way for fast and nondestructive fruit surface pesticide residue detection.
Keywords :
agricultural products; backpropagation; chemical engineering computing; chemical products; food products; infrared spectroscopy; neural nets; nondestructive testing; principal component analysis; BP artificial neural network; clustering; longan surface; near infrared spectroscopy; nondestructive fruit surface pesticide residue detection; principal component analysis; slathered pesticides trichlorfon; spectra data; wavelength 500 nm to 1000 nm; wavelength range; Artificial neural networks; Chemical industry; Data analysis; Educational technology; Infrared detectors; Infrared spectra; Principal component analysis; Spectroscopy; Spraying; Variable speed drives; artificial neural network (ANN); longan; near infrared spectroscopy; pesticide residue; principal component analysis (PCA);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on
Conference_Location :
Changsha
Print_ISBN :
978-1-4244-7279-6
Electronic_ISBN :
978-1-4244-7280-2
Type :
conf
DOI :
10.1109/ICICTA.2010.477
Filename :
5522639
Link To Document :
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