Detection of pesticide presence on round cabbages using visible shortwave near infrared spectroscopy

Pesticides have long been used in cabbage industry to control pests. This study aimed to investigate the potential application of visible shortwave near infrared spectroscopy for detection of typical pesticide (deltamethrin) on round cabbages. In this study, a total of 60 round cabbages were used. T...

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Bibliographic Details
Main Authors: Che Mohammad Ishkandar El-Rahimin, Che Dini Maryani, Mat Nawi, Nazmi, Janius, Rimfiel, Mazlan, Norida, Muhamad Radzi, Chut Afifa
Format: Conference or Workshop Item
Language:English
Published: Institute of Plantation Studies, Universiti Putra Malaysia 2017
Online Access:http://psasir.upm.edu.my/id/eprint/58878/1/Technical_Paper_8.pdf
http://psasir.upm.edu.my/id/eprint/58878/
http://spel2.upm.edu.my/webupm/upload/dokumen/penerbitan/20180101232338ICBAA2017_Technical_Paper_8.pdf
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Summary:Pesticides have long been used in cabbage industry to control pests. This study aimed to investigate the potential application of visible shortwave near infrared spectroscopy for detection of typical pesticide (deltamethrin) on round cabbages. In this study, a total of 60 round cabbages were used. The sample were divided into four batches. Three batches of round cabbage were sprayed with deltamethrin at three different concentrations level of pesticides namely low, medium and high with values of 0.08, 0.11 and 0.14 % (v/v), respectively. Spectral data of the cabbage samples were collected using visible shortwave near infrared spectrometer (VSWNIRS) with the wavelength range between 200 to 1100 nm. The spectral data was pretreated using multiple scattering correction (MSC) method in order to obtain optimal prediction values. Gas chromatography was used to determine the multi-residue limit (MRL) value of the samples. Calibration and prediction models were developed to correlate the spectral data with MRL values using partial least square regression (PLS) method. The calibration model produced the values of coefficient of determination (R2) and root mean square errors (RMSEP) of 0.98 and 0.02, respectively. The prediction models gave good R2 and RMSEC values of 0.94 and 0.04, respectively. These results demonstrated that the proposed spectroscopic measurement provide a promising technique for pesticide detection at different level of concentration on round cabbage.