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ARS Home » Plains Area » Manhattan, Kansas » Center for Grain and Animal Health Research » Stored Product Insect and Engineering Research » Research » Publications at this Location » Publication #381219

Research Project: Impacting Quality through Preservation, Enhancement, and Measurement of Grain and Plant Traits

Location: Stored Product Insect and Engineering Research

Title: Developing a multi-spectral NIR LED-based instrument for detection of pesticide residues containing chlorpyrifos-methyl in rough, brown and milled rice

item RODRIGUEZ, F. - Don Mariano Marcos Memorial State University
item Armstrong, Paul
item Maghirang, Elizabeth
item YAPTENCO, K. - University Of The Philippines Los Banos
item Scully, Erin
item ARTHUR, FRANKLIN - Retired ARS Employee
item Brabec, Daniel - Dan
item Adviento-Borbe, Arlene
item SUMINISTRADO, D. - University Of The Philippines Los Banos

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/1/2020
Publication Date: 11/1/2020
Citation: Rodriguez, F.S., Armstrong, P.R., Maghirang, E.B., Yaptenco, K.F., Scully, E.D., Arthur, F.H., Brabec, D.L., Adviento-Borbe, A.A., Suministrado, D.C. 2020. Developing a multi-spectral NIR LED-based instrument for detection of pesticide residues containing chlorpyrifos-methyl in rough, brown and milled rice. Transactions of the ASABE. 36(6):983-993.

Interpretive Summary: Rice is the most consumed staple food by humans, particularly in Asian countries, making it imperative that rice safety is given high priority. The presence of pesticide residues in rice and the possible adverse effects on human health associated with consuming residues represent a major concern. Chlorpyrifos-methyl is a commonly used insecticide used to eradicate insects in rice grain warehouses and if improperly applied, can have adverse human health effects. A low cost near infrared (NIR) instrument, using light emitting diodes (LEDs), was developed to provide a rapid technique to determine the presence of chlorpyrifos-methyl in bulk samples of rough (paddy) rice, brown and milled rice. Rice was treated with different concentrations of chlorpyrifos-methyl based on the maximum residue limits (MRL) for each type of rice. Measurements with the low-cost instrument resulted in poor results in trying to measure or discriminate insecticide residue the samples. A commercial NIR instrument, synthetically limited to the wavelengths of the LED instrument, showed much better performance indicating that hardware improvements to the LED instrument may help it perform adequately.

Technical Abstract: To comply with maximum residue limits (MRLs) for the insecticide chlorpyrifos-methyl in rice, slow and expensive lab-based procedures are currently used. Recently the use of a commercial near infrared reflectance (NIR) instrument (Perten DA7200) was shown by the authors to provide good quantitative and qualitative measurements for screening. However, this instrument is still lab-based and generally not suited for point of sale testing in many countries. To provide a field deployable version of this technique, an existing light emitting diode (LED)-based instrument which provides discrete NIR wavelength illumination and reflectance spectra over the range of 850-1550 nm was tested. Rough, brown and milled rice samples were prepared and treated to contain chlorpyrifos-methyl levels of 0-12 ppm, 0-6ppm and 0-0.8 ppm, respectively, which encompass their MRLs. Spectra were collected from these samples and analyzed for quantitative and qualitative measurement using partial least squares regression (PLS) and discriminant analysis (DA). PLS and DA for the existing LED-based instrument yielded poor results for most of the models developed and was generally considered unusable. Simulations for LED-based instruments were also evaluated using segments of spectra from the DA7200 to represent LED illumination. For the simulation of the existing LED-based instrument, LEDPrototype1, using the wavelengths of the existing LED-based instrument yielded 70.4% to 100% correct classification with DA for rough, milled, and brown rice but quantitative prediction was poor. Simulation of a second LED instrument, LEDPrototype2, with spectral segments selected based on significant wavelength regions from PLS regressions coefficients obtained from the DA7200 in the earlier study showed improved performance with R2 of 0.59 to 0.82 and correct classifications of 71.3 to 100%. An actual LED based instrument with this capability could provide a quick screening tool to determine if MRLs are exceeded.