NIR Spectroscopy Detects Chlorpyrifos-Methyl Pesticide Residue in Rough, Brown, and Milled Rice

HighlightsNIR spectroscopy detects quantitative and qualitative levels of chlorpyrifos-methyl residues in bulk rice.Levels of chlorpyrifos-methyl residues in bulk rice can be differentiated at 78% to 100% correct classification.Important NIR wavelengths for chlorpyrifos-methyl residue detection were identified.NIR spectroscopy can be used to detect maximum residue levels of chlorpyrifos-methyl pesticide in rice.Abstract. A rapid technique that uses near-infrared reflectance (NIR) spectroscopy for simultaneous qualitative and quantitative determination of the presence of varying concentrations of chlorpyrifos-methyl in bulk samples of rough, brown, and milled rice was established. Five rice varieties, free of pesticides, obtained from RiceTec Inc. and USDA-ARS Arkansas experimental field were used as rough rice samples and also processed to obtain corresponding brown and milled rice. Rice samples were treated with StorcideTM II containing varying levels of the active ingredient, chlorpyrifos-methyl: 0, 1.5, 3, 6, 9, and 12 ppm for rough rice, 0, 0.75, 1.5, 3, 4.5, and 6 ppm for brown rice, and 0, 0.1, 0.2, 0.4, 0.6, and 0.8 ppm for milled rice. Concentrations of chlorpyrifos-methyl were verified using gas chromatography-mass spectrometry analyses. A commercial NIR spectrometer (950-1650 nm wavelength range) was used to obtain spectra of bulk samples. Using partial least squares analysis for quantitative analysis, independent validation showed that chlorpyrifos-methyl residues in rough, brown, and milled rice are predictable with R2 ranging from 0.702 to 0.839 and standard error of prediction (SEP) of 1.763 to 2.374 for rough rice, R2 ranging from 0.722 to 0.800 and SEP of 0.953 to 1.168 for brown rice, and R2 ranging from 0.693 to 0.789 and SEP of 0.131 to 0.164 for milled rice. For qualitative analysis obtained using discriminant analysis, rough rice samples with concentrations of 0, 1.5, and 3 ppm pooled as low pesticide level (LPL) is distinguishable to 6, 9, and 12 ppm which were pooled as high pesticide level (HPL). Similarly, for brown and milled rice, the lower three concentrations pooled as LPL is distinguishable from the higher three concentrations pooled as HPL. Independent validation showed overall correct classifications ranging from 77.8% to 92.6% for rough rice, 79.6% to 88.9% for brown rice, and 94.4% to 100% for milled rice. Keywords: Food safety, Grain quality, NIR spectroscopy, Pesticide residue, Rice.

Pengujian Filter Fisik (Slow Sand Filter) Untuk Menurunkan Kadar Pestisida Golongan Organoklorin

Organochlorine pesticide is one of the pesticide that has high persistency and toxic. One of the attempt to degrade organochlorine pesticide using cheaper and easier way is using the slow sand filter technology. The research aimed to test the slow sand filter and to obtain the best filter media combination for degrading organochlorine pesticide. The experiment was conducted at the Laboratory of Pesticide and Toxicology, Laboratory of Plant Protection Biotechnology and Laboratory of Chemical and Soil Fertility, Faculty of Agriculutre, Padjadjaran Univeristy from December 2016 – May 2017. The experimental design used the observative and descriptive design with 8 treatments and repeated twice. P1 (activated carbon and gravel), P2 (sand and gravel), P3 (silica sand and gravel), P4 (activated carbon and zeolite), P5 (sand and zeolite), P6 (silica sand and zeolite), P7 (activated carbon, silica sand and zeolite), P8 (activated carbon, sand and gravel). The result showed that all treatments of slow sand filter were able to degrade organochlorine pesticide and the best filter media combination was from P7 with ability to degrade the pesticide level by 82,86%.Key words: slow sand filter, activated carbon, silica sand, sand, gravel, zeolite