Background and objectives:To maintain the competitiveness of U.S. long-grain rice in U.S. and foreign markets, having translucent whole milled grain is critical. An objective technique to detect grain chalk, opaque areas in the grain, will provide breeders and industry with an effective tool for developing low-chalk varieties or agronomic practices that reduce chalk occurrence. Two instruments developed at Service (USDA-ARS), a single-kernel near-infrared (SKNIR) tube instrument and a silicon-based light-emitting diode (SiLED) high-speed sorter, were compared with two commercially available imaging instruments, WinSEEDLE and SeedCount used for chalk quantification. Three 2-way chalk classifications were defined for single kernels based on visual inspection: (a) <50% or ≥50% opacity or chalk (modified Grain Inspection, Packers & Stockyards Administration [GIPSA]), (b) <10% or ≥10% opacity (10% cutoff), and (c) 100% opacity or 100% translucent (MaxLevel). Findings: The SKNIR method provided the best classification for the modified GIPSA definition with an 82.4% average correct classification (CC), that is, 89% and 76% for nonchalky and chalky kernels, respectively. The WinSEEDLE had the best classification for the 10% cutoff definition, with an 84% CC for nonchalky kernels and a 96% CC for chalky kernels. For the MaxLevel definition, average CCs of both the SKNIR and SiLED methods were similar, at 93% and 95%, respectively. The average CCs were lower for both the WinSEEDLE method and the SeedCount method at 14% and 58%, respectively. These low CC values are a result of using a threshold of 100% for chalky or nonchalky kernels, where a single misclassified pixel within the image will cause misclassification. Calibration models developed for both the SKNIR and SiLED methods indicate that their classifications were based mainly on spectral differences near the adsorption bands for starch, protein, and water content. Conclusions: All of the instruments can be used to classify chalk, but their level of accuracy depends on how chalk is defined.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

No abstract

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.

Intensive cultivation to meet the growing market demand of mangoes for both domestic and export consumption leads to the presence of possible pesticide residues and other agricultural chemicals on the fruit which may pose health hazards since mangoes are eaten as fresh fruit. The dry-extract system involving near infrared spectroscopy (DESIR) was employed using NIR reflectance spectroscopy for detecting pesticide residues on fresh Carabao mango fruit. Best calibration models were achieved using Partial Least Square Regression analysis. Results of spectra of dry extracts of aqueous solutions were encouraging due to its usability for most applications including research but with caution. Regression models for dry extracts from the recovery of water washes of the sprayed fruit were also inspiring for its model fitness (R 2 cv of approximately 0.7-0.81) and RMSECV range of 0.13-1.004 g/L of active ingredient. This result suggested acceptability of NIR as a rapid screening tool for immediate decision making but suspected samples being subjected to the reference GC-MS analysis method.

No abstract