High-speed photographs and videos of an ICP are used to examine the fates of solid particles produced by laser ablation (LA) in an inductively coupled plasma (ICP). The trajectories, lifetimes, and emission behavior of particles traversing the plasma are studied under a variety of conditions. Desolvated particles from a nebulized yttrium solution and particles ablated from a Y 2 O 3 pellet are mixed and introduced simultaneously into an ICP. High-resolution digital photographs and video sequences are captured using shutter speeds of approximately 65 ms. The plasma behavior of particles generated by a quadrupled Nd:YAG laser (266 nm) and an ArF excimer laser (193 nm) are compared, and either argon or helium is used as the transport gas through the ablation cell. No red emission clouds from YO are visible in the ICP when a 20 mL min 21 nebulizer is used to spray a 2000 ppm aqueous Y solution, suggesting that the wet droplets from this nebulizer desolvate almost completely before entering the ICP. These desolvated particles from the nebulized solution atomize and ionize like the small dry particulates from laser ablation. However, many large ablated particulates are observed to fly through the plasma intact, possibly contributing to signal noise, deposition on the sampler and skimmer cones, and elemental fractionation.1008

The objectives of this research were: (1) to develop a technique for creating calibrations to predict the constituent concentrations of single maize kernels from near-infrared (NIR) hyperspectral image data, and (2) to evaluate the feasibility of an NIR hyperspectral imaging spectrometer as a tool for the quality analysis of single maize kernels. Single kernels of maize were analyzed by hyperspectral transmittance in the range of 750 to 1090 nm. The transmittance data were standardized using an opal glass transmission standard and converted to optical absorbance units. Partial least squares (PLS) regression and principal components regression (PCR) were used to develop predictive calibrations for moisture and oil content using the standardized absorbance spectra. Standard normal variate, detrending, multiplicative scatter correction, wavelength selection by genetic algorithm, and no preprocessing were compared for their effect on model predictive performance. The moisture calibration achieved a best standard error of cross-validation (SECV) of 1.20%, with relative performance determinant (RPD) of 2.74. The best oil calibration achieved an SECV of 1.38%, with an RPD of only 1.45. The performance and subsequent analysis of the oil calibration reveal the need for improved methods of single-seed reference analysis.

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