Time, cost, and casualties associated with demining efforts underscore the need for improved detection techniques. Reduction in the number of false positives by directly detecting the explosive material, rather than casing material, is desirable. The desired field sensor must, at a minimum, demonstrate reproducibility, the necessary level of sensitivity, portability, instrumental stability, and fast system response times. Ideally, vibrational spectroscopic techniques have the potential to remove false positives, since every chemical has a unique bond structure. Herein, we demonstrate the capabilities of surface-enhanced Raman spectroscopy to detect the chemical vapor signature emanating from buried TNT-based landmines. We present reproducible results obtained from blind tests controlled by the Defense Advanced Research Projects Agency (DARPA) that demonstrate vapor detection of 2,4-dinitrotoluene at concentration levels of 5 ppb or less. The results presented used acquisition times of 30 s on a fieldable system and showed that SERS can be a significant improvement over current landmine detection methods.

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Due to their heterogeneous structure and variability in form, individual corn (Zea mays L.) kernels present an optical challenge for nondestructive spectroscopic determination of their chemical composition. Increasing demand in agricultural science for knowledge of specific traits in kernels is driving the need to find high-throughput methods of examination. In this study macroscopic near-infrared (NIR) reflectance hyperspectral imaging was used to measure small sets of kernels in the spectroscopic range of 950 nm to 1700 nm. Image analysis and principal component analysis (PCA) were used to determine kernel germ from endosperm regions as well as to define individual kernels as objects out of sets of kernels. Partial least squares (PLS) analysis was used to predict oil or oleic acid concentrations derived from germ or full kernel spectra. The relative precision of the minimum cross-validated root mean square error (RMSECV) and root mean square error of prediction (RMSEP) for oil and oleic acid concentration were compared for two sets of two hundred kernels. An optimal statistical prediction method was determined using a limited set of wavelengths selected by a genetic algorithm. Given these parameters, oil content was predicted with an RMSEP of 0.7% and oleic acid content with an RMSEP of 14% for a given corn kernel.

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A method of rapid, nondestructive chemical and physical analysis of individual maize (Zea mays L.) kernels is needed for the development of high value food, feed, and fuel traits. Near-infrared (NIR) spectroscopy offers a robust nondestructive method of trait determination. However, traditional NIR bulk sampling techniques cannot be applied successfully to individual kernels. Obtaining optimized single kernel NIR spectra for applied chemometric predictive analysis requires a novel sampling technique that can account for the heterogeneous forms, morphologies, and opacities exhibited in individual maize kernels. In this study such a novel technique is described and compared to less effective means of single kernel NIR analysis. Results of the application of a partial least squares (PLS) derived model for predictive determination of percent oil content per individual kernel are shown.

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