Design considerations for future radionuclide aerosol monitoring systems.

Pacific Northwest National Laboratory (PNNL) staff developed the Radionuclide Aerosol Sampler Analyzer (RASA) for worldwide aerosol monitoring in the 1990s. Recently, researchers at PNNL and Creare, LLC, have investigated possibilities for how RASA could be improved, based on lessons learned from more than 15 years of continuous operation, including during the Fukushima Daiichi Nuclear Power Plant disaster. Key themes addressed in upgrade possibilities include having a modular approach to additional radionuclide measurements, optimizing the sampling/analyzing times to improve detection location capabilities, and reducing power consumption by using electrostatic collection versus classic filtration collection. These individual efforts have been made in a modular context that might constitute retrofits to the existing RASA, modular components that could improve a manual monitoring approach, or a completely new RASA. Substantial optimization of the detection and location capabilities of an aerosol network is possible and new missions could be addressed by including additional measurements.

Chemometric analysis of multiple species of Bacillus bacterial endospores using infrared spectroscopy: discrimination to the strain level.

Previous work using infrared spectroscopy has shown potential for rapid discrimination between bacteria in either their sporulated or vegetative states, as well as between bacteria and other common interferents. For species within one physiological state, however, distinction is far more challenging, and requires chemometrics. In the current study, we have narrowed the field of study by eliminating the confounding issues of vegetative cells as well as growth media and focused on using IR spectra to distinguish only between different species all in the sporulated state. Using principal component analysis (PCA) and a classification method based upon similarity measurements, we demonstrate a successful identification rate to the species level of 85% for Bacillus spores grown and sporulated in a glucose broth medium.

QSAR modeling based on the bias/variance compromise: a harmonious and parsimonious approach.

Modeling quantitative structure-activity relationships (QSAR) is considered with an emphasis on prediction. An abundance of methods are available to develop such models. Using a harmonious approach that balances the bias and variance of predictions, the best calibration models are identified relative to the bias and variance criteria used. Criteria utilized to determine the adequacy of models are the root mean square error of calibration (RMSEC) and validation (RMSEV), respective R2 values, and the norm of the regression vector. QSAR data from the literature are used to demonstrate concepts. For these data sets and criteria used, it is suggested that models obtained by ridge regression (RR) are more harmonious and parsimonious than models obtained by partial least squares (PLS) and principal component regression (PCR) when the data is mean-centered. The most harmonious RR models have the best bias/variance tradeoff, reflected by the smallest RMSEC, RMSEV, and regression vector norms and the largest calibration and validation R2 values. The most parsimonious RR models have the smallest effective rank.