Model selection for geostatistical models.

We consider the problem of model selection for geospatial data. Spatial correlation is often ignored in the selection of explanatory variables, and this can influence model selection results. For example, the importance of particular explanatory variables may not be apparent when spatial correlation is ignored. To address this problem, we consider the Akaike Information Criterion (AIC) as applied to a geostatistical model. We offer a heuristic derivation of the AIC in this context and provide simulation results that show that using AIC for a geostatistical model is superior to the often-used traditional approach of ignoring spatial correlation in the selection of explanatory variables. These ideas are further demonstrated via a model for lizard abundance. We also apply the principle of minimum description length (MDL) to variable selection for the geostatistical model. The effect of sampling design on the selection of explanatory covariates is also explored. R software to implement the geostatistical model selection methods described in this paper is available in the Supplement.

Identification of sporulated and vegetative bacteria using statistical analysis of fourier transform mid-infrared transmission data.

A combined mid-infrared spectroscopic/statistical modeling approach for the discrimination and identification, at the strain level, of both sporulated and vegetative bacterial samples is presented. Transmission mode spectra of bacteria dried on ZnSe windows were collected using a Fourier transform mid-infrared (FT-IR) spectrometer. Five Bacillus bacterial strains (B. atrophaeus 49337, B. globigii Dugway, B. thuringiensis spp. kurstaki 35866, B. subtilis 49760, and B. subtilis 6051) were used to construct a reference spectral library and to parameterize a four-step statistical model for the systematic identification of bacteria. The statistical methods used in this initial feasibility study included principal component analysis (PCA), classification and regression trees (CART), and Mahalanobis distance calculations. Internal cross-validation studies successfully classified 100% of the samples into their correct physiological state (sporulated or vegetative) and identified 67% of the samples correctly as to their bacterial strain. Analysis of thirteen blind samples, which included reference and other bacteria, nonbiological materials, and mixtures of both nonbiological and bacterial samples, yielded comparable accuracy. The primary advantage of this approach is the accurate identification of unknown bacteria, including spores, in a matter of minutes.

Identification of bacterial spores using statistical analysis of Fourier transform infrared photoacoustic spectroscopy data.

Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS) has been applied for the first time to the identification and speciation of bacterial spores. A total of forty specimens representing five strains of Bacillus spores (Bacillus subtilis ATCC 49760, Bacillus atrophaeus ATCC 49337, Bacillus subtilis 6051, Bacillus thuringiensis subsp. kurstaki, and Bacillus globigii Dugway) were analyzed. Spores were deposited, with minimal preparation, into the photoacoustic sample cup and their spectra recorded. Principal component analysis (PCA), classification and regression trees (CART), and Mahalanobis distance calculations were used on this spectral library to develop algorithms for step-wise classification at three levels: (1) bacterial/nonbacterial, (2) membership within the spore library, and (3) bacterial strain. Internal cross-validation studies on library spectra yielded classification success rates of 87% or better at each of these three levels. Analysis of fifteen blind samples, which included five samples of spores already in the spectral library, two samples of closely related Bacillus globigii 01 spores not in the library, and eight samples of nonbacterial materials, yielded 100% accuracy in distinguishing among bacterial/nonbacterial samples, membership in the library, and bacterial strains within the library.