Trace-level
environmental data typically include values near or
below detection and quantitation thresholds where health effects may
result from low-concentration exposures to one chemical over time
or to multiple chemicals. In a cook stove case study, bias in dibenzo[a,h]anthracene
concentration means and standard deviations (SDs) was assessed following
censoring at thresholds for selected analysis approaches: substituting
threshold/2, maximum likelihood estimation, robust regression on order
statistics, Kaplan–Meier, and omitting censored observations.
Means and SDs for gas chromatography–mass spectrometry-determined
concentrations were calculated after censoring at detection and calibration
thresholds, 17% and 55% of the data, respectively. Threshold/2 substitution
was the least biased. Measurement values were subsequently simulated
from two log-normal distributions at two sample sizes. Means and SDs
were calculated for 30%, 50%, and 80% censoring levels and compared
to known distribution counterparts. Simulation results illustrated
(1) threshold/2 substitution to be inferior to modern after-censoring
statistical approaches and (2) all after-censoring approaches to be
inferior to including all measurement data in analysis. Additionally,
differences in stove-specific group means were tested for uncensored
samples and after censoring. Group differences of means tests varied
depending on censoring and distributional decisions. Investigators
should guard against censoring-related bias from (explicit or implicit)
distributional and analysis approach decisions.
Remote sensing of nutrient disorders has become more common in recent years. Most research has considered one or two nutrient disorders and few studies have sought to distinguish among multiple macronutrient deficiencies. This study was conducted to provide a baseline spectral characterization of macronutrient deficiencies in flue-cured tobacco (Nicotiana tabacum L.). Reflectance measurements were obtained from greenhouse-grown nutrient-deficient plants at several stages of development. Feature selection methods including information entropy and first and second derivatives were used to identify wavelengths useful for discriminating among these deficiencies. Detected variability was primarily within wavelengths in the visible spectrum, while near-infrared and shortwave-infrared radiation contributed little to the observed variability. Principal component analysis was used to reduce data dimensionality and the selected components were used to develop linear discriminant analysis models to classify the symptoms. Classification models for young, intermediate, and mature plants had overall accuracies of 92%, 82%, and 75%, respectively, when using 10 principal components. Nitrogen, sulfur, and magnesium deficiencies exhibited greater classification accuracies, while phosphorus and potassium deficiencies demonstrated poor or inconsistent results. This study demonstrates that spectral analysis of flue-cured tobacco is a promising methodology to improve current scouting methods.
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