PATTERN RECOGNITION INTEGRATED SENSING METHODOLOGIES (PRISMS) IN PHARMACEUTICAL PROCESS VALIDATION, REMOTE SENSING AND ASTROBIOLOGY

Modern analytical instrumentation is capable of creating enormous and complex volumes of data. Analysis of large data volumes are complicated by lengthy analysis time and high computational demand. Incorporating real-time analysis methods that are computationally efficient are desirable for modern analytical methods to be fully utilized. The use of modern instrumentation in on-line pharmaceutical process validation, remote sensing, and astrobiology applications requires real-time analysis methods that are computationally efficient. Integrated sensing and processing (ISP) is a method for minimizing the data burden and sensing time of a system. ISP is accomplished through implementation of chemometric calculations in the physics of the spectroscopic sensor itself. In ISP, the measurements collected at the detector are weighted to directly correlate to the sample properties of interest. This method is especially useful for large and complex data sets. In this research, ISP is applied to acoustic resonance spectroscopy, near-infrared hyperspectral imaging and a novel solid state spectral imager. In each application ISP produced a clear advantage over the traditional sensing method. The limitations of ISP must be addressed before it can become widely used. ISP is essentially a pattern recognition algorithm. Problems arise in pattern recognition when the pattern-recognition algorithm encounters a sample unlike any in the original calibration set. This is termed the false sample problem. To address the false sample problem the Bootstrap Error-Adjusted Single-Sample Technique (BEST, a nonparametric classification technique) was investigated. The BEST-ISP method utilizes a hashtable of normalized BEST points along an asymmetric probability density contour to estimate the BEST multidimensional standard deviation of a sample. The on-line application of the BEST method requires significantly less computation than the full algorithm allowing it to be utilized in real time as sample data is obtained. This research tests the hypothesis that a BEST-ISP metric can be used to detect false samples with sensitivity \u3e 90% and specificity \u3e 90% on categorical data

Modeling the population health impact of accurate and inaccurate perceptions of harm from nicotine

Abstract Background Scientific evidence clearly demonstrates that inhaling the smoke from the combustion of cigarettes is responsible for most of the harm caused by smoking, and not the nicotine. However, a majority of U.S. adults who smoke inaccurately believe that nicotine causes cancer which may be a significant barrier, preventing switching to potentially reduced risk, non-combustible products like electronic nicotine delivery systems (ENDS) and smokeless tobacco (ST). We assessed the population health impact associated with nicotine perceptions. Methods Using a previously validated agent-based model to the U.S. population, we analyzed nationally representative data from the Population Assessment of Tobacco and Health (PATH) study to estimate base case rates of sustained (maintained over four waves) cessation and switching to non-combustible product use, by sex. Nicotine perception scenarios were determined from PATH data. The overall switch rate from smoking in Wave 4 to non-combustible product use in Wave 5 (3.94%) was stratified based on responses to the nicotine perception question “Do you believe nicotine is the chemical that causes most of the cancer caused by smoking cigarettes?”, (four-item scale from “Definitely not” to “Definitely yes”). The relative percent change between the overall and stratified rates, corresponding to each item, was used to adjust the base case rates of switching, to determine the impact, if all adults who smoke exhibited switching behaviors based on responses to the nicotine perceptions question. The public health impact of nicotine perceptions was estimated as the difference in all-cause mortality between the base case and the four nicotine perception scenarios. Results Switch rates associated with those who responded, “Definitely not” (8.39%) resulted in a net benefit of preventing nearly 800,000 premature deaths over an 85-year period. Conversely switch rates reflective of those who responded, “Definitely yes” (2.59%) resulted in a net harm of nearly 300,000 additional premature deaths over the same period. Conclusions Accurate knowledge regarding the role of nicotine is associated with higher switch rates and prevention of premature deaths. Our findings suggest that promoting public education to correct perceptions of harm from nicotine has the potential to benefit public health

An Approach for Predicting Mainstream Cigarette Smoke Harmful and Potentially Harmful Constituent (HPHC) Yields

To ensure quality, consistency, and supply of cigarette products, a manufacturer may change materials, which can affect its product portfolio. Rather than testing each product individually to determine the effect of a change, designed experiments can be conducted using a subset of products, and statistical modeling can be performed to determine the harmful and potentially harmful constituent (HPHC) yields for the remaining products. To demonstrate this, we selected 30 representative cigarette products covering a wide range of tobacco blends, ingredients, and design parameters from a manufacturer's portfolio. Sets of cigarette products used papers produced with one type of manufacturing technology (control products) and two additional cigarette papers (changed products). The physical characteristics of the changed products' papers were similar to the control products but were manufactured using alternative methods, which could lead to differences in their chemical composition. The experiment was controlled to minimize variations among products, manufacturing, and testing. Linear regression was used to model the relationship between HPHC yields of the tested products. Twelve randomly selected products were used for validation by comparing predicted to measured yields. Model predictions were robust; differences between measured and predicted values were within standard repeatability limits, demonstrating the feasibility of this approach.https://doi.org/10.21423/jrs-v07hanne