Feature Extraction in Signal Regression: A Boosting Technique for Functional Data Regression

Main objectives of feature extraction in signal regression are the improvement of accuracy of prediction on future data and identification of relevant parts of the signal. A feature extraction procedure is proposed that uses boosting techniques to select the relevant parts of the signal. The proposed blockwise boosting procedure simultaneously selects intervals in the signal’s domain and estimates the effect on the response. The blocks that are defined explicitly use the underlying metric of the signal. It is demonstrated in simulation studies and for real-world data that the proposed approach competes well with procedures like PLS, P-spline signal regression and functional data regression. The paper is a preprint of an article published in the Journal of Computational and Graphical Statistics. Please use the journal version for citation

Bayesian modelling and quantification of Raman spectroscopy

Raman spectroscopy can be used to identify molecules such as DNA by the characteristic scattering of light from a laser. It is sensitive at very low concentrations and can accurately quantify the amount of a given molecule in a sample. The presence of a large, nonuniform background presents a major challenge to analysis of these spectra. To overcome this challenge, we introduce a sequential Monte Carlo (SMC) algorithm to separate each observed spectrum into a series of peaks plus a smoothly-varying baseline, corrupted by additive white noise. The peaks are modelled as Lorentzian, Gaussian, or pseudo-Voigt functions, while the baseline is estimated using a penalised cubic spline. This latent continuous representation accounts for differences in resolution between measurements. The posterior distribution can be incrementally updated as more data becomes available, resulting in a scalable algorithm that is robust to local maxima. By incorporating this representation in a Bayesian hierarchical regression model, we can quantify the relationship between molecular concentration and peak intensity, thereby providing an improved estimate of the limit of detection, which is of major importance to analytical chemistry

Practical Considerations on Indirect Calibration in Analytical Chemistry

Indirect or methodological calibration in chemical analysis is outlined. The establishment of calibration curves is introduced and discussed. Linear calibration is presented and considered in three scenarios commonly faced in chemical analysis: external calibration (EC) when there are no matrix effects in the sample analysis; standard addition calibration (SAC) when these effects are present and internal standard calibration (ISC) in cases of intrinsic variability of the analytical signal or possible losses of the analyte in stages prior to the measurement. In each kind of calibration, the uncertainty and confidence interval for the determined analyte concentration are given

A software system for emission spectrometry

A computer system was developed for an emission spectrometry facility consisting of a direct current (DC) argon arc spectrograph optically coupled to an inductively coupled plasma multichannel spectrometer. Custom hardware and software were designed to control analytical functions and perform data acquisition. The software system was designed to make operation of the facility simple for routine operation and flexible for research and development. Special software was written to collect data under controlled conditions to characterize and monitor system response. One sequence collects intensity versus time data on all channels and displays the data graphically. These profiles are useful in studying the effects of operating parameters on measurement precision. Another special sequence performs calibration using a spline curve fit procedure. Routines were also written to measure dark currents and signals from a standard tungsten halogen lamp mounted in place of the DC arc. For quality control purposes, histories of these values are kept and monitored for excess scatter or drift

Multivariate adaptive regression splines for estimating riverine constituent concentrations

Regression-based methods are commonly used for riverine constituent concentration/flux estimation, which is essential for guiding water quality protection practices and environmental decision making. This paper developed a multivariate adaptive regression splines model for estimating riverine constituent concentrations (MARS-EC). The process, interpretability and flexibility of the MARS-EC modelling approach, was demonstrated for total nitrogen in the Patuxent River, a major river input to Chesapeake Bay. Model accuracy and uncertainty of the MARS-EC approach was further analysed using nitrate plus nitrite datasets from eight tributary rivers to Chesapeake Bay. Results showed that the MARS-EC approach integrated the advantages of both parametric and nonparametric regression methods, and model accuracy was demonstrated to be superior to the traditionally used ESTIMATOR model. MARS-EC is flexible and allows consideration of auxiliary variables; the variables and interactions can be selected automatically. MARS-EC does not constrain concentration-predictor curves to be constant but rather is able to identify shifts in these curves from mathematical expressions and visual graphics. The MARS-EC approach provides an effective and complementary tool along with existing approaches for estimating riverine constituent concentrations

BOOTSTRAP ENHANCED N-DIMENSIONAL DEFORMATION OF SPACE WITH ACOUSTIC RESONANCE SPECTROSCOPY

Acoustic methods can often be used with limited or no sample preparations making them ideal for rapid process analytical technologies (PATs). This dissertation focuses on the possible use of acoustic resonance spectroscopy as a PAT in the pharmaceutical industry. Current good manufacturing processes (cGMP) need new technologies that have the ability to perform quality assurance testing on all products. ARS is a rapid and non destructive method that has been used to perform qualitative studies but has a major drawback when it comes to quantitative studies. Acoustic methods create highly non linear correlations which usually results in high level computations and chemometrics. Quantification studies including powder contamination levels, hydration amounts and active pharmaceutical ingredient (API) concentrations have been used to test the hypothesis that bootstrap enhanced n-dimensional deformation of space (BENDS) could be used to overcome the highly non linear correlations that occur with acoustic resonance spectroscopy (ARS) eliminating a major drawback with ARS to further promote the device as a possible process analytical technology (PAT) in the pharmaceutical industry. BENDS is an algorithm that has been created to calculate a reduced linear calibration model from highly non linear relationships with ARS spectra. ARS has been shown to correctly identify pharmaceutical tablets and with the incorporation of BENDS, determine the hydration amount of aspirin tablets, D-galactose contamination levels of Dtagatose powders and the D-tagatose concentrations in resveratrol/D-tagatose combinatory tablets

ANISOTROPIC POLARIZED LIGHT SCATTER AND MOLECULAR FACTOR COMPUTING IN PHARMACEUTICAL CLEANING VALIDATION AND BIOMEDICAL SPECTROSCOPY

Spectroscopy and other optical methods can often be employed with limited or no sample preparation, making them well suited for in situ and in vivo analysis. This dissertation focuses on the use of a near-infrared spectroscopy (NIRS) and polarized light scatter for two such applications: the assessment of cardiovascular disease, and the validation of cleaning processes for pharmaceutical equipment.There is a need for more effective in vivo techniques for assessing intravascular disorders, such as aortic aneurysms and vulnerable atherosclerotic plaques. These, and other cardiovascular disorders, are often associated with structural remodeling of vascular walls. NIRS has previously been demonstrated as an effective technique for the analysis of intact biological samples. In this research, traditional NIRS is used in the analysis of aortic tissue samples from a murine knockout model that develops abdominal aortic aneurysms (AAAs) following infusion of angiotensin II. Effective application of NIRS in vivo, however, requires a departure from traditional instrumental principles. Toward this end, the groundwork for a fiber optic-based catheter system employing a novel optical encoding technique, termed molecular factor computing (MFC), was developed for differentiating cholesterol, collagen and elastin through intervening red blood cell solutions. In MFC, the transmission spectra of chemical compounds are used to collect measurements directly correlated to the desired sample information.Pharmaceutical cleaning validation is another field that can greatly benefit from novel analytical methods. Conventionally cleaning validation is accomplished through surface residue sampling followed by analysis using a traditional analytical method. Drawbacks to this approach include cost, analysis time, and uncertainties associated with the sampling and extraction methods. This research explores the development of in situ cleaning validation methods to eliminate these issues. The use of light scatter and polarization was investigated for the detection and quantification of surface residues. Although effective, the ability to discriminate between residues was not established with these techniques. With that aim in mind, the differentiation of surface residues using NIRS and MFC was also investigated