Biomedical subjects

Julian Morris

Publications and source records attributed to Julian Morris.

4 recordsLinked to original sources

PubMed2006

Extracting chemical information from spectral data with multiplicative light scattering effects by optical path-length estimation and correction.

When analyzing complex mixtures that exhibit sample-to-sample variability using spectroscopic instrumentation, the variation in the optical path length, resulting from the physical variations inherent within the individual samples, will result in significant multiplicative light scattering perturbations. Although a number of algorithms have been proposed to address the effect of multiplicative light scattering, each has associated with it a number of underlying assumptions, which necessitates additional information relating to the spectra being attained. This information is difficult to obtain in practice and frequently is not available. Thus, with a view to removing the need for the attainment of additional information, a new algorithm, optical path-length estimation and correction (OPLEC), is proposed. The methodology is applied to two near-infrared transmittance spectral data sets (powder mixture data and wheat kernel data), and the results are compared with the extended multiplicative signal correction (EMSC) and extended inverted signal correction (EISC) algorithms. Within the study, it is concluded that the EMSC algorithm cannot be applied to the wheat kernel data set due to core information for the implementation of the algorithm not being available, while the analysis of the powder mixture data using EISC resulted in incorrect conclusions being drawn and hence a calibration model whose performance was unacceptable. In contrast, OPLEC was observed to effectively mitigate the detrimental effects of physical light scattering and significantly improve the prediction accuracy of the calibration models for the two spectral data sets investigated without any additional information pertaining to the calibration samples being required.

Algorithms↗

PubMed2005

Enhancing the signal-to-noise ratio of X-ray diffraction profiles by smoothed principal component analysis.

X-ray diffraction is one of the most widely applied methodologies for the in situ analysis of kinetic processes involving crystalline solids. However, due to its relatively high detection limit, it has only limited application in the context of crystallizations from liquids. Methods that can improve the detection limit of X-ray diffraction are therefore highly desirable. Signal processing approaches such as Savitzky-Golay, maximum likelihood, stochastic resonance, and wavelet transforms have been used previously to preprocess X-ray diffraction data. Since all these methods only utilize the frequency information contained in the single X-ray diffraction profile being processed to discriminate between the signals and the noise, they may not successfully identify very weak but important peaks especially when these weak signals are masked by severe noise. Smoothed principal component analysis (SPCA), which takes advantage of both the frequency information and the common variation within a set of profiles, is proposed as a methodology for the preprocessing of the X-ray diffraction data. Two X-ray diffraction data sets are used to demonstrate the effectiveness of the proposed approach. The first was obtained from mannitol-methanol suspensions, and the second data set was generated from slurries of L-glutamic acid (GA) in methanol. The results showed that SPCA can significantly improve the signal-to-noise ratio and hence lower the detection limits (approximately 0.389% g/mL for mannitol-methanol suspensions and 0.4 wt % for beta-form GA in GA-methanol slurries comprising mixtures of both alpha- and beta-forms of GA) thereby providing an important contribution to crystallization process performance monitoring.

Crystallization↗

PubMed2005

Correction of temperature-induced spectral variations by loading space standardization.

With a view to maintaining the validity of multivariate calibration models for chemical processes affected by temperature fluctuations, loading space standardization (LSS) is proposed. Through the application of LSS, multivariate calibration models built at temperatures other than those of the test samples can provide predictions with an accuracy comparable to the results obtained at a constant temperature. Compared with other methods, designed for the same purpose, such as continuous piecewise direct standardization, LSS has the advantages of straightforward implementation and good performance. The methodology was applied to shortwave NIR spectral data sets measured at different temperatures. The results showed that LSS can effectively remove the influence of temperature variations on the spectra and maintain the predictive abilities of the multivariate calibration models.

Journal Article↗

PubMed2002

The relationship between risk analysis and the precautionary principle.

Definitions of the precautionary principle (PP) are reviewed with particular reference to the role of risk assessment. In general, the PP is employed as a means of justifying decisions that are contrary to the conclusions of a formal risk assessment. Even where risk assessment is accepted as part of a precautionary approach, its importance in subsequent decision-making tends to be undermined by application of the PP. The implications for the future of risk assessment-based decisions in areas as diverse as environmental protection and food safety are briefly considered.

Environmental Pollutants↗