Convenient methods that are capable of determining potentially antimicrobial compounds in both vapour and liquid phases are required (inter alia) to facilitate the development of active packaging materials using natural substances. The suitability of single-drop microextraction (SDME) coupled with gas chromatography-mass spectrometry (GC-MS) for this purpose has been assessed by evaluating its ability to determine a range of analytes (mainly terpenes) in vapour samples and three liquid food simulants - distilled water, 10% (v/v) water/ethanol, and 3% (w/v) acetic acid - by headspace-SDME (HS-SDME) and direct immersion-SDME (DI-SDME), respectively. In this contribution, a screening strategy based on the Hildebrand solubility parameter has been used to build a solvent priority list. Solvents were then tested following the list, taking into account additional factors such as low volatility for HS-SDME or buoyancy and relative miscibility for DI-SDME. Other experimental parameters affecting the performance of SDME (such as drop volume, sampling time and temperature, drop position in the sample vial, sample vial size, stirring rate, filling rate and ionic strength of the sample) were investigated using a Plackett-Burman screening design. The method optimisation was completed by means of response surface modelling (RSM). The methods were validated by characterising relevant performance parameters including their robustness, linear range, accuracy (trueness and precision) and capability of detection as described by the International Organization for Standardization.
Brent crude, the global benchmark, fell $5.91, or 5.22%, to settle at $107.25 a barrel, while U.S. West Texas Intermediate dropped $5.65, or 5.22%, to settle at $102.56 a barrel. Prices declined despite lower output from OPEC+, which produced 1.45 million barrels per day (bpd) below its targets in March, as Russian output began to decrease following sanctions imposed by the West over its invasion of Ukraine, according to a report from the producer alliance seen by Reuters.
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In this thesis we develop methods for dealing with missing data in a univariate response variable when estimating regression parameters. Missing outcome data is a problem in a number of applications, one of which is follow-up studies. In follow-up studies data is collected at two (or more) occasions, and it is common that only some of the initial participants return at the second occasion. This is the case in Paper II, where we investigate predictors of decline in self reported health in older populations in Sweden, the Netherlands and Italy. In that study, around 50% of the study participants drop out. It is common that researchers rely on the assumption that the missingness is independent of the outcome given some observed covariates. This assumption is called data missing at random (MAR) or ignorable missingness mechanism. However, MAR cannot be tested from the data, and if it does not hold, the estimators based on this assumption are biased. In the study of Paper II, we suspect that some of the individuals drop out due to bad health. If this is the case the data is not MAR. One alternative to MAR, which we pursue, is to incorporate the uncertainty due to missing data into interval estimates instead of point estimates and uncertainty intervals instead of confidence intervals. An uncertainty interval is the analog of a confidence interval but wider due to a relaxation of assumptions on the missing data. These intervals can be used to visualize the consequences deviations from MAR have on the conclusions of the study. That is, they can be used to perform a sensitivity analysis of MAR. 17dc91bb1f
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