PubMed · 13856767
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1959-09-19. TESTS for steatorrhoea.. https://pubmed.ncbi.nlm.nih.gov/13856767/
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Biodiesel is usually produced from food-grade vegetable oils that are more expensive than diesel fuel. Therefore, biodiesel produced from food-grade vegetable oil is currently not economically feasible. Waste cooking oils, restaurant grease and animal fats are potential feedstocks for biodiesel. These inexpensive feedstocks represent one-third of the US total fats and oil production, but are currently devoted mostly to industrial uses and animal feed. The characteristics of feedstock are very important during the initial research and production stage. Free fatty acids and moisture reduce the efficiency of transesterification in converting these feedstocks into biodiesel. Hence, this study was conducted to determine the level of these contaminants in feedstock samples from a rendering plant. Levels of free fatty acids varied from 0.7% to 41.8%, and moisture from 0.01% to 55.38%. These wide ranges indicate that an efficient process for converting waste grease and animal fats must tolerate a wide range of feedstock properties.
Although the magnetic field of an MR scanner is very stable under little or no load, it can become less stable under heavy-duty cycle conditions, such as in diffusion tensor imaging (DTI). Uncorrected, such field drifts lead to an apparent image shift along the phase-encoding direction and decreasing effectiveness of fat saturation pulses. A method is presented to adjust the center frequency of all RF pulses and the receiver in real time during the acquisition. No data postprocessing or changes to the sequence timing are necessary. In vivo acquisitions were performed to assess the prolonged effectiveness of fat saturation. Field drifts of approximately 2.5 Hz/min were measured and corrected during DTI acquisitions at b-values of up to 3000 s/mm2. The effectiveness of fat saturation diminished over the duration of an 18-min acquisition when the drift was left uncorrected. The proposed method corrects for apparent image shift and ensures continuously effective fat saturation over the duration of an acquisition.
This paper describes mathematical techniques to correct for analyte-irrelevant optical variability in tissue spectra by combining multiple preprocessing techniques to address variability in spectral properties of tissue overlying and within the muscle. A mathematical preprocessing method called principal component analysis (PCA) loading correction is discussed for removal of inter-subject, analyte-irrelevant variations in muscle scattering from continuous-wave diffuse reflectance near-infrared (NIR) spectra. The correction is completed by orthogonalizing spectra to a set of loading vectors of the principal components obtained from principal component analysis of spectra with the same analyte value, across different subjects in the calibration set. Once the loading vectors are obtained, no knowledge of analyte values is required for future spectral correction. The method was tested on tissue-like, three-layer phantoms using partial least squares (PLS) regression to predict the absorber concentration in the phantom muscle layer from the NIR spectra. Two other mathematical methods, short-distance correction to remove spectral interference from skin and fat layers and standard normal variate scaling, were also applied and/or combined with the proposed method prior to the PLS analysis. Each of the preprocessing methods improved model prediction and/or reduced model complexity. The combination of the three preprocessing methods provided the most accurate prediction results. We also performed a preliminary validation on in vivo human tissue spectra.