In this article, we present an experimental approach to determine the milk fat content using scattered light intensity profiles. The elements of the scattering (Mueller) matrix have been shown to provide valuable information about variation of the optical properties of scattering particles. The scattering behavior of fat and casein in terms of the scattering matrix elements was experimentally determined for milk with varying fat levels ranging from 0.05 wt% (skim) to 3.20 wt% (whole). Three of the scattering Mueller matrix elements, specifically S 11 , S 12 /S 11 , and S 33 /S 11 , were found to be sensitive to the number of fat particles in milk. These results indicate that it should be possible to develop a reliable sensor based on the measurement of these scattering elements, which will allow for the development of a robust, in-line sensor to be used in food processing. In addition, an attempt was made to model the phenomena using a relatively simple approach based on single scattering with a size distribution. The disagreement between the model and experiments suggests that a more comprehensive model is needed which can account for multiple scattering.
A 785 nm laser dispersive Raman system was installed in a hazardous chemical production plant to monitor two reactors, a still, and a holding tank with measurement directly through sight glasses. Compositional information for several raw materials, intermediates, and products was obtained using partial least squares (PLS) calibrations. Discriminate analysis was used to exclude extremely poor spectra while including usable ones. Derivative/standard normal variate (SNV) treatment was found to be effective for correcting for background fluorescence and for large differences in spectral intensity. Final regression equations incorporated temperature fluctuations, occasional fluorescence, some sunlight effects, large intensity variations, and large compositional changes. Various installation problems were solved via data treatment or mechanical changes. The Raman system provides information for control of the process, resulting in cost savings and improved safety.
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