27 research outputs found

    The fitness–fatigue model : what’s in the numbers?

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    Purpose: The purpose of this commentary is to outline some of the pitfalls when using the fitness-fatigue model to unravel the interaction between training load and performance. By doing so, we encourage sport scientists and coaches to interpret the parameters from the model with some extra caution. Conclusions: Caution is needed when interpreting the fitness-fatigue model since the parameter values are influenced by the starting parameter values, the modeling technique, and the input of the model. Also, the use of general constants should be avoided since they do not account for interindividual differences and differences between training-load methods. Therefore, we advise sport scientists and coaches to use the model as a way to work more data-informed rather than working data-driven

    Two-dimensional moisture content and size measurement of pharmaceutical granules after fluid bed drying using near-infrared chemical imaging

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    In pharmaceutical wet granulation, drying is a critical step in terms of energy and material consumption, whereas granule moisture content and size are important process outcomes that determine tabletting performance. The drying process is, however, very complex due to the multitude of interacting mechanisms on different scales. Building robust physical models of this process therefore requires detailed data. Current data collection methods only succeed in measuring the average moisture content of a size fraction of granules, whereas this property rather follows a distribution that, moreover, contains information on the drying patterns. Therefore, a measurement method is devised to simultaneously characterise the moisture content and size of individual pharmaceutical granules. A setup with near-infrared chemical imaging (NIR-CI) is used to capture an image of a number of granules, in which the absorbance spectra are used for deriving the moisture content of the material and the size of the granules is estimated based on the amount of pixels containing pharmaceutical material. The quantification of moisture content based on absorption spectra is performed with two different regression methods, Partial Least Squares regression (PLSR) and Elastic Net Regression (ENR). The method is validated with particle size data for size determination, loss-on-drying (LOD) data of average moisture contents of granule samples and, finally, batch fluid bed experiments in which the results are compared to the most detailed method to date. The individual granule moisture contents confirmed again that granule size is an important factor in the drying process. The measurement method can be used to gain more detailed experimental insight in different fluidisation and particulate processes, which will allow building of robust process models
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