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Open AccessArticle

Acquisition of Sorption and Drying Data with Embedded Devices: Improving Standard Models for High Oleic Sunflower Seeds by Continuous Measurements in Dynamic Systems

Institute of Agricultural Engineering, Universität Hohenheim, Garbenstrasse 9, 70599 Stuttgart, Germany
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Agriculture 2019, 9(1), 1; https://doi.org/10.3390/agriculture9010001
Received: 7 October 2018 / Revised: 16 December 2018 / Accepted: 18 December 2018 / Published: 20 December 2018
(This article belongs to the Special Issue Sensors Application in Agriculture)
Innovative methods were used to determine both sorption and drying data at temperatures typically found in the handling of agricultural products. A robust sorption measurement system using multiple microbalances and a high precision through flow laboratory dryer, both with continuous data acquisition, were employed as the basis for a water vapor deficit based approach in modeling the sorption and drying behavior of high oleic sunflower seeds. A coherent set of data for sorption (Temperature T = 25–50 °C, water activity aw = 0.10–0.95) and for drying (T = 30–90 °C, humidity of the drying air x = 0.010–0.020 kg·kg−1) was recorded for freshly harvested material. A generalized single-layer drying model was developed and validated (R2 = 0.99, MAPE = 8.3%). An analytical solution for predicting effective diffusion coefficients was also generated (R2 = 0.976, MAPE of 6.33%). The water vapor pressure deficit-based approach allows for an easy integration of meaningful parameters recorded during drying while maintaining low complexity of the underlying equations in order for embedded microcontrollers with limited processing power to be integrated in current agro-industrial applications. View Full-Text
Keywords: dynamic vapor sorption; high-precision dryer; modeling; water vapor pressure deficit dynamic vapor sorption; high-precision dryer; modeling; water vapor pressure deficit
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MDPI and ACS Style

Munder, S.; Argyropoulos, D.; Müller, J. Acquisition of Sorption and Drying Data with Embedded Devices: Improving Standard Models for High Oleic Sunflower Seeds by Continuous Measurements in Dynamic Systems. Agriculture 2019, 9, 1.

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