Sesame and chickpea are important crops in Ethiopia because both are major export crops that generate much revenue for both small farmers and the country as a whole. However, there is a lack of information about the fundamental equilibrium moisture content (EMC) relationships among these crops, which would help facilitate better monitoring and storage. Therefore, EMC adsorption and desorption prediction models based on temperature (T) and relative humidity (RH) were developed for the modified Chung-Pfost and modified Henderson models for Kabuli chickpea (KC), black sesame (BS), and white sesame (WS) seeds. The samples for conducting the adsorption and desorption tests were conditioned to various moisture content (MC) levels for the EMC test models. The samples (~500 g) were placed in multiple sealed enclosures equipped with T and RH sensors, which were placed in an environmental chamber where they were exposed to three temperatures (15°C, 25°C, and 35°C). The MCdb ranges used for model development for adsorption and desorption were, respectively, 11.6% to 19.5% and 8.9% to 16.9% for KC samples, 5.0% to 8.7% and 4.3% to 6.9% for BS, and 4.2% to 8.7% and 3.5% to 7.6% for WS. Nonlinear regression was used to determine the model coefficients for the modified Henderson and modified Chung-Pfost equations. The prediction statistics for the adsorption and desorption models yielded an SEE of, respectively, 0.53% and 0.68% MCdb for KC, 0.23% and 0.13% for BS, and 0.28% and 0.25% for WS. The model coefficients obtained in this study will be used in a moisture meter based on EMC measurement, which is currently being used as part of a USAID postharvest project in various African and Asian countries. These EMC models may also be important for other grain operations, which include harvesting, drying, storage, conditioning, and processing.

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Published in Applied Engineering in Agriculture, v. 33, issue 5, p. 737-742.

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This research was funded by the United States Agency for International Development as part of the Feed the Future Innovation Lab for the Reduction of Post-Harvest Loss.