Submitted to: Applied Engineering in Agriculture
Publication Type: Peer reviewed journal
Publication Acceptance Date: 1/1/2006
Publication Date: N/A
Citation: Interpretive Summary: Moisture content (MC) measurement of grain during storage would enhance storage management by determining storage changes associated with aeration, natural convection, and mold and insect development. All of these can have a have significant effect on grain quality. Measurement of relative humidity (RH) and temperature (T) of grain during storage is feasible with small low-cost sensors. These sensors can be used to predict moisture content using RH and T. Prediction relies on water vapor adsorption and desorption behavior between the air and grain but varies by grain type and the agronomic conditions during grain development. The measurement of common grain properties and their use to improve MC prediction, would enhance the use of RH and T sensing for grain storage. This research used grain properties such as protein content, hardness, etc and near-infrared reflectance (NIR) spectroscopy, for several wheat varieties, to determine if better methods for MC prediction could be developed. The results showed that grain properties, in conjunction with RH and T, were not able to improve MC prediction and that NIR spectroscopy was not able to predict adsorption and desorption behavior. The inability of NIR spectroscopy to predict behavior indicates that the primary chemical components of wheat had little effect on the adsorption and desorption behavior.
Technical Abstract: Measurement of relative humidity (RH) within a grain storage is becoming feasible with small, low-cost RH sensors. Measurement of RH can give an indirect measurement of grain moisture content through use of equilibrium moisture content (EMC) equations, however, there is variation in EMC relationships due to grain variety and different agronomic growing conditions making prediction accuracy less than ideal Determining EMC relationships for specific samples is not feasible because it is time consuming. Because of this, EMC model parameters and grain physical and chemical properties were examined for relationships that could be used to predict EMC parameters or EMC behavior classified by cluster analysis. This would ultimately result in more accurate predictions. Grain properties examined were bulk density, protein content, single-kernel hardness, diameter and mass. Near-infrared reflectance (NIR) spectroscopy was also examined for prediction of EMC parameters and EMC behavior. Parameters for the modified Chung-Pfost equation were determined for 47 varieties of hard red winter, hard white winter, soft red winter, and Durum wheat. Multiple linear models between EMC parameters and grain attributes were found to be poor. The highest coefficient of determination (R squared) was 0.14 for parameter B of the Modified Chung-Pfost equation and included only kernel mass. Similarly, models between cluster-distance values from cluster analysis and grain attributes were poor. Cluster distance and kernel mass had the highest R squared of 0.20. NIR spectroscopy was also unable to predict EMC model parameters or cluster-distance values. Overall, results indicate that the chemical and physical attributes measured in this study did not influence EMC behavior. The inability of NIR spectroscopy to predict EMC behavior indicates that other chemical constituents not measured in this study but potentially measurable by NIR spectroscopy, do not influence EMC behavior.