Department of Plants, Soils, and Climate, Utah State University, Logan, UT 84322, USA.
Key Laboratory of Smart Agriculture Systems, China Agricultural University, Ministry of Education, Beijing 100083, China.
Sensors (Basel). 2022 Mar 8;22(6):2083. doi: 10.3390/s22062083.
Moisture content is a critical variable for the harvesting, processing, storing and marketing of cereal grains, oilseeds and legumes. Efficient and accurate determination of grain moisture content even with advanced nondestructive techniques, remains a challenge due to complex water-retaining biological structures and hierarchical composition and geometry of grains that affect measurement interpretation and require specific grain-dependent calibration. We review (1) the primary factors affecting permittivity measurements used in practice for inferring moisture content in grains; (2) develop novel methods for estimating critical parameters for permittivity modeling including packing density, porosity, water binding surface area and water phase permittivity and (3) represent the permittivity of packs of grains using dielectric mixture theory as a function of moisture content applied to high moisture corn (as a model grain). Grain permittivity measurements are affected by their free and bound water contents, chemical composition, temperature, constituent shape, phase configuration and measurement frequency. A large fraction of grain water is bound exhibiting reduced permittivity compared to that of free water. The reduced mixture permittivity and attributed to hydrophilic surfaces in starches, proteins and other high surface area grain constituents. The hierarchal grain structure (i.e., kernel, starch grain, lamella, molecule) and the different constituents influence permittivity measurements due to their layering, geometry (i.e., kernel or starch grain), configuration and water-binding surface area. Dielectric mixture theory offers a physically-based approach for modeling permittivity of agricultural grains and similar granular media.
水分含量是谷物、油籽和豆类收获、加工、储存和销售的关键变量。即使采用先进的非破坏性技术,高效准确地测定谷物水分含量仍然是一个挑战,这是由于谷物具有复杂的保水生物结构以及层次化的组成和几何形状,这些因素会影响测量结果的解释,需要针对具体谷物进行特定的校准。我们回顾了(1)影响在实践中用于推断谷物水分含量的介电常数测量的主要因素;(2)开发了用于估计介电常数建模的关键参数的新方法,包括堆积密度、孔隙率、水结合表面积和水相介电常数;(3)将颗粒的介电常数表示为与水分含量有关的混合理论函数,应用于高水分玉米(作为模型谷物)。谷物介电常数的测量受到其自由水和束缚水含量、化学成分、温度、组成形状、相结构和测量频率的影响。谷物中的大部分水分是被束缚的,其介电常数比自由水低。由于淀粉、蛋白质和其他高比表面积谷物成分中的亲水性表面,混合物的介电常数降低。层次化的谷物结构(即,内核、淀粉颗粒、薄片、分子)和不同的成分由于其分层、几何形状(即内核或淀粉颗粒)、构型和水结合表面积,会影响介电常数的测量。介电混合理论为模拟农业谷物和类似颗粒状介质的介电常数提供了一种基于物理的方法。