Synthesis of a linear static function for grain moisture meter with capacitive sensors

Moisture content is a grain quality factor, a parameter which changes during the processes of storage and processing and determines consumer properties of different food products. OIML organization in its international recommendation OIML R59 "Moisture Meters for Cereal Grain and Oilseeds" restricts maximal permissible value of moisture meters uncertainty to not more than 3% of relative full scale error. Main task of the research is in receiving linear static function for the grain moisture meter with four capacitive sensors. Method of Least Squares and general linear regression instruments had been used for that purpose. Analyzing the graphs of modified static function for different moist substances it was possible to say that it happened to be far more effective than initial static function and the static function received from a first-order polynomial after the LS method implementation. Root mean estimator was calculated for initial static function, the static function received with the LS method and static function, received after general linear regression implementation as an integral difference between nominal and calculated values of moisture content. Corresponding root mean estimator values were 1.3062%, 1.1616% and 0.4158%, that proves the effectiveness of a static function modified with the general linear regression instruments.