A method of determining the moisture content of bulk wheat grain

Chua, Hong Siang; Parkinson, G.; Haigh, Arthur; Gibson, Arthur

doi:10.1016/j.jfoodeng.2005.12.027

Cited by 10 publications

(6 citation statements)

References 8 publications

(7 reference statements)

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“…Visible spectrum analysis [58] Image analysis of the statistics of intensity distribution in images compared with established humidity patterns RF-based moisture content and Artificial Neural Network [60,61] RF signal strength analysis and use of Random Forest method with one input feature (RSSI/WSN) Prediction using ANN and SVR modeling techniques [57,62] Estimation by neural network training with inputs: number of days after sowing, air temperature, relative air humidity, hourly wind speed and 6 h precipitation Grain moisture determination by complex permittivity and compression [63] Electrical analysis of complex permittivity in compressed grain sample Microwave attenuation at 10.5 GHz and humidity density [64] Uses microwave attenuation at 10.5 GHz and humidity density…”

Section: Methods Operating Principlementioning

confidence: 99%

“…For the determination of moisture content in wheat, a new methodology has been developed that uses multilayer perceptron neural network (MLP) and support vector regression (SVR) techniques [62]. Five inputs are used to train the neural network: the number of days after planting, air temperature, relative humidity, wind speed per hour and 6 h precipitation.…”

Section: Prediction Using Ann and Svr Modeling Techniquesmentioning

confidence: 99%

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New Sensing Technologies for Grain Moisture

et al. 2022

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In this review, we present a description of conventional technologies and new advances for the estimation and sense of moisture content in grains. The operating principles, accuracies and response times are described. The review considers an exhaustive search of scientific developments and patent registrations. It was concluded that most of the new developments correspond to methods of which the measurement principles are based on the analysis of the electrical characteristics of the grains. In addition, new methods of image analysis have been implemented that provide measurements with reduced response times and with precisions of utility for its application in the agro-industrial field. In addition to this, wireless communication technologies have been implemented that allow the implementation of moisture measurement methods in moving grains within processing chains.

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Section: Methods Operating Principlementioning

confidence: 99%

Section: Prediction Using Ann and Svr Modeling Techniquesmentioning

confidence: 99%

New Sensing Technologies for Grain Moisture

et al. 2022

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“…The dielectric study reports of many types of loose cereal grains are available in the literature, but those in powdered form are rarely found. 8,9 A novel method in this regard is introduced here which involves the recently emerging metamaterial-based measurement techniques. 10 Metamaterials are articially engineered composites with dimensions much less than the wavelength of the interacting em wave and possessing exotic properties that cannot be found in natural materials.…”

Section: Introductionmentioning

confidence: 99%

A metamaterial based sensor for moisture and density of cereal flours

P. V.,

Sebastian,

Varghese

et al. 2024

Sustainable Food Technol.

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“…With good penetrability, the microwave has been widely studied for measuring the moisture of grain [10,11,12]. Microwave method indirectly calculates the moisture content of grain by measuring the complex permittivity, which is related to grain moisture.…”

Section: Introductionmentioning

confidence: 99%

A Novel, Portable and Fast Moisture Content Measuring Method for Grains Based on an Ultra-Wideband (UWB) Radar Module and the Mode Matching Method

Zhang

Shi

Yang

et al. 2019

Sensors

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To perform fast and portable grain moisture measurements under field conditions, a novel moisture sensor was designed, which consisted of a coaxial waveguide, a circular waveguide, and an isolation layer. The electromagnetic characteristics of the sensor were simulated and measured. The analytical model, which represented the relationship between the reflection coefficient of the sensor and the complex permittivity of grain, was established by using the mode matching method. The reflection coefficient of the sensor was measured by using an ultra-wideband (UWB) radar module, and the moisture content of grains was calculated from the complex permittivity by using density-independent model. To verify the performance of the proposed method, wheat, rough rice, and barley were taken as examples. The measured results in the range from 1.0% to 26.0%, wet basis, agreed well with the reference values (R2 was more than 0.99), and the maximum absolute errors for wheat, rough rice, and barley were 1.1%, 1.0%, and 1.4%, respectively. In addition, the effect of isolation layer was discussed. Both the simulation results and the experimental results showed that the isolation layer improved the stability of sensor.

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A method of determining the moisture content of bulk wheat grain

Cited by 10 publications

References 8 publications

New Sensing Technologies for Grain Moisture

New Sensing Technologies for Grain Moisture

A metamaterial based sensor for moisture and density of cereal flours

A Novel, Portable and Fast Moisture Content Measuring Method for Grains Based on an Ultra-Wideband (UWB) Radar Module and the Mode Matching Method

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