Developing an electronic portable device based on dielectric power spectroscopy for non-destructive prediction of date moisture content

2016 ◽  
Vol 247 ◽  
pp. 289-297 ◽  
Author(s):  
Seyed Ahmad Mireei ◽  
Rahmatollah Bagheri ◽  
Morteza Sadeghi ◽  
Ali Shahraki
Keyword(s):  
Moisture Content ◽  
Portable Device ◽  
Non Destructive

scholarly journals Moisture Measurement in Paper and Cardboard Packaging Waste Bales for Recycling

2021 ◽  
Vol 11 (10) ◽  
pp. 4586
Author(s):  
Ana Silveira ◽  
João Cardoso ◽  
Maria José Correia ◽  
Graça Martinho
Keyword(s):  
Quality Control ◽  
Dielectric Properties ◽  
Moisture Content ◽  
Reliable Method ◽  
Drying Method ◽  
Waste Streams ◽  
Oven Drying ◽  
Quality Issue ◽  
Commercial Device ◽  
Non Destructive

Moisture content is a quality issue raised by recycling plants in the acceptance of paper and cardboard coming from waste streams. The current way to measure this parameter is by the oven drying method, which is a slow and invasive process, costing time and resources for the recyclers to do this type of quality control. An alternative to such a measurement technique is the use of plate-form devices which indirectly measure the moisture content using the dielectric properties of water and paper. This study has tested this method and developed a representative equation for the use of devices with these properties in the Portuguese market. For that, 48 wastepaper and cardboard bales were tested with both the traditional (oven drying) method and a commercial device equipped with dielectric technology. An equation that fits the studied reality (R2 = 0.76) was achieved, and possible problems regarding the use of this device were tested. The results showed that this type of device could be used as a time- and cost-saving, non-destructive and reliable method in the quality control of wastepaper and cardboard bales.

Identification prediction moisture content of Thai coconut sugar (Cocos nucifera L.) using FT-NIR spectroscopy

Food Research ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 382-385
Author(s):  
D. Thumrongchote
Keyword(s):  
Moisture Content ◽  
Cocos Nucifera ◽  
Nir Spectroscopy ◽  
Grocery Stores ◽  
Absorbance Spectrum ◽  
Coconut Tree ◽  
Cocos Nucifera L ◽  
The Cost ◽  
Non Destructive ◽  
Low Glycemic Index

Coconut sugar is a local sugar from the blossoms of a coconut tree. It has been considered a healthy sugar due to its low glycemic index. There is an attempt to add other sugar to it to lower the cost. Thus, this research aimed to identify Thai coconut sugar and to establish models for predicting the moisture content of coconut sugar by using FT-NIR spectroscopy. Thai coconut sugar samples were purchased from local grocery stores in four provinces, online, and the community market. Their moisture contents were varied and equilibrated for 24 hrs prior to the measurements of moisture and FT-NIR spectra. The results showed that FT-NIR spectra of Thai coconut sugar differ from sucrose, glucose and fructose at the absorbance spectrum of 5379-5011 cm-1 . FT-NIR spectroscopy of 54 known moisture samples of Thai coconut sugar was used to obtain a model to predict moisture content. The predicted equation, using the PLS technique with the Spectrum Quant program, was found to give a standard error of prediction (SEP) 0.077% (less than 0.10%), indicating a non-destructive method of accurately and precisely predicting moisture levels in the coconut sugar. The results obtained suggested that FTNIR spectroscopy has the potential to be used as a tool to identify Thai coconut sugar accurately. It can rapidly predict the moisture content in the sample which will be useful in quality control standards.

scholarly journals Non-destructive evaluation of moisture content in wood by using Ground Penetrating Radar

2016 ◽  
Author(s):  
Hamza Reci ◽  
Tien Chinh Maï ◽  
Zoubir Mehdi Sbartaï ◽  
Lara Pajewski ◽  
Emanuela Kiri
Keyword(s):  
Moisture Content ◽  
Ground Penetrating Radar ◽  
Wood Fiber ◽  
Fiber Direction ◽  
Direct Wave ◽  
Reflected Waves ◽  
Moisture Variation ◽  
Reflection Configuration ◽  
Ground Penetrating ◽  
Non Destructive

Abstract. This paper presents the results of a series of laboratory measurements carried out to study how the Ground Penetrating Radar (GPR) signal is affected by moisture variation in wood material. The effects of the wood fiber direction, with respect to the polarisation of the electromagnetic field, are investigated. The relative permittivity of wood and the amplitude of the electric field received by the radar are measured for different humidity levels, by using the direct-wave method in Wide Angle Radar Reflection configuration, where one GPR antenna is moved while the other is kept in a fixed position. The received signal is recorded for different separations between transmitting and receiving antennas. Direct waves are compared to reflected waves: it is observed that they show a different behaviour when the moisture content varies, due to their different propagation paths.

scholarly journals CARS Algorithm-Based Detection of Wheat Moisture Content before Harvest

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 115
Author(s):  
Hong Ji ◽  
Wanzhang Wang ◽  
Dongfeng Chong ◽  
Boyang Zhang
Keyword(s):  
Moisture Content ◽  
Regression Model ◽  
Regression Models ◽  
Partial Least Squares Regression ◽  
Early Stage ◽  
Least Squares Regression ◽  
Calibration Sample ◽  
Non Destructive ◽  
Plsr Model ◽  
Variable Subset

To rapidly detect the wheat moisture content (WMC) without harm to the wheat and before harvest, this paper measured wheat and panicle moisture content (PMC) and the corresponding spectral reflectance of panicle before harvest at the Beijing Tongzhou experimental station of China Agricultural University. Firstly, we used correlation analysis to determine the optimal regression model of WMC and PMC. Secondly, we derived the spectral sensitive band of PMC before filtering the redundant variables competitive adaptive reweighted sampling (CARS) to select the variable subset with the least error. Finally, partial least squares regression (PLSR) was used to build and analyze the prediction model of PMC. At the early stage of wheat harvest, a high correlation existed between WMC and PMC. Among all regression models such as exponential, univariate linear, polynomial models, and the power function regression model, the logarithm regression model was the best. The determination coefficients of the modeling sample were: R2 = 0.9284, the significance F = 362.957, the determination coefficient of calibration sample R2v = 0.987, the root mean square error RMSEv = 3.859, and the relative error REv = 7.532. Within the range of 350–2500 nm, bands of 728–907 nm, 1407–1809 nm, and 1940–2459 nm had a correlation coefficient of PMC and wavelength reflectivity higher than 0.6. This paper used the CARS algorithm to optimize the variables and obtained the best variable subset, which included 30 wavelength variables. The PLSR model was established based on 30 variables optimized by the CARS algorithm. Compared with the all-sensitive band, which had 1103 variables, the PLSR model not only reduced the number of variables by 1073, but also had a higher accuracy in terms of prediction. The results showed that: RMSEC = 0.9301, R2c = 0.995, RMSEP = 2.676, R2p = 0.945, and RPD = 3.362, indicating that the CARS algorithm could effectively remove the variables of spectral redundant information. The CARS algorithm provided a new way of thinking for the non-destructive and rapid detection of WMC before harvest.

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