Study of sample temperature compensation in the measurement of soil moisture content

Liang, Xiu; Li, Xiao Yu; Lei, Ting; Wang, Wei; Gao, Yinjie

doi:10.1016/j.measurement.2011.07.008

Cited by 14 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multi-source information fusion is useful to analyze, control, and comprehensively process the multiple information resources for different times and space parameters, as well as to obtain new and more useful information to recognize and classify the measured object [ 26 , 27 , 28 ]. Multi-source information fusion can be divided into the following: data layer fusion, decision layer fusion, and feature layer fusion [ 29 , 30 ].…”

Section: Experimental Methodsmentioning

confidence: 99%

THz Spectroscopic Investigation of Wheat-Quality by Using Multi-Source Data Fusion

Jiang

Zhang

2018

Sensors

View full text Add to dashboard Cite

In order to improve the detection accuracy for the quality of wheat, a recognition method for wheat quality using the terahertz (THz) spectrum and multi-source information fusion technology is proposed. Through a combination of the absorption and the refractive index spectra of samples of normal, germinated, moldy, and worm-eaten wheat, support vector machine (SVM) and Dempster-Shafer (DS) evidence theory with different kernel functions were used to establish a classification fusion model for the multiple optical indexes of wheat. The results showed that the recognition rate of the fusion model for wheat samples can be as high as 96%. Furthermore, this approach was compared to the regression model based on single-spectrum analysis. The results indicate that the average recognition rates of fusion models for wheat can reach 90%, and the recognition rate of the SVM radial basis function (SVM-RBF) fusion model can reach 97.5%. The preliminary results indicated that THz-TDS combined with DS evidence theory analysis was suitable for the determination of the wheat quality with better detection accuracy.

show abstract

Section: Experimental Methodsmentioning

confidence: 99%

THz Spectroscopic Investigation of Wheat-Quality by Using Multi-Source Data Fusion

Jiang

Zhang

2018

Sensors

View full text Add to dashboard Cite

show abstract

“…The temperature compensation calibration models showed a high accuracy in prediction (R 2 ¼ 0.92, SEC ¼ 0.41 Brix, SEP ¼ 0.42 Brix), even if the sample temperature changed in the above range. Liang et al 16 developed a temperature compensation model based on back propagation neural network (BPNN) for the determination of moisture content in soil. They found that the temperature compensation model was less influenced by the temperature of the sample and had an RMSEP of 0.27%.…”

Section: Introductionmentioning

confidence: 99%

“…Liang et al. 16 developed a temperature compensation model based on back propagation neural network (BPNN) for the determination of moisture content in soil. They found that the temperature compensation model was less influenced by the temperature of the sample and had an RMSEP of 0.27%.…”

Section: Introductionmentioning

confidence: 99%

Temperature compensation for determination of moisture and reducing sugar of longan honey by near infrared spectroscopy

Thamasopinkul

Ritthiruangdej

Kasemsumran

et al. 2017

Journal of Near Infrared Spectroscopy

View full text Add to dashboard Cite

Near infrared spectra of honeys are affected by sample temperature variation, mainly due to a change in hydrogen bonding of water. The aim of this study was to develop robust and powerful calibration models which can compensate for a variation of sample temperature for the determination of moisture and reducing sugar content in honey using near infrared spectroscopy. Partial least squares regression with the aid of standard normal variate transformation was used to develop three calibration models at constant temperature (25, 35 and 45 C) and a robust calibration model with temperature compensation. All the developed models for moisture and reducing sugar content showed high performance of prediction with coefficient of determination (r 2) and residual prediction deviation values greater than 0.95 and 3.8, respectively. The results show that the temperature compensation model can be considered as a robust calibration model for near infrared determination of moisture and reducing sugar in the honey when sample temperature is varied.

show abstract

“…In these studies, the analysis of the spectral response of each sample is performed over a wide range of the electromagnetic spectrum in order to determine characteristics of interest [20,19,17,14]. However, some researchers have developed new instruments, proposing cheaper alternatives, different acquisition methods or equipments for special conditions applications [21,15,1,23,11,4,13,10].…”

Section: Introductionmentioning

confidence: 99%

Development and testing of a hardware platform for measuring instruments based on near-infrared diffuse reflection

et al. 2016

View full text Add to dashboard Cite

Study of sample temperature compensation in the measurement of soil moisture content

Cited by 14 publications

References 10 publications

THz Spectroscopic Investigation of Wheat-Quality by Using Multi-Source Data Fusion

THz Spectroscopic Investigation of Wheat-Quality by Using Multi-Source Data Fusion

Temperature compensation for determination of moisture and reducing sugar of longan honey by near infrared spectroscopy

Development and testing of a hardware platform for measuring instruments based on near-infrared diffuse reflection

Contact Info

Product

Resources

About