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Characterization of wheat varieties using terahertz time-domain spectroscopy.

Ge H, Jiang Y, Lian F, Zhang Y, Xia S - Sensors (Basel) (2015)

Bottom Line: Using partial least squares (PLS), a regression model for discriminating wheat varieties was developed.In addition, interval PLS was applied to optimize the models by selecting the most appropriate regions in the spectra, improving the prediction accuracy (R = 0.992 and RMSECV = 0.967).Results demonstrate that THz spectroscopy combined with multivariate analysis can provide rapid, nondestructive discrimination of wheat varieties.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Transducer Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100080, China. gehongyi2004@163.com.

ABSTRACT
Terahertz (THz) spectroscopy and multivariate data analysis were explored to discriminate eight wheat varieties. The absorption spectra were measured using THz time-domain spectroscopy from 0.2 to 2.0 THz. Using partial least squares (PLS), a regression model for discriminating wheat varieties was developed. The coefficient of correlation in cross validation (R) and root-mean-square error of cross validation (RMSECV) were 0.985 and 1.162, respectively. In addition, interval PLS was applied to optimize the models by selecting the most appropriate regions in the spectra, improving the prediction accuracy (R = 0.992 and RMSECV = 0.967). Results demonstrate that THz spectroscopy combined with multivariate analysis can provide rapid, nondestructive discrimination of wheat varieties.

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Related in: MedlinePlus

The calibration and validation results for wheat discrimination using the PLS model.
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sensors-15-12560-f004: The calibration and validation results for wheat discrimination using the PLS model.

Mentions: Figure 4 shows the calibration and validation results for wheat varietal discrimination using the absorption spectrum PLS regression model. In the model, the reference line indicates the zero residuals between the predicted and the actual values. Figure 4 shows that the predicted values for all varieties of samples agree with the actual values, indicating that the PLS model can identify wheat varieties.


Characterization of wheat varieties using terahertz time-domain spectroscopy.

Ge H, Jiang Y, Lian F, Zhang Y, Xia S - Sensors (Basel) (2015)

The calibration and validation results for wheat discrimination using the PLS model.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507591&req=5

sensors-15-12560-f004: The calibration and validation results for wheat discrimination using the PLS model.
Mentions: Figure 4 shows the calibration and validation results for wheat varietal discrimination using the absorption spectrum PLS regression model. In the model, the reference line indicates the zero residuals between the predicted and the actual values. Figure 4 shows that the predicted values for all varieties of samples agree with the actual values, indicating that the PLS model can identify wheat varieties.

Bottom Line: Using partial least squares (PLS), a regression model for discriminating wheat varieties was developed.In addition, interval PLS was applied to optimize the models by selecting the most appropriate regions in the spectra, improving the prediction accuracy (R = 0.992 and RMSECV = 0.967).Results demonstrate that THz spectroscopy combined with multivariate analysis can provide rapid, nondestructive discrimination of wheat varieties.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Transducer Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100080, China. gehongyi2004@163.com.

ABSTRACT
Terahertz (THz) spectroscopy and multivariate data analysis were explored to discriminate eight wheat varieties. The absorption spectra were measured using THz time-domain spectroscopy from 0.2 to 2.0 THz. Using partial least squares (PLS), a regression model for discriminating wheat varieties was developed. The coefficient of correlation in cross validation (R) and root-mean-square error of cross validation (RMSECV) were 0.985 and 1.162, respectively. In addition, interval PLS was applied to optimize the models by selecting the most appropriate regions in the spectra, improving the prediction accuracy (R = 0.992 and RMSECV = 0.967). Results demonstrate that THz spectroscopy combined with multivariate analysis can provide rapid, nondestructive discrimination of wheat varieties.

Show MeSH
Related in: MedlinePlus