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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.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).

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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(a) Time-domain THz spectra of the eight wheat samples and reference; and (b) the frequency spectra of the eight wheat samples and the reference in the range of 0.2–2.5 THz.
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sensors-15-12560-f001: (a) Time-domain THz spectra of the eight wheat samples and reference; and (b) the frequency spectra of the eight wheat samples and the reference in the range of 0.2–2.5 THz.

Mentions: To remove the random error and increase the signal-to-noise ratio (SNR), each sample is measured three times; the sample spectrum was the average of three scanning spectra in the range of 0.2–2.5 THz, and the reference was measured between every three samples. Figure 1a,b show the time-domain spectra of the eight wheat samples and the corresponding frequency-domain spectra obtained using a fast Fourier transform algorithm. Furthermore, the refractive indices and the absorption coefficients of the eight wheat samples are calculated using Equations (3) and (4), respectively, and the calculated results are shown in Figure 2a,b.


Characterization of wheat varieties using terahertz time-domain spectroscopy.

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

(a) Time-domain THz spectra of the eight wheat samples and reference; and (b) the frequency spectra of the eight wheat samples and the reference in the range of 0.2–2.5 THz.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-12560-f001: (a) Time-domain THz spectra of the eight wheat samples and reference; and (b) the frequency spectra of the eight wheat samples and the reference in the range of 0.2–2.5 THz.
Mentions: To remove the random error and increase the signal-to-noise ratio (SNR), each sample is measured three times; the sample spectrum was the average of three scanning spectra in the range of 0.2–2.5 THz, and the reference was measured between every three samples. Figure 1a,b show the time-domain spectra of the eight wheat samples and the corresponding frequency-domain spectra obtained using a fast Fourier transform algorithm. Furthermore, the refractive indices and the absorption coefficients of the eight wheat samples are calculated using Equations (3) and (4), respectively, and the calculated results are shown in Figure 2a,b.

Bottom Line: 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).

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