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.
Highlights
Wheat is one of the most important agricultural products in the world
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
The total time required for preparation, measurement, and analysis of wheat samples was within 5 min, which is equal to that in [6,7] using data fusion with multiple analytical measurements to increase the model performance and decrease the modeling error for the prediction of quality parameters of crude oil, and the average RMSEP for the fully fused model was calculated to be 0.307%
Summary
The aim of the present study is to investigate the potential of THz spectroscopy as a non-destructive method to discriminate wheat varieties
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