Determination of polyphenols in oats by near-infrared spectroscopy (NIRS) and two-dimensional correlation spectroscopy

Abstract

Two-dimensional correlation spectroscopy (2DCOS) and near-infrared spectroscopy (NIRS) were used to determine the polyphenol content in oat grain. A partial least squares (PLS) algorithm was used to perform the calibration. A total of 116 representative oat samples from four locations in China were prepared and the corresponding near-infrared spectra were measured. Two-dimensional correlation spectroscopy was employed to select wavelength bands for the PLS regression model for the polyphenol determination. The number of PLS components and intervals was optimized according to the coefficients of determination (R²) and root mean square error of cross validation (RMSECV) in the calibration set. The performance of the final model was evaluated using the correlation coefficient (R) and the root mean square error of validation (RMSEV) in the prediction set. The results showed the band corresponding to the optimal calibration model was between 1350 and 1848 nm and the optimal spectral preprocessing combination was second derivative with second smoothing. The optimal regression model was obtained with an R² of 0.8954 and an RMSECV of 0.06651 in the calibration set and R of 0.9614 and RMSEV of 0.04573 in the prediction set. These measurements reveal the calibration model had qualified predictive accuracy. The results demonstrated that the 2DCOS with PLS was a simple and rapid method for the quantitative determination of polyphenols in oats.

Keywords:

• Near-infrared spectroscopy (NIRS)
• partial least squares (PLS)
• polyphenol content
• oat grain
• root mean square error of cross validation (RMSECV)
• root mean square error of validation (RMSEV)
• two-dimensional correlation spectroscopy (2DCOS)

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors acknowledge financial support from the projects of the Fundamental Research Funds for the Central Universities (GK201601002), the Key Research and Development Program of Shaanxi Province (2018TSCXL-NY-01-02), the project from Xi'an Agricultural Science and Technology Innovation Program (2017050NC/NY009(3)), and the project from China Agricultural Research System (CARS-08-D1).