Citation: LIU Ya-Chao, LI Yong-Yu, PENG Yan-Kun, WANG Fan, YAN Shuai, DING Ji-Gang. Non-destructive Rapid Detection of Rice Amylose Content by Near-Infrared Diffuse Transmission Optical Compensation Method[J]. Chinese Journal of Analytical Chemistry, ;2019, 47(5): 785-793. doi: 10.19756/j.issn.0253-3820.181721 shu

Non-destructive Rapid Detection of Rice Amylose Content by Near-Infrared Diffuse Transmission Optical Compensation Method

College of Engineering, China Agricultural University, National Research and Development Center for Agro-processing Equipment, Beijing 10083, China

Received Date: 19 November 2018
Revised Date: 15 March 2019

Fund Project: This work was supported by the National Science and Technology Support Project of China (No.2016YFD0101205).

To solve the problem of low signal-to-noise ratio in detection of rice amylose content by long wavelength near infrared diffuse transmission spectroscopy, 3 spectral data acquisition systems were built. In the wavelength range of 900-1700 nm, the original spectrum curves of 62 kinds of rice samples before and after light compensation were collected, normalized, smoothed by SG, and treated by Savitzky-Golay derivative convolution. After that, partial least squares regression modeling was performed for amylose content analysis of rice, by which the light diffuse transmission spectrum curves of rice sample with different thicknesses before and after light compensation were comparatively investigated, and the physical and chemical standard values before and after light compensation were subjected to significant analysis. The results showed that the prediction model was improved with the increase of sample thickness before light compensation, but with the increase of sample thickness, the transmitted light intensity was weakened, the signal-to-noise ratio was decreased, the noise was increased, and the model modeling effect became worse. When the sample thickness was 9 mm, the prediction model of near-infrared diffuse transmission spectroscopy for rice amylose had the best result. Under this condition, the correlation coefficients of correction set (R_C) and prediction set (R_P) were 0.9103 and 0.9049, respectively, and their root mean square errors (RMSEC and RMSEP) were 1.4209% and 1.5654%, respectively. After the optical compensation, the signal-to-noise ratio of near-infrared diffuse transmission spectrum curve of the rice sample was significantly improved, especially the spectral absorption near 1203 nm and 1465 nm after the pretreatment, and the prediction model accuracy under different sample thicknesses was also significantly improved. When the thickness of rice samples was 9 mm, the effect of optical compensation prediction model for amylose detection was the best. The correlation coefficient of model correction set (R_C) and prediction set (R_P) were increased to 0.9654 and 0.9577, respectively, and their root mean square errors (RMSEC and RMSEP) were reduced to 0.8902% and 1.4261%, respectively. In addition, the significance of the light compensation was reduced compared with that before the light compensation, and the correlation coefficient and error of the model were improved compared with other methods. Finally, 20 samples were selected for external testing of the optical compensation model with a sample thickness of 9 mm, the model correlation coefficient (RV) was 0.9363, the root mean square error (RMSEV) was 1.4139%, and the RPD value was 2.85. The results showed that the light compensation method could effectively solve the problem of low signal-to-noise ratio caused by relatively weak penetrating power of diffuse transmission spectrum in detection of rice amylose by long wavelength near infrared spectrometry, which improved the accuracy of forecasting model, realized the rapid and nondestructive testing of rice amylose content, and provided technical support for rice quality detection.
- Rice,
- Amylose,
- Near-infrared diffuse transmission,
- Optical compensation
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