@article{LI2022, 
author = {Nannan LI and Yejia LIU and Lizhong LIN and Zhenzhen CAO and Siming ZHAO and Meng NIU and Caihua JIA and Binjia ZHANG},
title = {Rapid Quantitative Determination of Adulterated Thai Jasmine Rice Using Combined Near-Infrared Spectroscopy and Backward Propagation Neural Network},
year = {2022},
journal = {Food Science},
volume = {43},
number = {4},
pages = {277-283},
keywords = {Thai jasmine rice, near infrared spectroscopy, backward propagation neural network, rapid quantitative determination, prediction model},
url = {https://www.sciopen.com/article/10.7506/spkx1002-6630-20201126-267},
doi = {10.7506/spkx1002-6630-20201126-267},
abstract = {Here, a rapid method for identifying adulterated Thai jasmine rice was developed using near-infrared spectroscopy combined with backward propagation (BP) neural network. Near-infrared spectra of pure and adulterated rice samples were pretreated by multiplicative scatter correction (MSC) and 48 characteristic wavelengths were selected by competitive adaptive reweighted sampling (CARS). Then, the optimal structure model for BP neural network algorithm was established using absorbance values at these wavelengths as the input layer neurons and Thai jasmine rice contents in samples as the output layer neurons, involving a single hidden layer, seven hidden layer neurons, logsig as the transfer function of hidden layer, tansig as the transfer function of output layer, trainlm as the training function, learngdm as the learning function of the network, and learning rate of 0.35. The model showed an excellent prediction performance with a root mean square error (RMSE) of 0.000830, correlation coefficient of the calibration set of 0.9929, correlation coefficient of the verification set of 0.9761, and correlation coefficient of the test set of 0.9755.}
}