A robust identification model for herbal medicine using near infrared spectroscopy and artificial neural network
A robust identification model for herbal medicine was developed by combining near-infrared spectroscopy (NIR) and artificial neural network (ANN) to discriminate raw materials of herbal medicine, which are often similar in appearance and practically impossible to identify by visual inspection alone. The identification by chemical methods is usually higher in cost and lower in efficiency. Compared with other modern inspection methods, NIR is an alternative, which is non-destructive, rapid, and easy to operate. In this study, we employed ANN to analyze the absorption spectra of herbal medicines and successfully built an identification model, which is able to identify 30 different herbal medicines. The best identification model can reach a correct identification rate (CIR) of 99.67% when applied to a training set of 600 samples, and 100% CIR when applied to a test set of 300 samples.
Yang, C.I.-W.; Chen, S.; Ouyang, F.; Yang, I.-C.; and Tsai, C.-Y.
"A robust identification model for herbal medicine using near infrared spectroscopy and artificial neural network,"
Journal of Food and Drug Analysis: Vol. 19
, Article 9.
Available at: https://doi.org/10.38212/2224-6614.2193