Abstract: In order to rapidly and non-destructively detect gastrodin and 4-hydroxybenzyl alcohol in Rhizoma Gastrodiae, near infrared spectral data of the dried tuber of Gastrodia elata Bl. f. glauca S. Chow were collected in the wavelength range of 900–1 700 nm. First, convolutional smoothing (SG) and standard normal variable transformation (SNV) were used for spectral data preprocessing. Second, feature wavelength extraction was carried out by competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV). According to the results of establishing least squares support vector machine (LSSVR) model based on feature wavelength, the best method of feature wavelength extraction was selected. In order to improve the accuracy of the model, this study introduced the artificial rabbits optimization (ARO) algorithm to optimize the regularization parameter γ and the kernel function density σ2 in LSSVR and the superiority of ARO to particle swarm optimization (PSO) and grey wolf optimizer (GWO) was evaluated. The results showed that the ARO algorithm was superior to PSO and GWO in in terms of optimization speed and ability. The best prediction models for gastrodin and 4-hydroxybenzyl alcohol were CARS-ARO-LSSVR, with prediction correlation coefficient (R2p) of 0.969 6 and 0.957 7, and root mean square error of prediction (RMSEP) of 0.014 and 0.020, respectively. Therefore, this study shows that near-infrared spectroscopy can be used for quantitative detection of active components in Rhizoma Gastrodiae, which provides a theoretical basis for the development of rapid detection devices for Rhizoma Gastrodiae.

Key words: near infrared spectroscopy; Gastrodia; least squares support vector regression; artificial rabbits optimization algorithm