Abstract: Unibract fritillary bulb, a traditional precious Chinese medicinal material, has the effects of clearing away heat, moisturizing the lungs, reducing phlegm， and relieving cough. However, the adulteration of Unibract fritillary bulbs has posed a serious threat to the medicinal effect and the healthy development of the market in recent years. Therefore, it is of great significance to accurately and rapidly detect the adulterated Unibract fritillary bulb powder. In this study, a systematic detection was conducted to distinguish the adulterated fritillariae using terahertz time-domain spectroscopy. Five samples of Fritillaria powder were used as the research objects, containing different adulterants (rice flour, Kudzuvine root powder, sweet potato powder, wheat flour, and Fritillaria Ussuriensis Maxim powder), pure Unibract fritillary bulb powder as the control group. Chemometric methods were also selected to detect the quality of Unibract fritillary bulb. The specific procedure was as follows. Firstly, adulterated samples were prepared with different types of Unibract fritillary bulbs in the same content. Then, the terahertz time-domain spectra were collected. Partial Least Squares Discriminant Analysis (PLS-DA) was also used in the range of 0.5-3.0 THz, according to the original and five adulterated Fritillaria powders. The original spectrum was used to remove the irrelevant variables and noise using the Savitzky-Golay smoothing (S-G Smoothing), Normalize, and Multiple Scatter Correction (MSC). A two-class model was established using the obtained spectral data. Thirdly, Principal component analysis (PCA) was used to reduce the dimensionality of preprocessed data, while simplifying the calculation of the model. Kennard-Stone (KS) was selected to divide the sample data into a 1:3 ratio, while the spectral data into prediction and modeling set. Finally, a Support Vector Machine (SVM) multi-classification model was established using Grid Search and Particle Swarm Optimization (PSO), where two parameters were optimized, namely, the penalty parameters (c) and the number of cores (g) of SVM. Correspondingly, the recognition accuracy rates of various samples were compared under the optimal spectral preprocessing and parameter optimization. The results showed that six binary classification models for the original spectra presented a correct identification rate of 100%, indicating a high accuracy for the pure Unibract fritillary bulb and adulterated Fritillaria. There were also great differences in the time domain spectra in the terahertz of samples. A multi-classification model was then established using Normalize combined with MSC preprocessing, further optimizing parameters using Particle Swarm Optimization (PSO). The overall accuracy of PSO optimization was higher than that of grid search optimization, where the highest accuracy rate was 100%. The lowest accuracy rate was 90%, and the average prediction accuracy was 95.67%, while the root mean square error was 0.432 when Unibract fritillary bulb powder was mixed with Fritillaria Ussuriensis Maxim powder. Consequently, Terahertz spectroscopy combined with a support vector machine can simultaneously detect a variety of Unibract fritillary bulb powder containing different adulterants. This finding can provide a theoretical experience for the detection of Unibract fritillary bulb adulteration in the field of medicine, thereby ensuring the excellent quality of Chinese medicinal materials in the trading market.