Data Fusion of ATR-FTIR and UV-Vis Spectra to Identify the Origin of Polygonatum Kingianum

The quality of Polygonati Rhizoma medicinal materials is closely related to the original plants' origin environment. It is necessary to ensure their quality control and drug safety by establishing a simple, rapid and accurate origin identification method for the medicinal materials. In this study, the attenuated total Reflection-Fourier transform infrared (ATR-FTIR) spectra and ultraviolet visible (UV-Vis) spectra of 133 Polygonatum kingianum rhizomes from 9 geographic origins in Yunnan, Sichuan and Guangxi Provinces were collected to establish random forest (RF) modelafter data pretreatment, respectively. ATR-FTIR and UV-Vis spectra data were directly connected in series to complete the RF model of low-level data fusion. Principal components (PCs) and latent variables (LVs) of the two spectra were extracted to achieve RF model ofmid-level (mid-PCs and mid-LVs) and high-level (high-PCs and high-LVs) data fusion. The accuracy (ACC), sensitivity (SEN) and specificity (SPE) of different models were compared to select the best model for origin identification. The results showed that the peaks of ATR-FTIR and UV-Visspectrain P. kingianum were similar, and their absorbance were different. There were 14 common peaks in ATR-FTIR spectra of P. kingianum, which were related to carbohydrate, steroidal saponins, flavonoids and alkaloids. The common peaks of UV-Visspectra in P. kingianum were mainly at 272 and 327 nm, which were related to flavonoids. For the RF models of ATR-FTIR, UV-Vis and low-level fusion, the ACC of the training set and prediction set were respectively (76.34%, 95.00%), (80.65 %, 95.00%) and (83.87%, 100.00%), however, the SEN and SPE values were so low that they were not suitable to use. The SEN and SPE of mid-PCs and mid-LVs RF models were greater than 0.91 and 0.98, respectively. The ACC of the training set was 91.40% and 97.85%, respectively, and that of the prediction set both were 97.50%. The ACC of RF training set with high-PCs and high-LVs was 77.42% and 97.85%, respectively, and the prediction set ACC both were 95.00%. The RF model with high-PCs has poor identification effect, and the RF model with high-LVs was over-fitted. In summary, the identification of model from high to low was: mid-LVs > mid-PCs > low fusion > UV-Vis > ATR-FTIR > high-PCs. LVs extraction method is better than PCs for origin identification. RF model of mid-LVs established has the highest ACC with the best model performance, and the SEN and SPE greater than 0.98, and, which can provide a theoretical basis for the scientific evaluation of medicinal resources of Polygonati Rhizoma.

Automated summary

The quality of Polygonati Rhizoma medicinal materials is closely related to the original plants' origin environment. A total of 133 samples from 9 geographic origins were used to establish a RF model, with mid-level data fusion showing the highest accuracy and performance. This method provides a theoretical basis for the scientific evaluation of medicinal resources of Polygonati Rhizoma.