Rapid Qualitative and Quantitative Characterization of Arnebiae Radix by Near-Infrared Spectroscopy (NIRS) with Partial Least Squares—Discriminant Analysis (PLS-DA)

Abstract

Near infrared (NIR) spectroscopy with chemometrics was used for rapid source identification and component quantification of Arnebiae Radix. The modeling parameters were determined using the processing trajectory method. The quantitative model of target components was established using the model fusion method. The root mean square error of calibration (RMSEC), root mean square error of validation (RMSEP), determination coefficient of calibration (R_cal²) and validation (R_pre²), and the ratio of standard error of prediction to standard deviation (RPD) were used to evaluate the constructed model. The RMSEC, R_cal², RMSEP, R_pre², and RPD of β-β'-dimethylacryloacanin partial least square (PLS) model established using the step-by-step strategy were 1.3004 mg/g, 0.9453, 1.3833 mg/g, 0.9544, and 4.6650, respectively. The RMSEC, R_cal², RMSEP, R_pre², and RPD of total pigment PLS model were 4.5095 mg/g, 0.9533, 4.5679 mg/g, 0.9534, and 3.0509, respectively. The RMSEC, R_cal², RMSEP, R_pre², and RPD of β-β'-dimethylacryloacanin Bagging-PLS model established using the process trajectory were 1.6126 mg/g, 0.9159, 1.2254 mg/g, 0.9649, and 5.2662, respectively. The RMSEC, R_cal², RMSEP, R_pre², and RPD of the total pigment Bagging-PLS model were 4.3499 mg/g, 0.9565, 4.4662 mg/g, 0.9116, and 3.1205, respectively. The accuracy of quantitative model may be improved using the process trajectory and model fusion method. Partial least squares discriminant analysis (PLS-DA) was used to establish the discriminant models of different sources of Arnebiae Radix. The predicted accuracy rate was 100%. The method is simple, rapid, and demonstrated for the discrimination and quantitative analysis of the main active ingredients of Arnebiae Radix.

Keywords:

• Arnebiae Radix
• β,β'-dimethylacryloacanin
• multivariate calibration
• near-infrared (NIR) spectroscopy
• partial least squares—discriminant analysis (PLS-DA)
• quality control
• shikonin

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that influenced the results reported in this paper.

Additional information

Funding

This work was supported by the Young Innovative Talents Programme of Shihezi University (CXPY201912); the Open Fund Research Projects of Key Laboratory of Xinjiang Phytomedicine Resource and Utilization, Ministry of Education (20180103), the National Natural Science Foundation of China (U1803283, 81960769); and the study on the Key Technique Improvement of Xinjiang Licorice Planting and Quality Control of Xinjiang Production & Construction Corps (2018AB012).