Abstract
Angelicae Sinensis Radix is a widely used traditional Chinese medicine and spice in China. The purpose of this study was to develop a methodology for geographical classification of Angelicae Sinensis Radix and determine the contents of ferulic acid and Z-ligustilide in the samples using near-infrared spectroscopy. A qualitative model was established to identify the geographical origin of Angelicae Sinensis Radix using Fourier transform near-infrared (FT-NIR) spectroscopy. Support vector machine (SVM) algorithms were used for the establishment of a qualitative model. The optimum SVM model had a recognition rate of 100% for the calibration set and 83.72% for the prediction set. In addition, a quantitative model was established to predict the content of ferulic acid and Z-ligustilide using FT-NIR. Partial least squares regression (PLSR) algorithms were used for the establishment of a quantitative model. Synergy interval-PLS (Si-PLS) was used to screen the characteristic spectral interval to obtain the best PLSR model. The coefficient of determination for calibration (R2C) for the best PLSR models established with the optimal spectral preprocessing method and selected important spectral regions for the quantitative determination of ferulic acid and Z-ligustilide was 0.9659 and 0.9611, respectively, while the coefficient of determination for prediction (R2P) was 0.9118 and 0.9206, respectively. The values of the ratio of prediction to deviation (RPD) of the two final optimized PLSR models were greater than 2. The results suggested that NIR spectroscopy combined with SVM and PLSR algorithms could be exploited in the discrimination of Angelicae Sinensis Radix from different geographical locations for quality assurance and monitoring. This study might serve as a reference for quality evaluation of agricultural, pharmaceutical, and food products.
Highlights
Angelica sinensis (Oliv.) Diels is a perennial plant that is widely found in China, Korea, and Japan. e dried root of A. sinensis (Oliv.) Diels (Angelicae Sinensis Radix (ASR), Danggui in Chinese) has a long history of use in China
Partial least squares regression (PLSR) is a powerful statistical technique in constructing calibration models with infrared (IR) spectral data that can be used to explore the relationship between independent variables and dependent variables based on the reduction of the dimensionality of the data set [19]. e following parameters were calculated to assess the success of data preprocessing and model performance: coefficient of determination for calibration (R2C) and prediction (R2P), root mean square error of estimation (RMSEE), root mean square error of crossvalidation (RMSECV), root mean square error of prediction (RMSEP), and the ratio of prediction to deviation (RPD)
The RPD values of PLSR models for the determination of ferulic acid and Z-ligustilide were 1.7568 and 1.7392, respectively. e RPD value of the quantitative model constructed after spectral preprocessing was higher than that of the quantitative model based on the original spectrum without preprocessing. These results showed that the spectral pretreatment method selected in this study improved the performance of the PLSR model for the quantitative analysis of ferulic acid and Z-ligustilide
Summary
Angelica sinensis (Oliv.) Diels is a perennial plant that is widely found in China, Korea, and Japan. e dried root of A. sinensis (Oliv.) Diels (Angelicae Sinensis Radix (ASR), Danggui in Chinese) has a long history of use in China. To further explore the application of NIR in the rapid quantitative detection of ASR, the contents of ferulic acid and Z-ligustilide in ASR were estimated using NIR spectroscopy combined with the PLSR calibration model.
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