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Ultraviolet-visible diffuse reflectance spectroscopy combined with chemometrics for rapid discrimination Angelicae Sinensis Radix from its four similarities
作者:Xihui Bian*, Zhankui Lu, Geert van Kollenburg
关键字:UV-Vis spectra; ELM, CWT, PCA
论文来源:期刊
具体来源:Analytical Methods, 2020, DOI: 10.1039/D0AY00285B
发表时间:2020年
Ultraviolet-visible diffuse reflectance spectroscopy (UV-Vis DRS) combined with chemometrics was firstly used to differentiate Angelicae Sinensis Radix (ASR) from other four similar herbs (either from the same genus or of similar appearance). A total of 191 samples, including 40 ASR, 39 Angelicae Pubescentis Radix (APR), 38 Chuanxiong Rhizoma (CR), 35 Atractylodis Macrocephalae Rhizoma (AMR) and 39 Angelicae Dahuricae Radix (ADR) were collected and divided into the training and prediction sets. Principal component analysis (PCA) was used for observing the sample cluster tendency of the calibration set. Different preprocessing methods were investigated and the optimal preprocessing combination was selected according to spectral signal characteristics and three-dimensional PCA (3D PCA) clustering results. The final discriminant model was built by extreme learning machine (ELM). The exploratory studies on the raw spectra and their 3D PCA scores indicate that the classification of the five herbs cannot be achieved by PCA on the raw spectra. Autoscaling, continuous wavelet transform (CWT) and Savitzky-Golay (SG) smoothing can improve the clustering results in different degrees. Furthermore, their combination in the order of CWT + autoscaling + SG smoothing can enhance the spectral resolution and obtain the best clustering result. These results are also validated by ELM models on raw and different preprocessing methods. By using CWT + autoscaling + SG smoothing + ELM, 100% classification accuracy can be achieved on both the calibration set and the prediction set. Therefore, the developed method could be used as a rapid, economic and effective method for discriminating the five herbs used in this study.