Ultraviolet-visible diffuse reflectance spectroscopy combined with chemometrics for rapid discrimination of Angelicae Sinensis Radix from its four similar herbs

Ultraviolet-visible diffuse reflectance spectroscopy (UV-Vis DRS) combined with chemometrics was used for the first time to differentiate Angelicae Sinensis Radix (ASR) from four other similar herbs (either from the same genus or of similar appearance). A total of 191 samples, including 40 ASR, 39 A...

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Veröffentlicht in:Analytical methods 2020-07, Vol.12 (27), p.3499-357
Hauptverfasser: Bian, Xihui, Lu, Zhankui, van Kollenburg, Geert
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Lu, Zhankui
van Kollenburg, Geert
description Ultraviolet-visible diffuse reflectance spectroscopy (UV-Vis DRS) combined with chemometrics was used for the first time to differentiate Angelicae Sinensis Radix (ASR) from four other 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 using 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 of the raw spectra. Autoscaling, continuous wavelet transform (CWT) and Savitzky-Golay (SG) smoothing can improve the clustering results to 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 using ELM models of raw and different preprocessing methods. By using CWT + autoscaling + SG smoothing + ELM, 100% classification accuracy can be achieved in 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. Ultraviolet-visible diffuse reflectance spectroscopy (UV-Vis DRS) combined with chemometrics was used for the first time to differentiate Angelicae Sinensis Radix (ASR) from four other similar herbs (either from the same genus or of similar appearance).
doi_str_mv 10.1039/d0ay00285b
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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 using 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 of the raw spectra. Autoscaling, continuous wavelet transform (CWT) and Savitzky-Golay (SG) smoothing can improve the clustering results to different degrees. 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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 using 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 of the raw spectra. Autoscaling, continuous wavelet transform (CWT) and Savitzky-Golay (SG) smoothing can improve the clustering results to different degrees. 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Lu, Zhankui ; van Kollenburg, Geert</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c400t-69937b2a7178cc853f6efd1443a657adf877aabc083551337212c16e329c6d8b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Artificial neural networks</topic><topic>Calibration</topic><topic>Chemometrics</topic><topic>Classification</topic><topic>Cluster Analysis</topic><topic>Clustering</topic><topic>Continuous wavelet transform</topic><topic>Diffuse reflectance spectroscopy</topic><topic>Drugs, Chinese Herbal</topic><topic>Herbs</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Nickel</topic><topic>Preprocessing</topic><topic>Principal Component Analysis</topic><topic>Principal components analysis</topic><topic>Reflectance</topic><topic>Rhizome</topic><topic>Smoothing</topic><topic>Spectra</topic><topic>Spectral resolution</topic><topic>Spectroscopy</topic><topic>Spectrum Analysis</topic><topic>Three dimensional models</topic><topic>Ultraviolet reflection</topic><topic>Ultraviolet spectroscopy</topic><topic>Wavelet transforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bian, Xihui</creatorcontrib><creatorcontrib>Lu, Zhankui</creatorcontrib><creatorcontrib>van Kollenburg, Geert</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Analytical methods</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bian, Xihui</au><au>Lu, Zhankui</au><au>van Kollenburg, Geert</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Ultraviolet-visible diffuse reflectance spectroscopy combined with chemometrics for rapid discrimination of Angelicae Sinensis Radix from its four similar herbs</atitle><jtitle>Analytical methods</jtitle><addtitle>Anal Methods</addtitle><date>2020-07-16</date><risdate>2020</risdate><volume>12</volume><issue>27</issue><spage>3499</spage><epage>357</epage><pages>3499-357</pages><issn>1759-9660</issn><eissn>1759-9679</eissn><abstract>Ultraviolet-visible diffuse reflectance spectroscopy (UV-Vis DRS) combined with chemometrics was used for the first time to differentiate Angelicae Sinensis Radix (ASR) from four other 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 using 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 of the raw spectra. Autoscaling, continuous wavelet transform (CWT) and Savitzky-Golay (SG) smoothing can improve the clustering results to 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 using ELM models of raw and different preprocessing methods. By using CWT + autoscaling + SG smoothing + ELM, 100% classification accuracy can be achieved in 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. Ultraviolet-visible diffuse reflectance spectroscopy (UV-Vis DRS) combined with chemometrics was used for the first time to differentiate Angelicae Sinensis Radix (ASR) from four other similar herbs (either from the same genus or of similar appearance).</abstract><cop>England</cop><pub>Royal Society of Chemistry</pub><pmid>32672249</pmid><doi>10.1039/d0ay00285b</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-4985-5283</orcidid><orcidid>https://orcid.org/0000-0001-5554-7159</orcidid></addata></record>
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source MEDLINE; Royal Society Of Chemistry Journals 2008-
subjects Artificial neural networks
Calibration
Chemometrics
Classification
Cluster Analysis
Clustering
Continuous wavelet transform
Diffuse reflectance spectroscopy
Drugs, Chinese Herbal
Herbs
Learning algorithms
Machine learning
Nickel
Preprocessing
Principal Component Analysis
Principal components analysis
Reflectance
Rhizome
Smoothing
Spectra
Spectral resolution
Spectroscopy
Spectrum Analysis
Three dimensional models
Ultraviolet reflection
Ultraviolet spectroscopy
Wavelet transforms
title Ultraviolet-visible diffuse reflectance spectroscopy combined with chemometrics for rapid discrimination of Angelicae Sinensis Radix from its four similar herbs
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