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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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). |
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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).</description><identifier>ISSN: 1759-9660</identifier><identifier>EISSN: 1759-9679</identifier><identifier>DOI: 10.1039/d0ay00285b</identifier><identifier>PMID: 32672249</identifier><language>eng</language><publisher>England: Royal Society of Chemistry</publisher><subject>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</subject><ispartof>Analytical methods, 2020-07, Vol.12 (27), p.3499-357</ispartof><rights>Copyright Royal Society of Chemistry 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c400t-69937b2a7178cc853f6efd1443a657adf877aabc083551337212c16e329c6d8b3</citedby><cites>FETCH-LOGICAL-c400t-69937b2a7178cc853f6efd1443a657adf877aabc083551337212c16e329c6d8b3</cites><orcidid>0000-0002-4985-5283 ; 0000-0001-5554-7159</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32672249$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bian, Xihui</creatorcontrib><creatorcontrib>Lu, Zhankui</creatorcontrib><creatorcontrib>van Kollenburg, Geert</creatorcontrib><title>Ultraviolet-visible diffuse reflectance spectroscopy combined with chemometrics for rapid discrimination of Angelicae Sinensis Radix from its four similar herbs</title><title>Analytical methods</title><addtitle>Anal Methods</addtitle><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).</description><subject>Artificial neural networks</subject><subject>Calibration</subject><subject>Chemometrics</subject><subject>Classification</subject><subject>Cluster Analysis</subject><subject>Clustering</subject><subject>Continuous wavelet transform</subject><subject>Diffuse reflectance spectroscopy</subject><subject>Drugs, Chinese Herbal</subject><subject>Herbs</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Nickel</subject><subject>Preprocessing</subject><subject>Principal Component Analysis</subject><subject>Principal components analysis</subject><subject>Reflectance</subject><subject>Rhizome</subject><subject>Smoothing</subject><subject>Spectra</subject><subject>Spectral resolution</subject><subject>Spectroscopy</subject><subject>Spectrum Analysis</subject><subject>Three dimensional models</subject><subject>Ultraviolet reflection</subject><subject>Ultraviolet spectroscopy</subject><subject>Wavelet transforms</subject><issn>1759-9660</issn><issn>1759-9679</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kc1rFTEUxYMotlY37pWIGxFG8zGTTJbP-gkFQe3C1ZBJbnwpmcmYZKrvv_FPNe2rT3Dh6l44v3s5nIPQQ0peUMLVS0v0jhDWd-MtdExlpxolpLp92AU5QvdyviBEKC7oXXTEmZCMteoY_ToPJelLHwOU5tJnPwbA1ju3ZsAJXABT9GwA56VuKWYTlx02cRr9DBb_8GWLzRamOEFJ3mTsYsJJL97WL9kkP_lZFx9nHB3ezN8geKMBf67Xc_YZf9LW_8QuxQn7cnW9JpzrUdAJbyGN-T6643TI8OBmnqDzt2--nL5vzj6--3C6OWtMS0hphFJcjkxLKntj-o47Ac7StuVadFJb10up9WhIz7uOci4ZZYYK4EwZYfuRn6Bn-79Lit9XyGWYqn8IQc8Q1zywlrVt21PeV_TpP-hFtT1Xd9cU6aRSpFLP95SpqeUa5bDUNHTaDZQMV70Nr8nm63Vvryr8-OblOk5gD-ifoirwaA-kbA7q3-Kr_uR_-rBYx38Dvq6rcQ</recordid><startdate>20200716</startdate><enddate>20200716</enddate><creator>Bian, Xihui</creator><creator>Lu, Zhankui</creator><creator>van Kollenburg, Geert</creator><general>Royal Society of Chemistry</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SE</scope><scope>7SR</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>FR3</scope><scope>H8G</scope><scope>JG9</scope><scope>L7M</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-4985-5283</orcidid><orcidid>https://orcid.org/0000-0001-5554-7159</orcidid></search><sort><creationdate>20200716</creationdate><title>Ultraviolet-visible diffuse reflectance spectroscopy combined with chemometrics for rapid discrimination of Angelicae Sinensis Radix from its four similar herbs</title><author>Bian, Xihui ; 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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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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