Brand Identification of Soybean Milk Powder based on Raman Spectroscopy Combined with Random Forest Algorithm
Raman spectroscopy can characterize the rich molecular vibration information of soybean milk powder samples, but difficulties arise in its direct use for sample classification and identification. Therefore, it is urgent to develop an intelligent identification technology based on Raman spectroscopy....
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| Veröffentlicht in: | Journal of analytical chemistry (New York, N.Y.) N.Y.), 2022-10, Vol.77 (10), p.1282-1286 |
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| container_title | Journal of analytical chemistry (New York, N.Y.) |
| container_volume | 77 |
| creator | Zhang, Zheng-Yong Shi, Xiao-Jing Zhao, Ya-Ju Zhang, Yin-Sheng Wang, Hai-Yan |
| description | Raman spectroscopy can characterize the rich molecular vibration information of soybean milk powder samples, but difficulties arise in its direct use for sample classification and identification. Therefore, it is urgent to develop an intelligent identification technology based on Raman spectroscopy. For brand identification of soybean milk powder, this work investigates and discusses a variety of spectral processing technologies including wavelet denoising, normalization, principal component analysis, and the results show that appropriate spectral processing can improve the recognition accuracy of the random forest algorithm. Under the optimal conditions (db2 wavelet, normalization, principal component analysis, 30 decision trees), the best recognition effect of soybean milk brand identification can be achieved. The Raman spectral signal acquisition time of each sample is 40 s, and the spectra pretreatment and identification operation time only takes a few minutes. The analytical approach established in this paper has the advantages of convenient and fast Raman spectra acquisition, fast and accurate model identification. |
| doi_str_mv | 10.1134/S1061934822100173 |
| format | Article |
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Therefore, it is urgent to develop an intelligent identification technology based on Raman spectroscopy. For brand identification of soybean milk powder, this work investigates and discusses a variety of spectral processing technologies including wavelet denoising, normalization, principal component analysis, and the results show that appropriate spectral processing can improve the recognition accuracy of the random forest algorithm. Under the optimal conditions (db2 wavelet, normalization, principal component analysis, 30 decision trees), the best recognition effect of soybean milk brand identification can be achieved. The Raman spectral signal acquisition time of each sample is 40 s, and the spectra pretreatment and identification operation time only takes a few minutes. The analytical approach established in this paper has the advantages of convenient and fast Raman spectra acquisition, fast and accurate model identification.</description><identifier>ISSN: 1061-9348</identifier><identifier>EISSN: 1608-3199</identifier><identifier>DOI: 10.1134/S1061934822100173</identifier><language>eng</language><publisher>Moscow: Pleiades Publishing</publisher><subject>Algorithms ; Analytical Chemistry ; Brand identification ; Chemistry ; Chemistry and Materials Science ; Decision analysis ; Decision trees ; Dried milk ; Identification and classification ; Methods ; Principal components analysis ; Raman spectra ; Raman spectroscopy ; Recognition ; Soya bean milk ; Soybeans ; Soymilk ; Spectrum analysis ; Wavelet analysis</subject><ispartof>Journal of analytical chemistry (New York, N.Y.), 2022-10, Vol.77 (10), p.1282-1286</ispartof><rights>Pleiades Publishing, Ltd. 2022. 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Therefore, it is urgent to develop an intelligent identification technology based on Raman spectroscopy. For brand identification of soybean milk powder, this work investigates and discusses a variety of spectral processing technologies including wavelet denoising, normalization, principal component analysis, and the results show that appropriate spectral processing can improve the recognition accuracy of the random forest algorithm. Under the optimal conditions (db2 wavelet, normalization, principal component analysis, 30 decision trees), the best recognition effect of soybean milk brand identification can be achieved. The Raman spectral signal acquisition time of each sample is 40 s, and the spectra pretreatment and identification operation time only takes a few minutes. The analytical approach established in this paper has the advantages of convenient and fast Raman spectra acquisition, fast and accurate model identification.</description><subject>Algorithms</subject><subject>Analytical Chemistry</subject><subject>Brand identification</subject><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>Decision analysis</subject><subject>Decision trees</subject><subject>Dried milk</subject><subject>Identification and classification</subject><subject>Methods</subject><subject>Principal components analysis</subject><subject>Raman spectra</subject><subject>Raman spectroscopy</subject><subject>Recognition</subject><subject>Soya bean milk</subject><subject>Soybeans</subject><subject>Soymilk</subject><subject>Spectrum analysis</subject><subject>Wavelet analysis</subject><issn>1061-9348</issn><issn>1608-3199</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp1ksFuEzEQhlcIJErhAbhZ4sRhi-3drNfHEFEaqQjUtGfLa48Xl1072I5K3r5TBamKCPLBo_m_f-wZTVW9Z_SCsab9tGG0Y7Jpe84ZpUw0L6oz1tG-bpiULzFGuX7SX1dvcr6nlMqedWfV_DnpYMnaQijeeaOLj4FERzZxP4AO5JuffpEf8cFCIoPOYAnqN3pGabMFU1LMJm73ZBXnwQeUH3z5iUCwcSaXMUEuZDmNMWF6flu9cnrK8O7vfV7dXX65XV3V19-_rlfL69q0i0WpBbbhBgk9tsREKzkGWruFGDS1QLnoJNeDE1IYK7TtetvJAYRxg3aWc9GcVx8Odbcp_t7hF9R93KWATyouEOCiFfSZGvUEygcXS9Jm9tmopeBs0fKGS6TqE9QIAZKeYgDnMX3EX5zg8ViYvTlp-HhkQKbAnzLqXc5qvbk5ZtmBNTj3nMCpbfKzTnvFqHpaBPXPIqCHHzwZ2TBCeh7G_02PaCWyLw</recordid><startdate>20221001</startdate><enddate>20221001</enddate><creator>Zhang, Zheng-Yong</creator><creator>Shi, Xiao-Jing</creator><creator>Zhao, Ya-Ju</creator><creator>Zhang, Yin-Sheng</creator><creator>Wang, Hai-Yan</creator><general>Pleiades Publishing</general><general>Springer</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope></search><sort><creationdate>20221001</creationdate><title>Brand Identification of Soybean Milk Powder based on Raman Spectroscopy Combined with Random Forest Algorithm</title><author>Zhang, Zheng-Yong ; Shi, Xiao-Jing ; Zhao, Ya-Ju ; Zhang, Yin-Sheng ; Wang, Hai-Yan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c455t-7482fb9e861917492861aaf57ba0de027692abf797cd7ad68d69be7cfbafd2273</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Analytical Chemistry</topic><topic>Brand identification</topic><topic>Chemistry</topic><topic>Chemistry and Materials Science</topic><topic>Decision analysis</topic><topic>Decision trees</topic><topic>Dried milk</topic><topic>Identification and classification</topic><topic>Methods</topic><topic>Principal components analysis</topic><topic>Raman spectra</topic><topic>Raman spectroscopy</topic><topic>Recognition</topic><topic>Soya bean milk</topic><topic>Soybeans</topic><topic>Soymilk</topic><topic>Spectrum analysis</topic><topic>Wavelet analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Zheng-Yong</creatorcontrib><creatorcontrib>Shi, Xiao-Jing</creatorcontrib><creatorcontrib>Zhao, Ya-Ju</creatorcontrib><creatorcontrib>Zhang, Yin-Sheng</creatorcontrib><creatorcontrib>Wang, Hai-Yan</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Science</collection><jtitle>Journal of analytical chemistry (New York, N.Y.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Zheng-Yong</au><au>Shi, Xiao-Jing</au><au>Zhao, Ya-Ju</au><au>Zhang, Yin-Sheng</au><au>Wang, Hai-Yan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Brand Identification of Soybean Milk Powder based on Raman Spectroscopy Combined with Random Forest Algorithm</atitle><jtitle>Journal of analytical chemistry (New York, N.Y.)</jtitle><stitle>J Anal Chem</stitle><date>2022-10-01</date><risdate>2022</risdate><volume>77</volume><issue>10</issue><spage>1282</spage><epage>1286</epage><pages>1282-1286</pages><issn>1061-9348</issn><eissn>1608-3199</eissn><abstract>Raman spectroscopy can characterize the rich molecular vibration information of soybean milk powder samples, but difficulties arise in its direct use for sample classification and identification. Therefore, it is urgent to develop an intelligent identification technology based on Raman spectroscopy. For brand identification of soybean milk powder, this work investigates and discusses a variety of spectral processing technologies including wavelet denoising, normalization, principal component analysis, and the results show that appropriate spectral processing can improve the recognition accuracy of the random forest algorithm. Under the optimal conditions (db2 wavelet, normalization, principal component analysis, 30 decision trees), the best recognition effect of soybean milk brand identification can be achieved. The Raman spectral signal acquisition time of each sample is 40 s, and the spectra pretreatment and identification operation time only takes a few minutes. 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| subjects | Algorithms Analytical Chemistry Brand identification Chemistry Chemistry and Materials Science Decision analysis Decision trees Dried milk Identification and classification Methods Principal components analysis Raman spectra Raman spectroscopy Recognition Soya bean milk Soybeans Soymilk Spectrum analysis Wavelet analysis |
| title | Brand Identification of Soybean Milk Powder based on Raman Spectroscopy Combined with Random Forest Algorithm |
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