Screening analysis of beer ageing using near infrared spectroscopy and the Successive Projections Algorithm for variable selection
► Near infrared spectroscopy (NIR) is used for screening analysis of aged beers. ► Variable selection is accomplished by using the Successive Projections Algorithm (SPA). ► Only one wavenumber is selected for screening analysis of non-alcoholic or alcoholic beers. ► NIR and SPA-LDA can be used for q...
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description | ► Near infrared spectroscopy (NIR) is used for screening analysis of aged beers. ► Variable selection is accomplished by using the Successive Projections Algorithm (SPA). ► Only one wavenumber is selected for screening analysis of non-alcoholic or alcoholic beers. ► NIR and SPA-LDA can be used for quality control of beer samples.
This work proposes a method for monitoring the ageing of beer using near-infrared (NIR) spectroscopy and chemometrics classification tools. For this purpose, the Successive Projections Algorithm (SPA) is used to select spectral variables for construction of Linear Discriminant Analysis (LDA) classification models. A total of 83 alcoholic and non-alcoholic beer samples packaged in bottles and cans were examined. To simulate a long storage period, some of the samples were stored in an oven at 40°C, in the dark, during intervals of 10 and 20 days. The NIR spectrum of these samples in the range 12,500–5405cm−1 was then compared against those of the fresh samples. The results of a Principal Component Analysis (PCA) indicated that the alcoholic beer samples could be clearly discriminated with respect to ageing stage (fresh, 10-day or 20-day forced ageing). However, such discrimination was not apparent for the non-alcoholic samples. These findings were corroborated by a classification study using Soft Independent Modelling of Class Analogy (SIMCA). In contrast, the use of SPA-LDA provided good results for both types of beer (only one misclassified sample) by using a single wavenumber in each case, namely 5550cm−1 for non-alcoholic samples and 7228cm−1 for alcoholic samples. |
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This work proposes a method for monitoring the ageing of beer using near-infrared (NIR) spectroscopy and chemometrics classification tools. For this purpose, the Successive Projections Algorithm (SPA) is used to select spectral variables for construction of Linear Discriminant Analysis (LDA) classification models. A total of 83 alcoholic and non-alcoholic beer samples packaged in bottles and cans were examined. To simulate a long storage period, some of the samples were stored in an oven at 40°C, in the dark, during intervals of 10 and 20 days. The NIR spectrum of these samples in the range 12,500–5405cm−1 was then compared against those of the fresh samples. The results of a Principal Component Analysis (PCA) indicated that the alcoholic beer samples could be clearly discriminated with respect to ageing stage (fresh, 10-day or 20-day forced ageing). However, such discrimination was not apparent for the non-alcoholic samples. These findings were corroborated by a classification study using Soft Independent Modelling of Class Analogy (SIMCA). In contrast, the use of SPA-LDA provided good results for both types of beer (only one misclassified sample) by using a single wavenumber in each case, namely 5550cm−1 for non-alcoholic samples and 7228cm−1 for alcoholic samples.</description><identifier>ISSN: 0039-9140</identifier><identifier>EISSN: 1873-3573</identifier><identifier>DOI: 10.1016/j.talanta.2011.12.030</identifier><identifier>PMID: 22284494</identifier><identifier>CODEN: TLNTA2</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Ageing ; Aging ; Algorithms ; Analytical chemistry ; Beer ; Beer - analysis ; Chemistry ; Chemometrics ; Classification ; Discriminant Analysis ; Exact sciences and technology ; Food Storage ; Linear Discriminant Analysis ; Linear Models ; Mathematical models ; Near infrared spectroscopy ; Principal Component Analysis ; Projection ; Software ; Spectrometric and optical methods ; Spectroscopy, Near-Infrared ; Successive Projections Algorithm ; Wavelength selection</subject><ispartof>Talanta (Oxford), 2012-01, Vol.89, p.286-291</ispartof><rights>2011 Elsevier B.V.</rights><rights>2015 INIST-CNRS</rights><rights>Copyright © 2011 Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-63a961358429ca7df247f276ec3e28afe3dce41e8cbc7a05b75feb53408093bc3</citedby><cites>FETCH-LOGICAL-c474t-63a961358429ca7df247f276ec3e28afe3dce41e8cbc7a05b75feb53408093bc3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.talanta.2011.12.030$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3548,27923,27924,45994</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=25618251$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22284494$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ghasemi-Varnamkhasti, Mahdi</creatorcontrib><creatorcontrib>Mohtasebi, Seyed Saied</creatorcontrib><creatorcontrib>Rodriguez-Mendez, Maria Luz</creatorcontrib><creatorcontrib>Gomes, Adriano A.</creatorcontrib><creatorcontrib>Araújo, Mario Cesar Ugulino</creatorcontrib><creatorcontrib>Galvão, Roberto K.H.</creatorcontrib><title>Screening analysis of beer ageing using near infrared spectroscopy and the Successive Projections Algorithm for variable selection</title><title>Talanta (Oxford)</title><addtitle>Talanta</addtitle><description>► Near infrared spectroscopy (NIR) is used for screening analysis of aged beers. ► Variable selection is accomplished by using the Successive Projections Algorithm (SPA). ► Only one wavenumber is selected for screening analysis of non-alcoholic or alcoholic beers. ► NIR and SPA-LDA can be used for quality control of beer samples.
This work proposes a method for monitoring the ageing of beer using near-infrared (NIR) spectroscopy and chemometrics classification tools. For this purpose, the Successive Projections Algorithm (SPA) is used to select spectral variables for construction of Linear Discriminant Analysis (LDA) classification models. A total of 83 alcoholic and non-alcoholic beer samples packaged in bottles and cans were examined. To simulate a long storage period, some of the samples were stored in an oven at 40°C, in the dark, during intervals of 10 and 20 days. The NIR spectrum of these samples in the range 12,500–5405cm−1 was then compared against those of the fresh samples. The results of a Principal Component Analysis (PCA) indicated that the alcoholic beer samples could be clearly discriminated with respect to ageing stage (fresh, 10-day or 20-day forced ageing). However, such discrimination was not apparent for the non-alcoholic samples. These findings were corroborated by a classification study using Soft Independent Modelling of Class Analogy (SIMCA). In contrast, the use of SPA-LDA provided good results for both types of beer (only one misclassified sample) by using a single wavenumber in each case, namely 5550cm−1 for non-alcoholic samples and 7228cm−1 for alcoholic samples.</description><subject>Ageing</subject><subject>Aging</subject><subject>Algorithms</subject><subject>Analytical chemistry</subject><subject>Beer</subject><subject>Beer - analysis</subject><subject>Chemistry</subject><subject>Chemometrics</subject><subject>Classification</subject><subject>Discriminant Analysis</subject><subject>Exact sciences and technology</subject><subject>Food Storage</subject><subject>Linear Discriminant Analysis</subject><subject>Linear Models</subject><subject>Mathematical models</subject><subject>Near infrared spectroscopy</subject><subject>Principal Component Analysis</subject><subject>Projection</subject><subject>Software</subject><subject>Spectrometric and optical methods</subject><subject>Spectroscopy, Near-Infrared</subject><subject>Successive Projections Algorithm</subject><subject>Wavelength selection</subject><issn>0039-9140</issn><issn>1873-3573</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqF0U9rFDEYBvAgit1WP4KSi7SXGfN3MnOSUrQVCgrVc8hk3tlmmZ2seWcW9uonN8Ou9VYvCYTfm4TnIeQdZyVnvPq4KSc3uHFypWCcl1yUTLIXZMVrIwupjXxJVozJpmi4YmfkHHHDGBOSydfkTAhRK9WoFfn94BPAGMY1daMbDhiQxp62AIm6NSznMy7rCC7RMPbJJego7sBPKaKPu0Me7Oj0CPRh9h4Qwx7o9xQ3WYQ4Ir0e1jGF6XFL-5jo3qXg2gEownAUb8ir3g0Ib0_7Bfn55fOPm7vi_tvt15vr-8Iro6aikq6puNS1Eo13puuFMr0wFXgJonY9yM6D4lD71hvHdGt0D62WitWska2XF-TyeO8uxV8z4GS3AT0MOUWIM9qG143Uwpgsr56VnOX8Gq2rheoj9TkNTNDbXQpblw4Z2aUou7GnouxSlOXC5qLy3PvTE3O7he5p6m8zGXw4AYfeDTn30Qf853TFa6F5dp-ODnJ0-wDJog8weuhCyvnaLob_fOUPne22bw</recordid><startdate>20120130</startdate><enddate>20120130</enddate><creator>Ghasemi-Varnamkhasti, Mahdi</creator><creator>Mohtasebi, Seyed Saied</creator><creator>Rodriguez-Mendez, Maria Luz</creator><creator>Gomes, Adriano A.</creator><creator>Araújo, Mario Cesar Ugulino</creator><creator>Galvão, Roberto K.H.</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>IQODW</scope><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>7QQ</scope><scope>7SR</scope><scope>8FD</scope><scope>JG9</scope><scope>7X8</scope></search><sort><creationdate>20120130</creationdate><title>Screening analysis of beer ageing using near infrared spectroscopy and the Successive Projections Algorithm for variable selection</title><author>Ghasemi-Varnamkhasti, Mahdi ; Mohtasebi, Seyed Saied ; Rodriguez-Mendez, Maria Luz ; Gomes, Adriano A. ; Araújo, Mario Cesar Ugulino ; Galvão, Roberto K.H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-63a961358429ca7df247f276ec3e28afe3dce41e8cbc7a05b75feb53408093bc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Ageing</topic><topic>Aging</topic><topic>Algorithms</topic><topic>Analytical chemistry</topic><topic>Beer</topic><topic>Beer - analysis</topic><topic>Chemistry</topic><topic>Chemometrics</topic><topic>Classification</topic><topic>Discriminant Analysis</topic><topic>Exact sciences and technology</topic><topic>Food Storage</topic><topic>Linear Discriminant Analysis</topic><topic>Linear Models</topic><topic>Mathematical models</topic><topic>Near infrared spectroscopy</topic><topic>Principal Component Analysis</topic><topic>Projection</topic><topic>Software</topic><topic>Spectrometric and optical methods</topic><topic>Spectroscopy, Near-Infrared</topic><topic>Successive Projections Algorithm</topic><topic>Wavelength selection</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ghasemi-Varnamkhasti, Mahdi</creatorcontrib><creatorcontrib>Mohtasebi, Seyed Saied</creatorcontrib><creatorcontrib>Rodriguez-Mendez, Maria Luz</creatorcontrib><creatorcontrib>Gomes, Adriano A.</creatorcontrib><creatorcontrib>Araújo, Mario Cesar Ugulino</creatorcontrib><creatorcontrib>Galvão, Roberto K.H.</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Ceramic Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>MEDLINE - Academic</collection><jtitle>Talanta (Oxford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ghasemi-Varnamkhasti, Mahdi</au><au>Mohtasebi, Seyed Saied</au><au>Rodriguez-Mendez, Maria Luz</au><au>Gomes, Adriano A.</au><au>Araújo, Mario Cesar Ugulino</au><au>Galvão, Roberto K.H.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Screening analysis of beer ageing using near infrared spectroscopy and the Successive Projections Algorithm for variable selection</atitle><jtitle>Talanta (Oxford)</jtitle><addtitle>Talanta</addtitle><date>2012-01-30</date><risdate>2012</risdate><volume>89</volume><spage>286</spage><epage>291</epage><pages>286-291</pages><issn>0039-9140</issn><eissn>1873-3573</eissn><coden>TLNTA2</coden><abstract>► Near infrared spectroscopy (NIR) is used for screening analysis of aged beers. ► Variable selection is accomplished by using the Successive Projections Algorithm (SPA). ► Only one wavenumber is selected for screening analysis of non-alcoholic or alcoholic beers. ► NIR and SPA-LDA can be used for quality control of beer samples.
This work proposes a method for monitoring the ageing of beer using near-infrared (NIR) spectroscopy and chemometrics classification tools. For this purpose, the Successive Projections Algorithm (SPA) is used to select spectral variables for construction of Linear Discriminant Analysis (LDA) classification models. A total of 83 alcoholic and non-alcoholic beer samples packaged in bottles and cans were examined. To simulate a long storage period, some of the samples were stored in an oven at 40°C, in the dark, during intervals of 10 and 20 days. The NIR spectrum of these samples in the range 12,500–5405cm−1 was then compared against those of the fresh samples. The results of a Principal Component Analysis (PCA) indicated that the alcoholic beer samples could be clearly discriminated with respect to ageing stage (fresh, 10-day or 20-day forced ageing). However, such discrimination was not apparent for the non-alcoholic samples. These findings were corroborated by a classification study using Soft Independent Modelling of Class Analogy (SIMCA). In contrast, the use of SPA-LDA provided good results for both types of beer (only one misclassified sample) by using a single wavenumber in each case, namely 5550cm−1 for non-alcoholic samples and 7228cm−1 for alcoholic samples.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><pmid>22284494</pmid><doi>10.1016/j.talanta.2011.12.030</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Ageing Aging Algorithms Analytical chemistry Beer Beer - analysis Chemistry Chemometrics Classification Discriminant Analysis Exact sciences and technology Food Storage Linear Discriminant Analysis Linear Models Mathematical models Near infrared spectroscopy Principal Component Analysis Projection Software Spectrometric and optical methods Spectroscopy, Near-Infrared Successive Projections Algorithm Wavelength selection |
title | Screening analysis of beer ageing using near infrared spectroscopy and the Successive Projections Algorithm for variable selection |
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