Validation of chemometric models for the determination of deoxynivalenol on maize by mid-infrared spectroscopy
Validation methods for chemometric models are presented, which are a necessity for the evaluation of model performance and prediction ability. Reference methods with known performance can be employed for comparison studies. Other validation methods include test set and cross validation, where some s...
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| Veröffentlicht in: | Mycotoxin research 2003-06, Vol.19 (2), p.149-153 |
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| creator | Kos, G Lohninger, H Krska, R |
| description | Validation methods for chemometric models are presented, which are a necessity for the evaluation of model performance and prediction ability. Reference methods with known performance can be employed for comparison studies. Other validation methods include test set and cross validation, where some samples are set aside for testing purposes. The choice of the testing method mainly depends on the size of the original dataset. Test set validation is suitable for large datasets (>50), whereas cross validation is the best method for medium to small datasets ( |
| doi_str_mv | 10.1007/BF02942955 |
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Reference methods with known performance can be employed for comparison studies. Other validation methods include test set and cross validation, where some samples are set aside for testing purposes. The choice of the testing method mainly depends on the size of the original dataset. Test set validation is suitable for large datasets (>50), whereas cross validation is the best method for medium to small datasets (<50). In this study the K-nearest neighbour algorithm (KNN) was used as a reference method for the classification of contaminated and blank corn samples. A Partial least squares (PLS) regression model was evaluated using full cross validation. Mid-Infrared spectra were collected using the attenuated total reflection (ATR) technique and the fingerprint range (800-1800 cm(-1)) of 21 maize samples that were contaminated with 300 - 2600 µg/kg deoxynivalenol (DON) was investigated. Separation efficiency after principal component analysis/cluster analysis (PCA/CA) classification was 100%. Cross validation of the PLS model revealed a correlation coefficient of r=0.9926 with a root mean square error of calibration (RMSEC) of 95.01. Validation results gave an r=0.8111 and a root mean square error of cross validation (RMSECV) of 494.5 was calculated. No outliers were reported.</description><identifier>ISSN: 0178-7888</identifier><identifier>EISSN: 1867-1632</identifier><identifier>DOI: 10.1007/BF02942955</identifier><identifier>PMID: 23604768</identifier><language>eng</language><publisher>Germany: Springer Nature B.V</publisher><subject>Algorithms ; Chemometrics ; Classification ; Cluster analysis ; Corn ; Correlation coefficient ; Correlation coefficients ; Datasets ; Deoxynivalenol ; Infrared spectra ; Infrared spectroscopy ; Mean square errors ; Methods ; Outliers (statistics) ; Performance evaluation ; Principal components analysis ; Regression models ; Root-mean-square errors ; Spectrum analysis ; Studies ; Test sets ; Zea mays</subject><ispartof>Mycotoxin research, 2003-06, Vol.19 (2), p.149-153</ispartof><rights>Society for Mycotoxin Research 2003.</rights><rights>Society of Mycotoxin Research and Springer 2003</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c288t-1204baec6b2b177c8075eb0683c2b9f1d7971d48bf147906f1bfc01773c370c93</citedby><cites>FETCH-LOGICAL-c288t-1204baec6b2b177c8075eb0683c2b9f1d7971d48bf147906f1bfc01773c370c93</cites></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/23604768$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kos, G</creatorcontrib><creatorcontrib>Lohninger, H</creatorcontrib><creatorcontrib>Krska, R</creatorcontrib><title>Validation of chemometric models for the determination of deoxynivalenol on maize by mid-infrared spectroscopy</title><title>Mycotoxin research</title><addtitle>Mycotoxin Res</addtitle><description>Validation methods for chemometric models are presented, which are a necessity for the evaluation of model performance and prediction ability. Reference methods with known performance can be employed for comparison studies. Other validation methods include test set and cross validation, where some samples are set aside for testing purposes. The choice of the testing method mainly depends on the size of the original dataset. Test set validation is suitable for large datasets (>50), whereas cross validation is the best method for medium to small datasets (<50). In this study the K-nearest neighbour algorithm (KNN) was used as a reference method for the classification of contaminated and blank corn samples. A Partial least squares (PLS) regression model was evaluated using full cross validation. Mid-Infrared spectra were collected using the attenuated total reflection (ATR) technique and the fingerprint range (800-1800 cm(-1)) of 21 maize samples that were contaminated with 300 - 2600 µg/kg deoxynivalenol (DON) was investigated. Separation efficiency after principal component analysis/cluster analysis (PCA/CA) classification was 100%. Cross validation of the PLS model revealed a correlation coefficient of r=0.9926 with a root mean square error of calibration (RMSEC) of 95.01. Validation results gave an r=0.8111 and a root mean square error of cross validation (RMSECV) of 494.5 was calculated. No outliers were reported.</description><subject>Algorithms</subject><subject>Chemometrics</subject><subject>Classification</subject><subject>Cluster analysis</subject><subject>Corn</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Datasets</subject><subject>Deoxynivalenol</subject><subject>Infrared spectra</subject><subject>Infrared spectroscopy</subject><subject>Mean square errors</subject><subject>Methods</subject><subject>Outliers (statistics)</subject><subject>Performance evaluation</subject><subject>Principal components analysis</subject><subject>Regression models</subject><subject>Root-mean-square errors</subject><subject>Spectrum analysis</subject><subject>Studies</subject><subject>Test sets</subject><subject>Zea mays</subject><issn>0178-7888</issn><issn>1867-1632</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp90VtrFTEQB_Agij2tvvgBJCgUKazmtrk8ammtUPBFfV1ymdCUzeaY7CkeP70prRUEfRoYfsww80foBSVvKSHq3YdzwoxgZhwfoQ3VUg1UcvYYbQhVelBa6wN02No1IZILqZ-iA8YlEUrqDVq-2TkFu6ay4BKxv4JcMqw1eZxLgLnhWCperwAHWKHmtDzYAOXHfkk3doalzLg3s00_Abs9zikMaYnVVgi4bcGvtTRftvtn6Em0c4Pn9_UIfT0_-3J6MVx-_vjp9P3l4JnW60AZEc6Cl445qpTXRI3giNTcM2ciDcooGoR2kQpliIzURd-vVdxzRbzhR-j4bu62lu87aOuUU_Mwz3aBsmsTo2TsO2SHb_4LKeeaG0mM6PTVX_S67OrSz5ikMJRRM96i1_9CTGnFhFKKdHVyp3z_S6sQp21N2db9RMl0m-n0J9OOX96P3LkM4YH-DpH_AmQjmyA</recordid><startdate>200306</startdate><enddate>200306</enddate><creator>Kos, G</creator><creator>Lohninger, H</creator><creator>Krska, R</creator><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7T7</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88I</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>7X8</scope></search><sort><creationdate>200306</creationdate><title>Validation of chemometric models for the determination of deoxynivalenol on maize by mid-infrared spectroscopy</title><author>Kos, G ; Lohninger, H ; Krska, R</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c288t-1204baec6b2b177c8075eb0683c2b9f1d7971d48bf147906f1bfc01773c370c93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Algorithms</topic><topic>Chemometrics</topic><topic>Classification</topic><topic>Cluster analysis</topic><topic>Corn</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Datasets</topic><topic>Deoxynivalenol</topic><topic>Infrared spectra</topic><topic>Infrared spectroscopy</topic><topic>Mean square errors</topic><topic>Methods</topic><topic>Outliers (statistics)</topic><topic>Performance evaluation</topic><topic>Principal components analysis</topic><topic>Regression models</topic><topic>Root-mean-square errors</topic><topic>Spectrum analysis</topic><topic>Studies</topic><topic>Test sets</topic><topic>Zea mays</topic><toplevel>online_resources</toplevel><creatorcontrib>Kos, G</creatorcontrib><creatorcontrib>Lohninger, H</creatorcontrib><creatorcontrib>Krska, R</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Mycotoxin research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kos, G</au><au>Lohninger, H</au><au>Krska, R</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Validation of chemometric models for the determination of deoxynivalenol on maize by mid-infrared spectroscopy</atitle><jtitle>Mycotoxin research</jtitle><addtitle>Mycotoxin Res</addtitle><date>2003-06</date><risdate>2003</risdate><volume>19</volume><issue>2</issue><spage>149</spage><epage>153</epage><pages>149-153</pages><issn>0178-7888</issn><eissn>1867-1632</eissn><abstract>Validation methods for chemometric models are presented, which are a necessity for the evaluation of model performance and prediction ability. 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| subjects | Algorithms Chemometrics Classification Cluster analysis Corn Correlation coefficient Correlation coefficients Datasets Deoxynivalenol Infrared spectra Infrared spectroscopy Mean square errors Methods Outliers (statistics) Performance evaluation Principal components analysis Regression models Root-mean-square errors Spectrum analysis Studies Test sets Zea mays |
| title | Validation of chemometric models for the determination of deoxynivalenol on maize by mid-infrared spectroscopy |
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