Application of transmission infrared spectroscopy and partial least squares regression to predict immunoglobulin G concentration in dairy and beef cow colostrum
Abstract The objective of this study was to explore the potential of transmission infrared (TIR) spectroscopy in combination with partial least squares regression (PLSR) for quantification of dairy and beef cow colostral immunoglobulin G (IgG) concentration and assessment of colostrum quality. A tot...
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| Veröffentlicht in: | Journal of animal science 2018-03, Vol.96 (2), p.771-782 |
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| creator | Elsohaby, Ibrahim Windeyer, M Claire Haines, Deborah M Homerosky, Elizabeth R Pearson, Jennifer M McClure, J Trenton Keefe, Greg P |
| description | Abstract
The objective of this study was to explore the potential of transmission infrared (TIR) spectroscopy in combination with partial least squares regression (PLSR) for quantification of dairy and beef cow colostral immunoglobulin G (IgG) concentration and assessment of colostrum quality. A total of 430 colostrum samples were collected from dairy (n = 235) and beef (n = 195) cows and tested by a radial immunodiffusion (RID) assay and TIR spectroscopy. Colostral IgG concentrations obtained by the RID assay were linked to the preprocessed spectra and divided into combined and prediction data sets. Three PLSR calibration models were built: one for the dairy cow colostrum only, the second for beef cow colostrum only, and the third for the merged dairy and beef cow colostrum. The predictive performance of each model was evaluated separately using the independent prediction data set. The Pearson correlation coefficients between IgG concentrations as determined by the TIR-based assay and the RID assay were 0.84 for dairy cow colostrum, 0.88 for beef cow colostrum, and 0.92 for the merged set of dairy and beef cow colostrum. The average of the differences between colostral IgG concentrations obtained by the RID- and TIR-based assays were −3.5, 2.7, and 1.4 g/L for dairy, beef, and merged colostrum samples, respectively. Further, the average relative error of the colostral IgG predicted by the TIR spectroscopy from the RID assay was 5% for dairy cow, 1.2% for beef cow, and 0.8% for the merged data set. The average intra-assay CV% of the IgG concentration predicted by the TIR-based method were 3.2%, 2.5%, and 6.9% for dairy cow, beef cow, and merged data set, respectively.
The utility of TIR method for assessment of colostrum quality was evaluated using the entire data set and showed that TIR spectroscopy accurately identified the quality status of 91% of dairy cow colostrum, 95% of beef cow colostrum, and 89% and 93% of the merged dairy and beef cow colostrum samples, respectively. The results showed that TIR spectroscopy demonstrates potential as a simple, rapid, and cost-efficient method for use as an estimate of IgG concentration in dairy and beef cow colostrum samples and assessment of colostrum quality. The results also showed that merging the dairy and beef cow colostrum sample data sets improved the predictive ability of the TIR spectroscopy. |
| doi_str_mv | 10.1093/jas/sky003 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6140976</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><oup_id>10.1093/jas/sky003</oup_id><sourcerecordid>2041766683</sourcerecordid><originalsourceid>FETCH-LOGICAL-c436t-a002124e03d81bb387ac9af04eec2d81482c2698304b28aa188758a1c94789443</originalsourceid><addsrcrecordid>eNp9kU1rFTEUhoMo9ra68QdIQIQiXJuvmUk2QilahYIbXYczmcw110wyTTLK_Tf-VHOdWtSFi3BIzpP3fLwIPaPkNSWKX-whX-SvB0L4A7ShDWu2nLb8IdoQwuhWSspO0GnOe0Ioa1TzGJ0wxWUjOrZBPy7n2TsDxcWA44hLgpAnl_Px7sKYINkB59makmI2cT5gCAOeIRUHHnsLueB8u1Qs42R3Nfz6WiKe609nCnbTtIS487FfvAv4GpsYjA21UlmL4AFcWnV7a8ea_16Pj7mkZXqCHo3gs316F8_Q53dvP1293958vP5wdXmzNYK3ZQvHWZmwhA-S9j2XHRgFIxHWGlafhGSGtUpyInomAaiUXSOBGiU6qYTgZ-jNqjsv_WSHtUGv5-QmSAcdwem_M8F90bv4TbdUENW1VeD8TiDF28XmousajfUego1L1lQpziVjLa3oi3_QfVxSqONpRgTt2raVvFKvVsrUzedkx_tmKNFH43U1Xq_GV_j5n-3fo7-drsDLFYjL_D-hn4sHu4E</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2041766683</pqid></control><display><type>article</type><title>Application of transmission infrared spectroscopy and partial least squares regression to predict immunoglobulin G concentration in dairy and beef cow colostrum</title><source>MEDLINE</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Oxford University Press Journals All Titles (1996-Current)</source><source>PubMed Central</source><creator>Elsohaby, Ibrahim ; Windeyer, M Claire ; Haines, Deborah M ; Homerosky, Elizabeth R ; Pearson, Jennifer M ; McClure, J Trenton ; Keefe, Greg P</creator><creatorcontrib>Elsohaby, Ibrahim ; Windeyer, M Claire ; Haines, Deborah M ; Homerosky, Elizabeth R ; Pearson, Jennifer M ; McClure, J Trenton ; Keefe, Greg P</creatorcontrib><description>Abstract
The objective of this study was to explore the potential of transmission infrared (TIR) spectroscopy in combination with partial least squares regression (PLSR) for quantification of dairy and beef cow colostral immunoglobulin G (IgG) concentration and assessment of colostrum quality. A total of 430 colostrum samples were collected from dairy (n = 235) and beef (n = 195) cows and tested by a radial immunodiffusion (RID) assay and TIR spectroscopy. Colostral IgG concentrations obtained by the RID assay were linked to the preprocessed spectra and divided into combined and prediction data sets. Three PLSR calibration models were built: one for the dairy cow colostrum only, the second for beef cow colostrum only, and the third for the merged dairy and beef cow colostrum. The predictive performance of each model was evaluated separately using the independent prediction data set. The Pearson correlation coefficients between IgG concentrations as determined by the TIR-based assay and the RID assay were 0.84 for dairy cow colostrum, 0.88 for beef cow colostrum, and 0.92 for the merged set of dairy and beef cow colostrum. The average of the differences between colostral IgG concentrations obtained by the RID- and TIR-based assays were −3.5, 2.7, and 1.4 g/L for dairy, beef, and merged colostrum samples, respectively. Further, the average relative error of the colostral IgG predicted by the TIR spectroscopy from the RID assay was 5% for dairy cow, 1.2% for beef cow, and 0.8% for the merged data set. The average intra-assay CV% of the IgG concentration predicted by the TIR-based method were 3.2%, 2.5%, and 6.9% for dairy cow, beef cow, and merged data set, respectively.
The utility of TIR method for assessment of colostrum quality was evaluated using the entire data set and showed that TIR spectroscopy accurately identified the quality status of 91% of dairy cow colostrum, 95% of beef cow colostrum, and 89% and 93% of the merged dairy and beef cow colostrum samples, respectively. The results showed that TIR spectroscopy demonstrates potential as a simple, rapid, and cost-efficient method for use as an estimate of IgG concentration in dairy and beef cow colostrum samples and assessment of colostrum quality. The results also showed that merging the dairy and beef cow colostrum sample data sets improved the predictive ability of the TIR spectroscopy.</description><identifier>ISSN: 0021-8812</identifier><identifier>EISSN: 1525-3163</identifier><identifier>DOI: 10.1093/jas/sky003</identifier><identifier>PMID: 29385472</identifier><language>eng</language><publisher>US: Oxford University Press</publisher><subject>Animal lactation ; Animals ; Assaying ; Beef ; Calibration ; Cattle ; Colostrum ; Colostrum - chemistry ; Correlation coefficients ; Data processing ; Datasets ; Female ; Immunodiffusion ; Immunoglobulin G ; Immunoglobulin G - chemistry ; Immunoglobulins ; Infrared spectroscopy ; Least squares method ; Least-Squares Analysis ; Mathematical models ; Performance prediction ; Pregnancy ; Quality assessment ; Spectrophotometry, Infrared - methods ; Spectrophotometry, Infrared - veterinary ; Spectroscopy ; Spectrum analysis ; Technology in Animal Science</subject><ispartof>Journal of animal science, 2018-03, Vol.96 (2), p.771-782</ispartof><rights>The Author(s) 2018. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. 2018</rights><rights>Copyright Oxford University Press, UK Feb 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c436t-a002124e03d81bb387ac9af04eec2d81482c2698304b28aa188758a1c94789443</citedby><cites>FETCH-LOGICAL-c436t-a002124e03d81bb387ac9af04eec2d81482c2698304b28aa188758a1c94789443</cites><orcidid>0000-0003-2533-988X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6140976/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6140976/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,724,777,781,882,1579,27905,27906,53772,53774</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29385472$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Elsohaby, Ibrahim</creatorcontrib><creatorcontrib>Windeyer, M Claire</creatorcontrib><creatorcontrib>Haines, Deborah M</creatorcontrib><creatorcontrib>Homerosky, Elizabeth R</creatorcontrib><creatorcontrib>Pearson, Jennifer M</creatorcontrib><creatorcontrib>McClure, J Trenton</creatorcontrib><creatorcontrib>Keefe, Greg P</creatorcontrib><title>Application of transmission infrared spectroscopy and partial least squares regression to predict immunoglobulin G concentration in dairy and beef cow colostrum</title><title>Journal of animal science</title><addtitle>J Anim Sci</addtitle><description>Abstract
The objective of this study was to explore the potential of transmission infrared (TIR) spectroscopy in combination with partial least squares regression (PLSR) for quantification of dairy and beef cow colostral immunoglobulin G (IgG) concentration and assessment of colostrum quality. A total of 430 colostrum samples were collected from dairy (n = 235) and beef (n = 195) cows and tested by a radial immunodiffusion (RID) assay and TIR spectroscopy. Colostral IgG concentrations obtained by the RID assay were linked to the preprocessed spectra and divided into combined and prediction data sets. Three PLSR calibration models were built: one for the dairy cow colostrum only, the second for beef cow colostrum only, and the third for the merged dairy and beef cow colostrum. The predictive performance of each model was evaluated separately using the independent prediction data set. The Pearson correlation coefficients between IgG concentrations as determined by the TIR-based assay and the RID assay were 0.84 for dairy cow colostrum, 0.88 for beef cow colostrum, and 0.92 for the merged set of dairy and beef cow colostrum. The average of the differences between colostral IgG concentrations obtained by the RID- and TIR-based assays were −3.5, 2.7, and 1.4 g/L for dairy, beef, and merged colostrum samples, respectively. Further, the average relative error of the colostral IgG predicted by the TIR spectroscopy from the RID assay was 5% for dairy cow, 1.2% for beef cow, and 0.8% for the merged data set. The average intra-assay CV% of the IgG concentration predicted by the TIR-based method were 3.2%, 2.5%, and 6.9% for dairy cow, beef cow, and merged data set, respectively.
The utility of TIR method for assessment of colostrum quality was evaluated using the entire data set and showed that TIR spectroscopy accurately identified the quality status of 91% of dairy cow colostrum, 95% of beef cow colostrum, and 89% and 93% of the merged dairy and beef cow colostrum samples, respectively. The results showed that TIR spectroscopy demonstrates potential as a simple, rapid, and cost-efficient method for use as an estimate of IgG concentration in dairy and beef cow colostrum samples and assessment of colostrum quality. The results also showed that merging the dairy and beef cow colostrum sample data sets improved the predictive ability of the TIR spectroscopy.</description><subject>Animal lactation</subject><subject>Animals</subject><subject>Assaying</subject><subject>Beef</subject><subject>Calibration</subject><subject>Cattle</subject><subject>Colostrum</subject><subject>Colostrum - chemistry</subject><subject>Correlation coefficients</subject><subject>Data processing</subject><subject>Datasets</subject><subject>Female</subject><subject>Immunodiffusion</subject><subject>Immunoglobulin G</subject><subject>Immunoglobulin G - chemistry</subject><subject>Immunoglobulins</subject><subject>Infrared spectroscopy</subject><subject>Least squares method</subject><subject>Least-Squares Analysis</subject><subject>Mathematical models</subject><subject>Performance prediction</subject><subject>Pregnancy</subject><subject>Quality assessment</subject><subject>Spectrophotometry, Infrared - methods</subject><subject>Spectrophotometry, Infrared - veterinary</subject><subject>Spectroscopy</subject><subject>Spectrum analysis</subject><subject>Technology in Animal Science</subject><issn>0021-8812</issn><issn>1525-3163</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kU1rFTEUhoMo9ra68QdIQIQiXJuvmUk2QilahYIbXYczmcw110wyTTLK_Tf-VHOdWtSFi3BIzpP3fLwIPaPkNSWKX-whX-SvB0L4A7ShDWu2nLb8IdoQwuhWSspO0GnOe0Ioa1TzGJ0wxWUjOrZBPy7n2TsDxcWA44hLgpAnl_Px7sKYINkB59makmI2cT5gCAOeIRUHHnsLueB8u1Qs42R3Nfz6WiKe609nCnbTtIS487FfvAv4GpsYjA21UlmL4AFcWnV7a8ea_16Pj7mkZXqCHo3gs316F8_Q53dvP1293958vP5wdXmzNYK3ZQvHWZmwhA-S9j2XHRgFIxHWGlafhGSGtUpyInomAaiUXSOBGiU6qYTgZ-jNqjsv_WSHtUGv5-QmSAcdwem_M8F90bv4TbdUENW1VeD8TiDF28XmousajfUego1L1lQpziVjLa3oi3_QfVxSqONpRgTt2raVvFKvVsrUzedkx_tmKNFH43U1Xq_GV_j5n-3fo7-drsDLFYjL_D-hn4sHu4E</recordid><startdate>20180306</startdate><enddate>20180306</enddate><creator>Elsohaby, Ibrahim</creator><creator>Windeyer, M Claire</creator><creator>Haines, Deborah M</creator><creator>Homerosky, Elizabeth R</creator><creator>Pearson, Jennifer M</creator><creator>McClure, J Trenton</creator><creator>Keefe, Greg P</creator><general>Oxford University Press</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>3V.</scope><scope>7RQ</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>88I</scope><scope>8AF</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M2P</scope><scope>M7P</scope><scope>M7S</scope><scope>MBDVC</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0X</scope><scope>U9A</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-2533-988X</orcidid></search><sort><creationdate>20180306</creationdate><title>Application of transmission infrared spectroscopy and partial least squares regression to predict immunoglobulin G concentration in dairy and beef cow colostrum</title><author>Elsohaby, Ibrahim ; Windeyer, M Claire ; Haines, Deborah M ; Homerosky, Elizabeth R ; Pearson, Jennifer M ; McClure, J Trenton ; Keefe, Greg P</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c436t-a002124e03d81bb387ac9af04eec2d81482c2698304b28aa188758a1c94789443</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Animal lactation</topic><topic>Animals</topic><topic>Assaying</topic><topic>Beef</topic><topic>Calibration</topic><topic>Cattle</topic><topic>Colostrum</topic><topic>Colostrum - chemistry</topic><topic>Correlation coefficients</topic><topic>Data processing</topic><topic>Datasets</topic><topic>Female</topic><topic>Immunodiffusion</topic><topic>Immunoglobulin G</topic><topic>Immunoglobulin G - chemistry</topic><topic>Immunoglobulins</topic><topic>Infrared spectroscopy</topic><topic>Least squares method</topic><topic>Least-Squares Analysis</topic><topic>Mathematical models</topic><topic>Performance prediction</topic><topic>Pregnancy</topic><topic>Quality assessment</topic><topic>Spectrophotometry, Infrared - methods</topic><topic>Spectrophotometry, Infrared - veterinary</topic><topic>Spectroscopy</topic><topic>Spectrum analysis</topic><topic>Technology in Animal Science</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Elsohaby, Ibrahim</creatorcontrib><creatorcontrib>Windeyer, M Claire</creatorcontrib><creatorcontrib>Haines, Deborah M</creatorcontrib><creatorcontrib>Homerosky, Elizabeth R</creatorcontrib><creatorcontrib>Pearson, Jennifer M</creatorcontrib><creatorcontrib>McClure, J Trenton</creatorcontrib><creatorcontrib>Keefe, Greg P</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Career & Technical Education Database</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>STEM Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</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>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</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>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Research Library (Corporate)</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>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>SIRS Editorial</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of animal science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Elsohaby, Ibrahim</au><au>Windeyer, M Claire</au><au>Haines, Deborah M</au><au>Homerosky, Elizabeth R</au><au>Pearson, Jennifer M</au><au>McClure, J Trenton</au><au>Keefe, Greg P</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application of transmission infrared spectroscopy and partial least squares regression to predict immunoglobulin G concentration in dairy and beef cow colostrum</atitle><jtitle>Journal of animal science</jtitle><addtitle>J Anim Sci</addtitle><date>2018-03-06</date><risdate>2018</risdate><volume>96</volume><issue>2</issue><spage>771</spage><epage>782</epage><pages>771-782</pages><issn>0021-8812</issn><eissn>1525-3163</eissn><abstract>Abstract
The objective of this study was to explore the potential of transmission infrared (TIR) spectroscopy in combination with partial least squares regression (PLSR) for quantification of dairy and beef cow colostral immunoglobulin G (IgG) concentration and assessment of colostrum quality. A total of 430 colostrum samples were collected from dairy (n = 235) and beef (n = 195) cows and tested by a radial immunodiffusion (RID) assay and TIR spectroscopy. Colostral IgG concentrations obtained by the RID assay were linked to the preprocessed spectra and divided into combined and prediction data sets. Three PLSR calibration models were built: one for the dairy cow colostrum only, the second for beef cow colostrum only, and the third for the merged dairy and beef cow colostrum. The predictive performance of each model was evaluated separately using the independent prediction data set. The Pearson correlation coefficients between IgG concentrations as determined by the TIR-based assay and the RID assay were 0.84 for dairy cow colostrum, 0.88 for beef cow colostrum, and 0.92 for the merged set of dairy and beef cow colostrum. The average of the differences between colostral IgG concentrations obtained by the RID- and TIR-based assays were −3.5, 2.7, and 1.4 g/L for dairy, beef, and merged colostrum samples, respectively. Further, the average relative error of the colostral IgG predicted by the TIR spectroscopy from the RID assay was 5% for dairy cow, 1.2% for beef cow, and 0.8% for the merged data set. The average intra-assay CV% of the IgG concentration predicted by the TIR-based method were 3.2%, 2.5%, and 6.9% for dairy cow, beef cow, and merged data set, respectively.
The utility of TIR method for assessment of colostrum quality was evaluated using the entire data set and showed that TIR spectroscopy accurately identified the quality status of 91% of dairy cow colostrum, 95% of beef cow colostrum, and 89% and 93% of the merged dairy and beef cow colostrum samples, respectively. The results showed that TIR spectroscopy demonstrates potential as a simple, rapid, and cost-efficient method for use as an estimate of IgG concentration in dairy and beef cow colostrum samples and assessment of colostrum quality. The results also showed that merging the dairy and beef cow colostrum sample data sets improved the predictive ability of the TIR spectroscopy.</abstract><cop>US</cop><pub>Oxford University Press</pub><pmid>29385472</pmid><doi>10.1093/jas/sky003</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0003-2533-988X</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Oxford University Press Journals All Titles (1996-Current); PubMed Central |
| subjects | Animal lactation Animals Assaying Beef Calibration Cattle Colostrum Colostrum - chemistry Correlation coefficients Data processing Datasets Female Immunodiffusion Immunoglobulin G Immunoglobulin G - chemistry Immunoglobulins Infrared spectroscopy Least squares method Least-Squares Analysis Mathematical models Performance prediction Pregnancy Quality assessment Spectrophotometry, Infrared - methods Spectrophotometry, Infrared - veterinary Spectroscopy Spectrum analysis Technology in Animal Science |
| title | Application of transmission infrared spectroscopy and partial least squares regression to predict immunoglobulin G concentration in dairy and beef cow colostrum |
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