Evaluation of the Moisture Prediction Capability of Near-Infrared and Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy Using Superdisintegrants as Model Compounds
The superdisintegrants (SDs) moisture content measurement by near-infrared (NIR) spectroscopy and attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy has been evaluated against thermogravimetric analysis as a reference method. SDs with varying moisture content were used t...
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Veröffentlicht in: | Journal of pharmaceutical sciences 2014-12, Vol.103 (12), p.4012-4020 |
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description | The superdisintegrants (SDs) moisture content measurement by near-infrared (NIR) spectroscopy and attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy has been evaluated against thermogravimetric analysis as a reference method. SDs with varying moisture content were used to build calibration and independent model verification data sets. Calibration models were developed based on the water-specific NIR and ATR-FTIR spectral regions using partial least-square regression methods. Because of the NIR water low molar absorptivity, NIR spectroscopy handled higher moisture content (∼81%, w/w) than ATR-FTIR (∼25%, w/w). A two-way ANOVA test was performed to compare R2 values obtained from measured and predicted moisture content (5%–25%, w/w) of SDs. No statistically significant difference was observed between the predictability of NIR and ATR-FTIR methods (p = 0.3504). However, the interactions between the two independent variables, SDs, and analytical methods were statistically significant (p = 0.0002), indicating that the predictability of the analytical method is material dependent. Thus, it would be important to recognize this highly dependent material and analytical method interaction when using NIR moisture analysis in process analytical technology to analyze and control critical quality and performance attributes of raw materials during processing with the goal of ensuring final product quality attributes. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association. |
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SDs with varying moisture content were used to build calibration and independent model verification data sets. Calibration models were developed based on the water-specific NIR and ATR-FTIR spectral regions using partial least-square regression methods. Because of the NIR water low molar absorptivity, NIR spectroscopy handled higher moisture content (∼81%, w/w) than ATR-FTIR (∼25%, w/w). A two-way ANOVA test was performed to compare R2 values obtained from measured and predicted moisture content (5%–25%, w/w) of SDs. No statistically significant difference was observed between the predictability of NIR and ATR-FTIR methods (p = 0.3504). However, the interactions between the two independent variables, SDs, and analytical methods were statistically significant (p = 0.0002), indicating that the predictability of the analytical method is material dependent. Thus, it would be important to recognize this highly dependent material and analytical method interaction when using NIR moisture analysis in process analytical technology to analyze and control critical quality and performance attributes of raw materials during processing with the goal of ensuring final product quality attributes. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.</description><identifier>ISSN: 0022-3549</identifier><identifier>EISSN: 1520-6017</identifier><identifier>DOI: 10.1002/jps.24220</identifier><identifier>PMID: 25332106</identifier><identifier>CODEN: JPMSAE</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>ATR-FTIR spectroscopy ; Calibration ; Least-Squares Analysis ; material science, moisture content ; mathematical model ; multivariate analysis (MVA) ; NIR spectroscopy ; partial least square regression (PLS) ; principal component analysis ; process analytical technology ; Spectroscopy, Fourier Transform Infrared - methods ; Spectroscopy, Near-Infrared - methods ; superdisintegrants (SDs) ; Water - chemistry</subject><ispartof>Journal of pharmaceutical sciences, 2014-12, Vol.103 (12), p.4012-4020</ispartof><rights>2014 Wiley Periodicals, Inc. and the American Pharmacists Association</rights><rights>2014 Wiley Periodicals, Inc. and the American Pharmacists Association.</rights><rights>Copyright © 2014 Wiley Periodicals, Inc., A Wiley Company</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3970-8b6087cc91a6a1dc72292ec62c2adbef1e127feedf907d3454ece73edf88f6213</citedby><cites>FETCH-LOGICAL-c3970-8b6087cc91a6a1dc72292ec62c2adbef1e127feedf907d3454ece73edf88f6213</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjps.24220$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjps.24220$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,778,782,1414,27911,27912,45561,45562</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25332106$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Uppaluri, Sai G.</creatorcontrib><creatorcontrib>Bompelliwar, Sai K.</creatorcontrib><creatorcontrib>Johnson, Paul R.</creatorcontrib><creatorcontrib>Gupta, Mali R.</creatorcontrib><creatorcontrib>Al-Achi, Antoine</creatorcontrib><creatorcontrib>Stagner, William C.</creatorcontrib><creatorcontrib>Haware, Rahul V.</creatorcontrib><title>Evaluation of the Moisture Prediction Capability of Near-Infrared and Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy Using Superdisintegrants as Model Compounds</title><title>Journal of pharmaceutical sciences</title><addtitle>J Pharm Sci</addtitle><description>The superdisintegrants (SDs) moisture content measurement by near-infrared (NIR) spectroscopy and attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy has been evaluated against thermogravimetric analysis as a reference method. SDs with varying moisture content were used to build calibration and independent model verification data sets. Calibration models were developed based on the water-specific NIR and ATR-FTIR spectral regions using partial least-square regression methods. Because of the NIR water low molar absorptivity, NIR spectroscopy handled higher moisture content (∼81%, w/w) than ATR-FTIR (∼25%, w/w). A two-way ANOVA test was performed to compare R2 values obtained from measured and predicted moisture content (5%–25%, w/w) of SDs. No statistically significant difference was observed between the predictability of NIR and ATR-FTIR methods (p = 0.3504). However, the interactions between the two independent variables, SDs, and analytical methods were statistically significant (p = 0.0002), indicating that the predictability of the analytical method is material dependent. Thus, it would be important to recognize this highly dependent material and analytical method interaction when using NIR moisture analysis in process analytical technology to analyze and control critical quality and performance attributes of raw materials during processing with the goal of ensuring final product quality attributes. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.</description><subject>ATR-FTIR spectroscopy</subject><subject>Calibration</subject><subject>Least-Squares Analysis</subject><subject>material science, moisture content</subject><subject>mathematical model</subject><subject>multivariate analysis (MVA)</subject><subject>NIR spectroscopy</subject><subject>partial least square regression (PLS)</subject><subject>principal component analysis</subject><subject>process analytical technology</subject><subject>Spectroscopy, Fourier Transform Infrared - methods</subject><subject>Spectroscopy, Near-Infrared - methods</subject><subject>superdisintegrants (SDs)</subject><subject>Water - chemistry</subject><issn>0022-3549</issn><issn>1520-6017</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kc9uEzEQhy0EomnhwAsgS1zgsK3t_ePdYxW1UFSgIunZcuxxcbRZb21vUV6sz8ekaXtAcLKt-eanGX-EvOPsmDMmTtZjOhaVEOwFmfFasKJhXL4kM6yJoqyr7oAcprRmjDWsrl-TA1GXpeCsmZH7szvdTzr7MNDgaP4F9FvwKU8R6FUE681Daa5HvfK9z9sd9R10LC4GFzUSVA-WnuYMA8bgcxmy7ulPcD2YrAcD9DxM0UOky6iH5ELc0OfexYhQDMmEcUuvkx9u6GIaIVqP9ww32JET1QmHstDTediMYRpsekNeOd0nePt4HpHr87Pl_Etx-ePzxfz0sjBlJ1nRrhrWSmM6rhvNrZFCdAJMI4zQdgWOAxfSAVjXMWnLqq7AgCzx3bauEbw8Ih_3uWMMtxOkrDY-Geh7PUCYkuKNkExKWTWIfvgLXePeA063o2op2q4VSH3aUwa3ThGcGqPf6LhVnKmdTIUy1YNMZN8_Jk6rDdhn8skeAid74LfvYfv_JPX1avEUWe47AD_tDqWoZDygJOsjmlA2-H8M8gekiL7Y</recordid><startdate>201412</startdate><enddate>201412</enddate><creator>Uppaluri, Sai G.</creator><creator>Bompelliwar, Sai K.</creator><creator>Johnson, Paul R.</creator><creator>Gupta, Mali R.</creator><creator>Al-Achi, Antoine</creator><creator>Stagner, William C.</creator><creator>Haware, Rahul V.</creator><general>Elsevier Inc</general><general>Elsevier Limited</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>7QO</scope><scope>7QP</scope><scope>7QR</scope><scope>7TK</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope></search><sort><creationdate>201412</creationdate><title>Evaluation of the Moisture Prediction Capability of Near-Infrared and Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy Using Superdisintegrants as Model Compounds</title><author>Uppaluri, Sai G. ; Bompelliwar, Sai K. ; Johnson, Paul R. ; Gupta, Mali R. ; Al-Achi, Antoine ; Stagner, William C. ; Haware, Rahul V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3970-8b6087cc91a6a1dc72292ec62c2adbef1e127feedf907d3454ece73edf88f6213</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>ATR-FTIR spectroscopy</topic><topic>Calibration</topic><topic>Least-Squares Analysis</topic><topic>material science, moisture content</topic><topic>mathematical model</topic><topic>multivariate analysis (MVA)</topic><topic>NIR spectroscopy</topic><topic>partial least square regression (PLS)</topic><topic>principal component analysis</topic><topic>process analytical technology</topic><topic>Spectroscopy, Fourier Transform Infrared - methods</topic><topic>Spectroscopy, Near-Infrared - methods</topic><topic>superdisintegrants (SDs)</topic><topic>Water - chemistry</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Uppaluri, Sai G.</creatorcontrib><creatorcontrib>Bompelliwar, Sai K.</creatorcontrib><creatorcontrib>Johnson, Paul R.</creatorcontrib><creatorcontrib>Gupta, Mali R.</creatorcontrib><creatorcontrib>Al-Achi, Antoine</creatorcontrib><creatorcontrib>Stagner, William C.</creatorcontrib><creatorcontrib>Haware, Rahul V.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of pharmaceutical sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Uppaluri, Sai G.</au><au>Bompelliwar, Sai K.</au><au>Johnson, Paul R.</au><au>Gupta, Mali R.</au><au>Al-Achi, Antoine</au><au>Stagner, William C.</au><au>Haware, Rahul V.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluation of the Moisture Prediction Capability of Near-Infrared and Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy Using Superdisintegrants as Model Compounds</atitle><jtitle>Journal of pharmaceutical sciences</jtitle><addtitle>J Pharm Sci</addtitle><date>2014-12</date><risdate>2014</risdate><volume>103</volume><issue>12</issue><spage>4012</spage><epage>4020</epage><pages>4012-4020</pages><issn>0022-3549</issn><eissn>1520-6017</eissn><coden>JPMSAE</coden><abstract>The superdisintegrants (SDs) moisture content measurement by near-infrared (NIR) spectroscopy and attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy has been evaluated against thermogravimetric analysis as a reference method. SDs with varying moisture content were used to build calibration and independent model verification data sets. Calibration models were developed based on the water-specific NIR and ATR-FTIR spectral regions using partial least-square regression methods. Because of the NIR water low molar absorptivity, NIR spectroscopy handled higher moisture content (∼81%, w/w) than ATR-FTIR (∼25%, w/w). A two-way ANOVA test was performed to compare R2 values obtained from measured and predicted moisture content (5%–25%, w/w) of SDs. No statistically significant difference was observed between the predictability of NIR and ATR-FTIR methods (p = 0.3504). However, the interactions between the two independent variables, SDs, and analytical methods were statistically significant (p = 0.0002), indicating that the predictability of the analytical method is material dependent. Thus, it would be important to recognize this highly dependent material and analytical method interaction when using NIR moisture analysis in process analytical technology to analyze and control critical quality and performance attributes of raw materials during processing with the goal of ensuring final product quality attributes. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>25332106</pmid><doi>10.1002/jps.24220</doi><tpages>9</tpages></addata></record> |
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subjects | ATR-FTIR spectroscopy Calibration Least-Squares Analysis material science, moisture content mathematical model multivariate analysis (MVA) NIR spectroscopy partial least square regression (PLS) principal component analysis process analytical technology Spectroscopy, Fourier Transform Infrared - methods Spectroscopy, Near-Infrared - methods superdisintegrants (SDs) Water - chemistry |
title | Evaluation of the Moisture Prediction Capability of Near-Infrared and Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy Using Superdisintegrants as Model Compounds |
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