DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, KS | - |
dc.contributor.author | Ko, YH | - |
dc.contributor.author | Lee, H | - |
dc.contributor.author | Jun, CH | - |
dc.contributor.author | Chung, H | - |
dc.contributor.author | Ku, MS | - |
dc.date.accessioned | 2016-03-31T13:21:29Z | - |
dc.date.available | 2016-03-31T13:21:29Z | - |
dc.date.created | 2009-02-28 | - |
dc.date.issued | 2001-01-13 | - |
dc.identifier.issn | 0169-7439 | - |
dc.identifier.other | 2001-OAK-0000001848 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/19644 | - |
dc.description.abstract | A variety of standardization or transfer methods between near infrared spectrometric instruments are applied for the content prediction of five major constituents of the product at trans-alkylation process with spectra measured on two different instruments, Because process samples are difficult to be stored, we use independent transfer samples by blending some pure materials for the spectrum standardization of the process samples, Using the independent standardization samples, we investigate the transfer performance of well-known piecewise direct standardization combined with several regression methods on the raw spectra. Also, we propose some indirect standardization methods utilizing wavelet transferred scores or factor scores through principal component analysis and partial least squares. The standardization by transferring scores takes only a few transfer coefficients, but it shows similar performance to the spectrum transfer case. In addition, we show the possibility of using a fewer number of stable samples than the original set of samples for the standardization with similar performance, (C) 2001 Elsevier Science B.V. All rights reserved. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.relation.isPartOf | CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | - |
dc.subject | NIR | - |
dc.subject | standardization | - |
dc.subject | PDS | - |
dc.subject | wavelet | - |
dc.subject | factor transfer | - |
dc.subject | CALIBRATION TRANSFER | - |
dc.subject | MONOCHROMATOR INSTRUMENTS | - |
dc.title | Near-infrared spectral data transfer using independent standardization samples: a case study on the trans-alkylation process | - |
dc.type | Article | - |
dc.contributor.college | 산업경영공학과 | - |
dc.identifier.doi | 10.1016/S0169-7439(00)00115-5 | - |
dc.author.google | Park, KS | - |
dc.author.google | Ko, YH | - |
dc.author.google | Lee, H | - |
dc.author.google | Jun, CH | - |
dc.author.google | Chung, H | - |
dc.author.google | Ku, MS | - |
dc.relation.volume | 55 | - |
dc.relation.issue | 1-2 | - |
dc.relation.startpage | 53 | - |
dc.relation.lastpage | 65 | - |
dc.contributor.id | 10070938 | - |
dc.relation.journal | CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, v.55, no.1-2, pp.53 - 65 | - |
dc.identifier.wosid | 000167347100005 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 65 | - |
dc.citation.number | 1-2 | - |
dc.citation.startPage | 53 | - |
dc.citation.title | CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | - |
dc.citation.volume | 55 | - |
dc.contributor.affiliatedAuthor | Jun, CH | - |
dc.identifier.scopusid | 2-s2.0-0035852410 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 20 | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | NIR | - |
dc.subject.keywordAuthor | standardization | - |
dc.subject.keywordAuthor | PDS | - |
dc.subject.keywordAuthor | wavelet | - |
dc.subject.keywordAuthor | factor transfer | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalResearchArea | Mathematics | - |
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