Methodological study demonstrates prediction of product quality from spectral data using partial least-squares regression, highlighting rapid non-destructive testing.
ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTPrediction of Product Quality from Spectral Data Using the Partial Least-Squares MethodIldiko Frank, John Feikema, Nick Constantine, and Bruce KowalskiCite this: J. Chem. Inf. Comput. Sci. 1984, 24, 1, 20–24Publication Date (Print):February 1, 1984Publication History Published online6 August 2003Published inissue 1 February 1984https://doi.org/10.1021/ci00041a602Request reuse permissionsArticle Views146Altmetric-Citations58LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (563 KB) Get e-Alerts
No takes yet. Share an insight, caveat, or question.
Frank et al. (1984) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: