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Cover Image, Volume 135, Issue 16
Publication year - 2018
Publication title -
journal of applied polymer science
Language(s) - English
Resource type - Reports
SCImago Journal Rank - 0.575
H-Index - 166
eISSN - 1097-4628
pISSN - 0021-8995
DOI - 10.1002/app.46247
Subject(s) - cover (algebra) , volume (thermodynamics) , computer science , quality (philosophy) , hyperspectral imaging , information retrieval , partial least squares regression , polymer science , materials science , artificial intelligence , physics , machine learning , mechanical engineering , engineering , quantum mechanics
In this image, Shun Muroga shows a schematic of near‐infrared (NIR) hyperspectral imaging for evaluating molded poly(lactic acid) (PLA) products. Hydrolysis of PLA products, which occurs during melt processing, must be detected rapidly to assure their quality. Partial least squares (PLS) regression was applied to connect NIR spectra and product properties of PLA. NIR imaging combined with the constructed PLS models clearly visualized the differences in the extent of hydrolyzed PLA under varying degrees of crystallization. This cover article appears in J. Appl. Polym. Sci. (2017) volume 135, issue 8, DOI: 10.1002/app.45898 .