Surface modification of ethylene-vinyl alcohol (EVOH) copolymer films by the attachment of triethoxysilane functionality

1996 ◽  
Vol 37 (1) ◽  
pp. 51-57 ◽  
Author(s):  
Jianye Wen ◽  
Garth L. Wilkes
1991 ◽  
Vol 56 (2) ◽  
pp. 500-503 ◽  
Author(s):  
TOHRU IKEGAMI ◽  
KAZUFUMI NAGASHIMA ◽  
MITSUYA SHIMODA ◽  
YOSHINARI TANAKA ◽  
YUTAKA OSAJIMA

2020 ◽  
Vol 24 ◽  
pp. 100502 ◽  
Author(s):  
María Jesús Cejudo-Bastante ◽  
Cristina Cejudo-Bastante ◽  
Marlene J. Cran ◽  
Francisco J. Heredia ◽  
Stephen W. Bigger

2009 ◽  
Vol 113 (5) ◽  
pp. 2988-2996 ◽  
Author(s):  
Seung In Hong ◽  
Ki Beom Kim ◽  
Yeonhee Lee ◽  
Seung Yong Cho ◽  
Jung A Ko ◽  
...  

Toxins ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 545
Author(s):  
Eva María Mateo ◽  
José Vicente Gómez ◽  
Andrea Tarazona ◽  
María Ángeles García-Esparza ◽  
Fernando Mateo

The efficacy of ethylene-vinyl alcohol copolymer films (EVOH) incorporating the essential oil components cinnamaldehyde (CINHO), citral (CIT), isoeugenol (IEG), or linalool (LIN) to control growth rate (GR) and production of T-2 and HT-2 toxins by Fusarium sporotrichioides cultured on oat grains under different temperature (28, 20, and 15 °C) and water activity (aw) (0.99 and 0.96) regimes was assayed. GR in controls/treatments usually increased with increasing temperature, regardless of aw, but no significant differences concerning aw were found. Toxin production decreased with increasing temperature. The effectiveness of films to control fungal GR and toxin production was as follows: EVOH-CIT > EVOH-CINHO > EVOH-IEG > EVOH-LIN. With few exceptions, effective doses of EVOH-CIT, EVOH-CINHO, and EVOH-IEG films to reduce/inhibit GR by 50%, 90%, and 100% (ED50, ED90, and ED100) ranged from 515 to 3330 µg/culture in Petri dish (25 g oat grains) depending on film type, aw, and temperature. ED90 and ED100 of EVOH-LIN were >3330 µg/fungal culture. The potential of several machine learning (ML) methods to predict F. sporotrichioides GR and T-2 and HT-2 toxin production under the assayed conditions was comparatively analyzed. XGBoost and random forest attained the best performance, support vector machine and neural network ranked third or fourth depending on the output, while multiple linear regression proved to be the worst.


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