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Issue 23, 2021
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Development of a polylactic acid-coated nanocellulose/chitosan-based film indicator for real-time monitoring of beef spoilage

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Abstract

Food safety is one of the biggest challenges in global markets. There is a critical need to develop a simple, affordable, and environmentally friendly color indicator that can quickly and conveniently monitor and indicate the quality of packaged food products in the home, supermarkets, shops, etc. This study aimed to develop a nanocellulose/chitosan-based film coated with polylactic acid (PLA) to monitor beef spoilage in real-time. This film named PLA/NCM was fabricated by casting a suspension of a nanocellulose/chitosan mixture doped with methyl red, followed by a coating of PLA on the film surface. The film displayed a visible color change in response to different pH buffer solutions (2–10). The PLA/NCM film was applied to monitor the spoilage of beef under a refrigeration condition of 4 °C and showed an apparent color change after 5 days as a threshold for beef spoilage. The color modulation of the PLA/NCM films was processed each time via a colorimetric device and revealed substantial color difference values (ΔE) after 5 days of beef spoilage. The total viable microbial counts (TVC) and pH of the beef sample were determined, and the findings showed that the TVC and pH increased simultaneously during the beef spoilage. Although further research is necessary, the PLA/NCM film has the potential to be a color indicator for application in both smart food packaging and real-time monitoring of spoilage of beef and other meat products.

Graphical abstract: Development of a polylactic acid-coated nanocellulose/chitosan-based film indicator for real-time monitoring of beef spoilage

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Supplementary files

Article information


Submitted
04 Mar 2021
Accepted
30 Apr 2021
First published
30 Apr 2021

Anal. Methods, 2021,13, 2612-2623
Article type
Paper

Development of a polylactic acid-coated nanocellulose/chitosan-based film indicator for real-time monitoring of beef spoilage

A. Sobhan, K. Muthukumarappan, L. Wei, R. Zhou and H. Tummala, Anal. Methods, 2021, 13, 2612
DOI: 10.1039/D1AY00365H

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