A Biomass-Based Colorimetric Sulfur Dioxide Gas Sensor for Smart Packaging

Sulfur dioxide (SO2) gas, which can effectively prohibit the growth of pathogenic microorganisms, has been internationally used in commercial food packaging to maintain high-quality food and reduce the incidence of foodborne illnesses. However, the current mainstream methods for SO2 detection are ei...

Full description

Saved in:
Bibliographic Details
Published in:ACS nano 2023-04, Vol.17 (7), p.6849-6856
Main Authors: Yuan, Liubo, Gao, Meng, Xiang, Hubing, Zhou, Zihan, Yu, Dongqing, Yan, Ruixiang
Format: Article
Language:English
Subjects:
Biomass
Colorimetry - methods
Food Packaging
Sulfur Dioxide
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Sulfur dioxide (SO2) gas, which can effectively prohibit the growth of pathogenic microorganisms, has been internationally used in commercial food packaging to maintain high-quality food and reduce the incidence of foodborne illnesses. However, the current mainstream methods for SO2 detection are either large and expensive instruments or synthesized chemical-based labels, which are not suitable for large-scale gas detection in food packaging. Recently, we discovered that petunia dye (PD), which is extracted from natural petunia flowers, demonstrates a highly sensitive colorimetric response to SO2 gas with its total color difference (ΔE) modulation reaching up to 74.8 and detection limit down to 1.52 ppm. To apply the extracted petunia dye in smart packaging for real-time gas sensing and food-quality prediction, a flexible and freestanding PD-based SO2 detection label is prepared by incorporating PD in biopolymers and assembling the films through a layer-by-layer approach. The developed label is utilized to predict grapes’ quality and safety by monitoring the embedded SO2 gas concentration. The developed colorimetric SO2 detection label could potentially be used as an intelligent gas sensor for food status prediction in daily life, food storage, and supply chains.
ISSN:1936-0851
1936-086X
DOI:10.1021/acsnano.3c00530