Journal of the science of food and agriculture · 2025
Multicolor fluorescent sensors based on a mixture of CdTe quantum dots and ionic liquid for the visual detection of sulfur dioxide residues in food.
СтатьяОценочное исследованиеНазвание и аннотация: Перевод в очереди
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Zhao Xiangyu, Wu Yue, Lan Wei, Che Siying, Rao Yanmin, Min Xinyi, Zhao Yi, Chen Hengye, Long Wanjun, She Yuanbin, Fu Haiyan
Аннотация
BACKGROUND: Sulfur dioxide (SO2) is commonly used as a preservative and antioxidant in food processing; however, its residues can pose significant health risks. The accurate and rapid detection of SO2 residues in food is crucial for ensuring food safety and protecting consumer health. In this study, we have developed a novel multicolor fluorescent sensor system that combines N-acetyl-l-cysteine-modified CdTe quantum dots with a trihexyl(tetradecyl)phosphonium hydroxyphenylbenzoxazole ionic liquid.
RESULTS: The developed dual-response sensor exhibits a remarkable fluorescent color transition, shifting from blue to purple, pink and finally yellow, in response to SO2 concentrations ranging from 10 to 800 mg kg-1 in acid-distilled samples. This color transition enables the semiquantitative visual detection of SO2 residues, providing a rapid and intuitive assessment method. Furthermore, a smartphone application integrated with advanced machine learning algorithms has been developed to facilitate the precise quantification of SO2 through the red-green-blue analysis of fluorescence images, achieving a limit of detection of 9.22 mg kg-1. The sensor system exhibited high accuracy with recovery rates ranging from 94.86% to 109.28% for complex food matrices.
CONCLUSION: The proposed multicolor fluorescent sensor represents a significant advancement in the intelligence, simplicity and efficiency of SO2 residue detection in food samples. This method also offers a valuable reference for improving food safety and quality assurance. © 2025 Society of Chemical Industry.
RESULTS: The developed dual-response sensor exhibits a remarkable fluorescent color transition, shifting from blue to purple, pink and finally yellow, in response to SO2 concentrations ranging from 10 to 800 mg kg-1 in acid-distilled samples. This color transition enables the semiquantitative visual detection of SO2 residues, providing a rapid and intuitive assessment method. Furthermore, a smartphone application integrated with advanced machine learning algorithms has been developed to facilitate the precise quantification of SO2 through the red-green-blue analysis of fluorescence images, achieving a limit of detection of 9.22 mg kg-1. The sensor system exhibited high accuracy with recovery rates ranging from 94.86% to 109.28% for complex food matrices.
CONCLUSION: The proposed multicolor fluorescent sensor represents a significant advancement in the intelligence, simplicity and efficiency of SO2 residue detection in food samples. This method also offers a valuable reference for improving food safety and quality assurance. © 2025 Society of Chemical Industry.