Detection and discrimination of sulfur dioxide using a colorimetric sensor array.

Area of Science:

• Analytical Chemistry
• Food Science
• Chemical Sensing

Background:

• Sulfur dioxide (SO2) is a common food preservative, but its excessive use poses health risks.
• Accurate detection of SO2 residues in food is crucial for ensuring consumer safety and regulatory compliance.
• Existing methods for SO2 detection can be complex and time-consuming.

Purpose of the Study:

• To develop a simple and effective colorimetric sensor array for the detection and discrimination of sulfur dioxide (SO2) in food.
• To evaluate the performance of the sensor array in identifying SO2 and differentiating it from other substances.
• To assess the array's capability in determining varying concentrations of SO2 in real food samples.

Main Methods:

• Fabrication of a colorimetric sensor array.
• Utilizing difference maps to analyze color changes upon reaction with SO2.
• Applying multivariate analysis techniques, including principal component analysis (PCA), hierarchical clustering analysis (HCA), and linear discriminant analysis (LDA).
• Testing the array's performance on real food samples.

Main Results:

• The colorimetric array successfully discriminated SO2 residues based on unique color fingerprints.
• Multivariate analysis confirmed the sensor array's ability to distinguish SO2 from interferents and different concentrations.
• The array demonstrated good accuracy, precision, and repeatability when applied to real food samples for SO2 determination.

Conclusions:

• The developed colorimetric sensor array is a promising tool for the rapid and reliable detection of SO2 in food.
• This method offers a simple yet powerful approach for food safety monitoring.
• The sensor array's performance indicates its potential for practical application in the food industry.