Analysis of multiple mycotoxins-contaminated wheat by a smart analysis platform

Area of Science:

• Agricultural Science
• Analytical Chemistry
• Biotechnology

Background:

• Mycotoxin contamination in crops poses significant risks to food safety and agricultural economics.
• Accurate and rapid detection methods are crucial for effective crop monitoring and quality control.
• Existing methods can be time-consuming and require specialized laboratory equipment.

Purpose of the Study:

• To develop and validate a smart analysis platform for simultaneous, quantitative detection of multiple mycotoxins in wheat.
• To establish a matrix-based standard curve for five key fungal toxins: aflatoxin B1 (AFB1), fumonisin B1 (FB1), T-2, deoxynivalenol (DON), and zearalenone (ZEN).
• To integrate the platform with cloud storage for data management and accessibility.

Main Methods:

• Utilized a multiplex lateral flow strip for simultaneous detection of five mycotoxins.
• Developed a smart analysis platform on an open Android system capable of reading six lines on the strip.
• Implemented a Quick Response (QR) code scanning model for rapid quantification using established standard curves.
• Stored all data and sample information on a central server linked to the cloud.

Main Results:

• Successfully detected and quantified AFB1, FB1, T-2, DON, and ZEN in naturally contaminated wheat samples.
• Achieved limits of detection (LOD) for AFB1, FB1, T-2, DON, and ZEN at 4, 20, 10, 200, and 40 μg/kg, respectively.
• Validated the platform's performance against LC-MS/MS, showing good agreement in results for 10 wheat samples.

Conclusions:

• The developed smart analysis platform provides a powerful tool for quantitative mycotoxin analysis in wheat.
• The platform enables rapid, simultaneous detection and cloud-based data management, enhancing crop monitoring capabilities.
• This technology has the potential to significantly improve food safety and agricultural surveillance systems.