Advancing Mycotoxin Detection: Multivariate Rapid Analysis on Corn Using Surface Enhanced Raman Spectroscopy (SERS)

Area of Science:

• Analytical Chemistry
• Food Science
• Spectroscopy

Background:

• Mycotoxin contamination in food and feed poses significant risks to human and animal health.
• Simultaneous detection of multiple mycotoxins is crucial for comprehensive food safety assessment.
• Existing detection methods may lack the speed and multiplexing capability required for routine screening.

Purpose of the Study:

• To develop a rapid, label-free method for simultaneous detection of three common mycotoxins in corn.
• To utilize surface-enhanced Raman spectroscopy (SERS) for identifying aflatoxin B1 (AFB1), zearalenone (ZEN), and ochratoxin A (OTA).
• To establish the efficacy of multivariate statistical analysis for quantifying mycotoxin concentrations.

Main Methods:

• Label-free surface-enhanced Raman spectroscopy (SERS) was employed for mycotoxin analysis.
• Raman spectra of AFB1, ZEN, and OTA were characterized to establish unique chemical fingerprints.
• Multivariate statistical analysis was applied to correlate SERS spectra with known mycotoxin concentrations.

Main Results:

• SERS enabled clear spectral discrimination between AFB1, ZEN, and OTA.
• Limits of detection (LOD) were determined as 10 ppb for AFB1, 20 ppb for ZEN, and 100 ppb for OTA.
• Multivariate analysis achieved correlation coefficients of 0.74 (AFB1), 0.89 (ZEN), and 0.72 (OTA) for concentration prediction up to 1.5 ppm.
• Sample analysis time was under 30 minutes.

Conclusions:

• Label-free SERS combined with multivariate analysis provides a rapid and effective method for simultaneous mycotoxin detection in corn.
• This approach demonstrates potential for broader application to other mycotoxins and agricultural commodities.
• The method contributes to enhanced food safety protocols through efficient mycotoxin screening.