Real-Time Food Authentication Using a Miniature Mass Spectrometer.

Area of Science:

• Analytical Chemistry
• Food Science
• Spectroscopy

Background:

• Food adulteration poses significant public health and economic risks.
• Efficient, on-site analytical methods are crucial for timely food authentication.
• Existing methods often require extensive sample preparation and are not suitable for rapid, on-site analysis.

Purpose of the Study:

• To develop and evaluate a rapid, on-site method for food authentication using a miniaturized mass spectrometer and ambient ionization techniques.
• To assess the accuracy and efficiency of the developed method for differentiating various food types.
• To demonstrate the utility of a custom software for processing mass spectrometric data for food classification.

Main Methods:

• Utilized a miniaturized mass spectrometer coupled with three ambient ionization methods: electrospray ionization (ESI), desorption electrospray ionization (DESI), and low-temperature plasma ionization (LTP).
• Analyzed chemical fingerprints of milk, fish, and coffee with minimal sample preparation.
• Employed a laboratory-built software, MS food classifier, for data processing, utilizing multivariate statistical methods for classification.

Main Results:

• Achieved 100% classification accuracy for differentiating milk types and fish species.
• Attained 96.4% classification accuracy for coffee types in cross-validation experiments.
• Demonstrated high accuracy (>94%) for classifying milk mixtures and comparable accuracies in real-time classification.

Conclusions:

• The combination of a miniaturized mass spectrometer, ambient ionization, and statistical analysis is effective for on-site, real-time food authentication.
• The developed method offers a rapid and accurate solution for ensuring food safety and quality.
• The MS food classifier software enables efficient data processing and classification, with short processing times for both reference and new data.