Speciation and milk adulteration analysis by rapid ambient liquid MALDI mass spectrometry profiling using machine...

Area of Science:

• Analytical Chemistry
• Food Science
• Biotechnology

Background:

• Increasing consumer and regulatory demand for food quality and traceability necessitates faster, more versatile, and cost-effective analytical techniques.
• Milk analysis presents challenges due to its complex, heterogeneous composition of metabolites, lipids, and proteins.
• Simultaneous, quantitative profiling of milk biomolecules is crucial for assessing food quality, safety, and origin.

Purpose of the Study:

• To develop and validate a rapid, versatile, and cost-effective analytical method for milk analysis.
• To demonstrate the simultaneous profiling of proteins, lipids, and metabolites in milk.
• To apply machine learning for accurate milk speciation and adulterant detection.

Main Methods:

• Atmospheric pressure matrix-assisted laser desorption/ionization (AP-MALDI) using homogenous liquid sample droplets on a Q-TOF mass analyzer.
• Simultaneous detection and profiling of multiply charged proteinaceous ions and singly charged lipids/metabolites.
• Coupling AP-MALDI with user-friendly machine-learning software for data analysis.

Main Results:

• Achieved 100% classification accuracy in rapid milk speciation (cow, goat, sheep, camel).
• Successfully detected cow milk adulteration in goat milk down to 5% concentration.
• Demonstrated high sensitivity (92.5%) and specificity (94.5%) for adulterant detection.

Conclusions:

• AP-MALDI coupled with machine learning provides a powerful tool for rapid and accurate milk analysis.
• The method effectively addresses the need for simultaneous biomolecular profiling, enhancing food quality and traceability.
• This approach offers a cost-effective solution for identifying milk origin and detecting adulteration in the food industry.