Mass Spectrometry Imaging for Spatial Ingredient Classification in Plant-Based Food

Area of Science:

• Food Science
• Analytical Chemistry
• Biotechnology

Background:

• Mass spectrometry imaging (MSI) generates molecular maps of complex samples.
• Burger patties, both meat and plant-based, are complex matrices requiring detailed analysis.
• Understanding ingredient distribution is key for quality control and consumer satisfaction.

Purpose of the Study:

• To develop and apply MSI techniques for burger analysis.
• To utilize machine learning for classifying burger composition and detecting adulteration.
• To differentiate between various burger recipes and identify individual ingredients.

Main Methods:

• Prepared thin sections of burger samples and vegetable constituents.
• Performed matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI) MSI.
• Processed MSI data to build three machine learning models.

Main Results:

• Successfully detected meat adulteration in vegetable burgers.
• Accurately identified individual ingredients within vegetable burger matrices.
• Effectively discriminated between burgers from different manufacturers and recipes.

Conclusions:

• MSI coupled with machine learning offers comprehensive burger characterization.
• This approach addresses critical food industry and consumer concerns regarding food authenticity and quality.
• Demonstrates the potential for advanced analytical techniques in food product analysis.