Year: 2024
Type: article
Abstract: Mass spectrometry imaging (MSI) techniques enable the generation of molecular maps from complex and heterogeneous matrices. A burger patty, whether plant-based or meat-based, represents one such complex matrix where studying the spatial distribution of components can unveil crucial features relevant to the consumer experience or production process. Furthermore, the MSI data can aid in the classification of ingredients and composition. Thin sections of different burger samples and vegetable constituents (carrot, pea, pepper, onion, and corn) were prepared for matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI) MSI analysis. MSI measurements were performed on all samples, and the data sets were processed to build three machine learning models aimed at detecting meat adulteration in vegetable burger samples, identifying individual ingredients within the vegetable burger matrix, and discriminating between burgers from different manufacturers. Ultimately, the successful detection of adulteration and differentiation of various burger recipes and their constituent ingredients were achieved. This study demonstrates the potential of MSI coupled with building machine learning models to enable the comprehensive characterization of burgers, addressing critical concerns for both the food industry and consumers.
Authors: Mudita Vats, Bryn Flinders, Theodoros Visvikis, Corinna Dawid, Thomas F. Hofmann, Eva Cuypers, Ron M. A. Heeren
Institutions: Maastricht University, Technical University of Munich
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: hybrid
Estimated APC paid: $4,500
OpenAlex ID: W4405130435