Matrix-assisted laser desorption ionization time-of-flight mass spectrometry combined with chemometrics for protein profiling and classification of boiled and extruded quinoa from conventional and organic crops

• Autores: Galindo Luján, Rocío, Pont, Laura, Quispe Jacobo, Fredy Enrique, Sanz Nebot, Victoria, Benavente, Fernando
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Instituto Nacional de Innovación Agraria
• Repositorio: INIA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:null:20.500.12955/2583
• Enlace del recurso: https://hdl.handle.net/20.500.12955/2583; https://doi.org/10.3390/foods13121906
• Nivel de acceso: acceso abierto
• Materia: Boiling; Conventional Farming; Extrusion; Maldiquant; MALDI-TOF-MS; Multivariate; Data Analysis; Organic Farming; Proteins; Quinoa; https://purl.org/pe-repo/ocde/ford#2.11.00; Ebullición; Conventional farming; Agricultura convencional; Spectrometry; Espectrometría; Multivariate analysis; Análisis multivariante; Data analysis; Análisis de datos; Organic agriculture; Agricultura orgánica; Quinua

Quinoa is an Andean crop that stands out as a high-quality protein-rich and gluten-free food. However, its increasing popularity exposes quinoa products to the potential risk of adulteration with cheaper cereals. Consequently, there is a need for novel methodologies to accurately characterize the composition of quinoa, which is influenced not only by the variety type but also by the farming and processing conditions. In this study, we present a rapid and straightforward method based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) to generate global fingerprints of quinoa proteins from white quinoa varieties, which were cultivated under conventional and organic farming and processed through boiling and extrusion. The mass spectra of the different protein extracts were processed using the MALDIquant software (version 1.19.3), detecting 49 proteins (with 31 tentatively identified). Intensity values from these proteins were then considered protein fingerprints for multivariate data analysis. Our results revealed reliable partial least squares-discriminant analysis (PLS-DA) classification models for distinguishing between farming and processing conditions, and the detected proteins that were critical for differentiation. They confirm the effectiveness of tracing the agricultural origins and technological treatments of quinoa grains through protein fingerprinting by MALDI-TOF-MS and chemometrics. This untargeted approach offers promising applications in food control and the food-processing industry.