Simon Gregersen Echers

This study evaluates the stabilizing effects of clover grass proteins and their hydrolysate in fish oil-in-water emulsions, showing improved physical and oxidative stability compared to commercial proteins.

This study examines how different processing methods affect the functional properties and composition of clover grass protein prototypes.

This paper describes a two-stage membrane-based biorefinery process that selectively removes green pigments and unwanted proteins from clover-grass, resulting in a high-quality protein product with reduced allergens and increased antioxidants.

Pfly predicts peptide detectability in mass spectrometry using a deep learning model trained on synthetic and biological data, offering customizable classification for different experimental conditions.

This study characterizes the proteome of brewers spent grain (BSG) and identifies key proteins, differential abundance, and safety concerns to guide downstream processing for functional food ingredients.

The paper uses deep learning to analyze how neighboring amino acid pairs affect MS1 intensity, identifying key motifs and their impact on peptide response.

The paper analyzes variance sources in LC-MS data and evaluates transfer learning's impact on ML performance across public datasets.

The paper develops a deep learning model to predict peptide MS1 intensities using sequence data and identifies key physicochemical properties influencing the response.