Application of a Deep Learning Model to Predict Liquid Chromatography Retention Times of Food Peptides Across Chromatographic Conditions

> 0.98), and 95% of the retention time predictions of a yeast protein hydrolysate validation set fell within a ±1.0 min window across a wide range of chromatographic conditions, demonstrating both its robustness and practical relevance. We further validated this approach by applying it to the analysis of plant protein hydrolysates. The good performance seen showed its versatility and applicability for diverse sets of peptides including tryptic and non-tryptic peptides. Our work underscores the potential of transfer learning in chromatographic analysis, providing an efficient and adaptable tool for rapid and reliable peptide analysis in food research. Transfer learning enabled the utilization of extensive databases from the proteomics area in the much narrower and specialized field of food peptide analysis.