Boudewijn Hollebrands

LCGC International’s interview series on the evolving role of artificial intelligence (AI)/machine learning (ML) in separation science continues with Boudewijn Hollebrands from Unilever Foods R&D, Wageningen, Netherlands, discussing deep learning models to predict food peptide retention times.

For lipid-containing food products like mayonnaise, determining nonvolatile lipid oxidation products, the precursor compounds for rancidity, makes it possible to predict product shelf life at an earlier stage in product development. A method based on normal-phase liquid chromatography with atmospheric pressure photoionization-mass spectrometry (LC–APPI-MS) was developed for this purpose.

Monitoring lipid oxidation during the shelf life of lipid-containing food emulsions, such as mayonnaise, is challenging. It is, however, essential for the development of improved, consumer-preferred products. Determining the nonvolatile lipid oxidation products (NONVOLLOPS), the precursor compounds for rancidity, is required to determine the effectiveness of product stabilization technologies. A method based on normal-phase liquid chromatography with atmospheric pressure photo ionization-mass spectrometry (LC–APPI-MS) was developed for this purpose. The inclusion of a size-exclusion chromatography (SEC) step was needed to remove interfering diacylglycerides and free fatty acids from the samples. The combined SEC and normal-phase LC–APPI-MS method allowed the identification of a wide range of oxidized species including hydroperoxides, oxo-2½ glycerides, epoxides, and other oxidized species. The method was found to be more suitable for the analysis of large sample sets.

Monitoring lipid oxidation during the shelf life of lipid-containing food emulsions, such as mayonnaise, is challenging. It is, however, essential for the development of improved, consumer-preferred products. Determining the nonvolatile lipid oxidation products (NONVOLLOPS), the precursor compounds for rancidity, is required to determine the effectiveness of product stabilization technologies. A method based on normal-phase liquid chromatography with atmospheric pressure photo ionization-mass spectrometry (LC–APPI-MS) was developed for this purpose. The inclusion of a size-exclusion chromatography (SEC) step was needed to remove interfering diacylglycerides and free fatty acids from the samples. The combined SEC and normal-phase LC–APPI-MS method allowed the identification of a wide range of oxidized species including hydroperoxides, oxo-2½ glycerides, epoxides, and other oxidized species. The method was found to be more suitable for the analysis of large sample sets. The relative levels of NONVOLLOPS from bo