Named Entity Recognition (NER) and Named Entity Linking (NEL) are key tasks in Information Extraction, addressing the identification and normalization of entity mentions from raw text. In the domain of food and nutrition, there have been several NER methods already developed, however, when applied to scientific text, they fail to generalize and produce large performance degradation. This introduces the need for new food NER and NEL models, developed specifically for extracting food entities from scientific text. In this paper, we present a scientific food NER and NEL model, SciFoodNER, obtained by fine-tuning transformer models on a corpus of scientific abstracts annotated with food entities. The models can identify mentions of food entites from raw text, and link the food entities to the Hansard Taxonomy, the FoodOn ontology and the Systematised Nomenclature of Medicine Clinical Terms (SNOMEDCT). Out of the evaluated models, the BioBERT model achieves the best results, reaching a median macro-averaged F1 score of 0.90 for the NER task, 0.66 for the NEL task linking to the Hansard Taxonomy, 0.43 for the NEL task linking to the FoodOn ontology and 0.58 for the NEL task linking to the SNOMEDCT ontology.