The need of increasing trustworthiness and transparency in artificial intelligence (AI)-based systems that adhere ethical principles of respect for human autonomy, prevention of harm, fairness, and explainability; has boosting the development of systems that incorporate such issues as a key component. Recommender systems (RSs) are included in such AI-based systems, because they use intelligent algorithms for providing the most suitable items to active users according to other users’ preferences. The RSs success is based on how much customers trust on the system, therefore recommendation explainability has become a crucial dimension for RSs adoption in real-world scenarios. Among the different successful applications of RS, it is remarkable the recent and exponential importance of recommendations for health and wellness areas. Hence, this paper aims at exploring, adapting and applying explanations for nutrition/recipes recommendations, that not only explain why the recommendation is enjoyable but also, it is aware of how healthy is the recommendation. Among the different methodologies to explain recommendations, this paper is focused on post-hoc explainability approaches and its adaptation, application and evaluation for nutrition/recipes recommendation. Eventually, it is included a comprehensive experimental study for characterizing the strengths and weaknesses of such explainability approaches in the recipe recommendation context.