Allergens in food items can be dangerous for individuals affected by food allergens. Considering how many different ingredients and food items exists, it is hard to keep track of which food items contain relevant allergens. Food businesses in the EU are required to label foods with information about the 14 major food allergens defined by the EU legislation. This improves the situation for affected individuals. Nevertheless, more changes are necessary to provide reasonable protection for people with severe allergic reactions. Recipe websites and online content is usually not labelled with allergens. In addition, the 14 main allergen categories consist of a variety of different ingredients that are not always easy to remember. Scanning websites and recipes for specific allergens can consume a fair amount of time if the reader wants to make sure no allergen is missed. In this article, a dataset is processed and used for machine learning to classify cuisine style and allergens. The dataset used contains labelling for the 14 major allergen categories. Furthermore, a system is proposed that informs the user about style and allergens in a recipe with the help of a browser add-on. To measure the performance of the proposed system, a user study is conducted where participants label recipes with food allergens. A comparison between human and system performance as well as the time needed to read and label recipes concludes this article.