Abstract
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.
Highlights
Analyzing some of the top features for a trained model and the “nut” category shows that while a lot of words are correct for this category, a good portion contains words that have nothing to do with the allergen category
To evaluate how the proposed system and the trained classifiers perform in the real world, a study with human participants is conducted with a set of randomly selected recipes taken from Yummly, allrecipes and On And Off
The participants range in age from 21 to 43, with 7 of them being male and the rest female. Both groups are introduced to the 14 major allergens proposed by the EU legislation, since some participants have not come into contact with a few of the categories yet
Summary
Allergen labelling has gotten better over the years, especially the introduction of laws that require companies to label their food items with the corresponding allergen labels have contributed greatly to this change. The categories include: cereals containing gluten, crustaceans, eggs, fish, soybeans, milk, nuts, celery, mustard, sesame seeds, sulphur dioxide and sulphite, lupin and molluscs. This is a big step forward in the right direction for individuals affected by food allergens. More changes are necessary to provide good protection for people with severe allergic reactions One such improvement concerns online content and recipe websites. The lack of informational labels for recipe websites and online content can be frustrating. Even if the chef knows about the 14 major food allergens proposed by the EU, remembering all information or rather all ingredients for a certain category can be quite a challenge. The results of the system and the comparison to the user study determine if the system with the trained classifier can be used in real-world scenarios and is a viable alternative to human screening
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