  Cultures across the world are distinguished by the idiosyncratic patterns in
their cuisines. These cuisines are characterized in terms of their
substructures such as ingredients, cooking processes and utensils. A complex
fusion of these substructures intrinsic to a region defines the identity of a
cuisine. Accurate classification of cuisines based on their culinary features
is an outstanding problem and has hitherto been attempted to solve by
accounting for ingredients of a recipe as features. Previous studies have
attempted cuisine classification by using unstructured recipes without
accounting for details of cooking techniques. In reality, the cooking
processes/techniques and their order are highly significant for the recipe's
structure and hence for its classification. In this article, we have
implemented a range of classification techniques by accounting for this
information on the RecipeDB dataset containing sequential data on recipes. The
state-of-the-art RoBERTa model presented the highest accuracy of 73.30% among a
range of classification models from Logistic Regression and Naive Bayes to
LSTMs and Transformers.
