Published: 22 January 2024| Version 1 | DOI: 10.17632/58mfpfxksk.1


Ingredients are the building blocks of packaged food product that enables a product to have texture, flavor, and nutritional information as well. The companies dealing with these products add some ingredients in the form of artificial flavor enhancers, color, and sweeteners to enhance the flavor and appearance of a product. Artificial additives may have health implications as the consumers are not very much aware of the products that are being used therefore it is a complex task for a common person to figure out manually that the product based on its ingredients is good for health. Complex tasks are very efficiently performed in the field of artificial intelligence and machine learning as they are trained for solving complex mathematical problems. With the availability of an appropriate dataset, machine learning can easily solve complex tasks using statistical techniques by model built on available dataset. To achieve the objective, the ingredients dataset has generated by extracting text from images containing ingredient lists of different ready-to-eat food products. In the dataset, several images of different lines of food products have been acquired to create a harmonized dataset. The dataset contains the list of ingredients named with 7 categories and corresponding attributes with appropriate labeling. The dataset will be very useful for training and testing machine learning models for food product classification through their ingredients.


Steps to reproduce

1. List of ingredients is extracted in text form, from images of packaged food product ingredients. 2. The dataset can be reproduced by adding or modifying labels of the ingredients for accurate classification, according to the requirement


Chaudhary Devi Lal University


Food Analysis, Food Additive, Classification (Machine Learning)