Code and Data for the publication on "Spectroscopic Analysis; a machine learning workflow for raw food classification in a future industry"

Over the years, technology has changed how we produce and find our food through applications, robotics, data, and processing techniques. Such approaches in the food industry ensure quality and affordability while also drives down the costs of keeping the food fresh and increases productivity. Here we provide the raw FTIR data used in the submitted paper entitled as "Spectroscopic Analysis; a machine learning workflow for raw food classification in a future industry" at Sensors Journal. The resulting classifier exhibited ideal efficiency in the multi-class classification of 7 different types of raw food including vegetables, meat, poultry, vegetables, fish and fruits. It must be noted that the food samples used were diverse in terms of storage conditions (temperature, storage time and packaging) while also originated from several different batches.