METHODS AND SYSTEMS FOR PREDICTING A REMAINING SHELF-LIFE OF A MILLET FLOUR USING DATA-DRIVEN APPROACH

The disclosure relates generally to methods and systems for predicting a remaining shelf-life of a millet flour using data-driven approach. Conventional techniques for estimating the remaining shelf life of the millet flour are very limited and not effective. The methods and systems of the present disclosure employs a Long short-term memory (LSTM) network architecture-based model to predict the remaining shelf-life of the millet flour based on both the nutritional quality and the rancidity using the information of millet variant and days after milling of the grain. The LSTM network based model is trained on the training data obtained using the chemical reaction kinetic model from the input data comprising a peroxide content, and a fat acidity. The present disclosure predicts the remaining shelf-life of the millet flour in terms of both the remaining nutritional shelf-life and the remaining rancid shelf-life.