Titled, ‘Food Temperature Analysis and Forecasting’, the research has been conducted by Narayana Darapaneni, Sulekha Dileep, Dinakar Komanduri, Nandan Garimella, Swaroop Manchala, Prajwal Nagisetti, Santhosh Vadlamani and Anwesh Reddy Paduri who are a part of Great Learning’s AI & Machine learning Programme.
‘Artificial Intelligence (AI) in food safety can improve food quality, lower operational costs, and minimal losses from the spoilage of edibles.’
It is estimated that the value of food wastage in India is around Rs 92,000 crore per annum and that almost 50 per cent of total waste of the hotel industry comes from the food waste.
The research applied Data Analytics and Mathematical Model to study the temperature of several food items which they recorded over a period of three months. The students collected the data by applying deep learning models and machine learning with time-series sensors to predict accuracy and timing of the changing food temperature.
The model continuously monitors the gas level, the humidity level, and the temperature of vacuum-packed foods and notifies a user when the temperature of stored food warms up. The main purpose of this research is to build an algorithm which would predict temperature fluctuations in each food sample and suggest when the food item shall perish based on the history recorded. This way, hotels will be able to prevent the food from going stale and utilize it for consumption at the right time.
The food industry can apply these algorithms that detect abnormal and missing values of the temperature and humidity data of cold storage areas which are received and displayed by the sensors. This method can predict temperature for the next 10 time periods based on data stored as history.
“Preserving food with higher safety norms is a very important aspect of the food industry. Raising and falling temperature would affect the quality of food, increase operational costs, and loss from spoilage of edibles. To improve food safety by maintaining appropriate temperatures we have built an AI model to predict temperature using historical data,” said Dr. Narayana Darapaneni, Director — AIML Programme.
“This is an ongoing problem for one of the clients in the food industry which we were able to solve. AI implementations have given very good results even in testing and deployment,” said Dr Narayana.
The research received the award at IEEE World AI IOT Congress 2021, which was held in Seattle. It received the best paper award under the Artificial Intelligence and Machine Learning category.
According to the paper, recent advances in Remote Sensing, Cloud Computing and Machine Learning hold the potential to revolutionize Food Safety. Now more than ever, it is possible to automate the maintenance of temperature, microorganism growth, humidity and other parameters pertaining to the upkeep of food standards in real time.
The implementation of Artificial Intelligence in food safety should yield greater food quality, lower operational costs, and minimal losses from the spoilage of edibles. Such benefits would add tremendous value to the day to day operations of the food industry
Source: IANS
