  The issue of food waste is a major problem contributing to the emission of
greenhouse gases into the environment in addition to causing illness in humans.
This research aimed to develop a correlation between the amount of time until a
food spoiled and dates on food labels in conjunction with sensory observations.
Sensory observations are more accurate as they are immediate observations that
are specific to the food. This experiment observed bananas, bread, milk, eggs,
and leafy greens over a period of time using characteristics specific to the
food to quantify food spoilage. It was shown that the actual time until
spoilage for all foods was longer than that of the best by date and that
sensory observations proved to be a more accurate factor in determining
spoilage. From this data, a machine learning algorithm was trained to predict
if food was spoiled or not, in addition to the number of days until spoilage.
This was presented to the consumer as an app, where the user can track foods
and are reminded to check on them to prevent wastage. In addition, the
experimental procedures were incorporated into a test kit for the consumer to
take instructed observations to assess the spoilage of their food, which are
then entered into the app to improve the algorithm. This paper discusses the
individual effects of sensorial observations on each food and examines the
shifting of consumer habits through an app and test kit to combat environmental
consequences of food waste.
