Testing of agricultural products volatiles to predict quality using machine learning

A method for generating a trained model to determine a quality characteristic of a food item, the method comprising:

• receiving, by a computing system, (i) volatiles data of a first plurality of food items that have a quality characteristic, (ii) volatiles data of a second plurality of food items that do not have the quality characteristic, (iii) human observations of other food items, and (iv) historic supply chain information associated with the other food items, wherein the first plurality of food items, the second plurality of food items, and the other food items are a same food type;
• generating, by the computing system, a volatiles marker profile based on performing random forest modeling on (i) and (ii), wherein the volatiles marker profile indicates one or more volatiles whose concentrations are present in the first plurality of food items but not in the second plurality of food items;
• and
• generating, by the computing system, a machine learning model based on mapping the volatiles marker profile to quality characteristics identified by (iii) and (iv), wherein the machine learning model correlates presence and concentrations of the volatiles with one or more quality characteristics of food items of the same food type.

A method for producing a volatile ripening marker timeline associated with agricultural products, the method comprising:

• separating, by a computing system, a plurality of an agricultural product into n groups, wherein each group of the n groups represents a different ripeness stage of the agricultural product and n is an integer greater than 1;
• independently assessing, by the computing system, presence and concentrations of one or more volatile compounds that are outgassed from each group of the n groups;
• producing, by the computing system, a volatile marker profile for the agricultural product, wherein the volatile marker profile indicates the one or more volatile compounds whose presence and concentrations are identified in some but not all of the n groups;
• and
• generating, by the computing system, a volatile ripening marker timeline, wherein each ripening stage represented in the volatile ripening marker timeline is correlated with the one or more volatile compounds in the volatile marker profile.