Abstract
Controlling flavor perception by analyzing volatile and taste compounds is a key challenge for food industries, as flavor is the result of a complex mix of components. Machine-learning methodologies are already used to predict odor perception, but they are used to a lesser extent to predict aroma perception. The objectives of this work were, for the processed cream cheese models studied, to (1) analyze the impact of the composition and process on the sensory perception and VOC release and (2) predict "fresh cream" aroma perception from the VOC characteristics. Sixteen processed cream cheese models were produced according to a three-factor experimental design: the texturing agent type (κ-carrageenan, agar-agar) and level and the heating time. A R-A-T-A test on 59 consumers was carried out to describe the sensory perception of the cheese models. VOC release from the cheese model boli during swallowing was investigated with an in vitro masticator (Oniris device patent), followed by HS-SPME-GC-(ToF)MS analysis. Regression trees and random forests were used to predict "fresh cream" aroma perception, i.e., one of the main drivers of liking of processed cheeses, from the VOC release during swallowing. Agar-agar cheese models were perceived as having a "milk" odor and favored the release of a greater number of VOCs; κ-carrageenan samples were perceived as having a "granular" and "brittle" texture and a "salty" and "sour" taste and displayed a VOC retention capacity. Heating induced firmer cheese models and promoted Maillard VOCs responsible for "cooked" and "chemical" aroma perceptions. Octa-3,5-dien-2-one and octane-2,3-dione were the two main VOCs that contributed positively to the "fresh cream" aroma perception. Thus, regression trees and random forests are powerful statistical tools to provide a first insight into predicting the aroma of cheese models based on VOC characteristics.