Oniris-UMR CNRS 6144 GEPEA-MA(PS)2/USC INRAE 1498 TRANSFORM, 44322 Nantes, France.
Bel Group-Bio-Engineering Team, 41100 Vendôme, France.
Molecules. 2023 Oct 23;28(20):7224. doi: 10.3390/molecules28207224.
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.
通过分析挥发性和味觉化合物来控制风味感知是食品工业面临的一个关键挑战，因为风味是多种成分复杂混合的结果。机器学习方法已经被用于预测气味感知，但在预测香气感知方面的应用较少。本研究的目的是（1）分析组成和加工过程对感官感知和挥发性有机化合物（VOC）释放的影响，以及（2）从 VOC 特征预测“新鲜奶油”香气感知。根据三因子实验设计，制备了 16 种加工奶油奶酪模型：胶凝剂类型（κ-卡拉胶、琼脂）及其水平和加热时间。对 59 名消费者进行了 R-A-T-A 测试，以描述奶酪模型的感官感知。通过体外咀嚼器（Oniris 设备专利）研究了奶酪模型 bolus 在吞咽过程中释放的 VOC，随后进行 HS-SPME-GC-(ToF)MS 分析。回归树和随机森林用于预测“新鲜奶油”香气感知，即加工奶酪喜爱的主要驱动因素之一，来自吞咽过程中 VOC 的释放。琼脂奶酪模型被感知为具有“牛奶”气味，释放出更多的 VOC；κ-卡拉胶样品被感知为具有“颗粒状”和“易碎”质地以及“咸”和“酸”味道，表现出 VOC 保留能力。加热会使奶酪模型更坚固，并促进美拉德 VOC 的产生，这些 VOC 会产生“煮熟”和“化学”的香气感知。八碳-3,5-二烯-2-酮和辛烷-2,3-二酮是对“新鲜奶油”香气感知有积极贡献的两种主要 VOC。因此，回归树和随机森林是强大的统计工具，可以提供基于 VOC 特征预测奶酪模型香气的初步见解。
