Research on odor interaction between aldehyde compounds via a partial differential equation (PDE) model

Characterization of Six Lactones in Cheddar Cheese and Their Sensory Interactions Studied by Odor Activity Values and Feller's Additive Model

To evaluate the perceptual interactions among important lactone compounds in cheddar cheese, a molecular-level flavoromic approach, in combination with perceptual interaction analysis, was applied. Six aroma-active lactones with flavor dilution factors ranging from 4 to 128 were identified in three cheddar samples by gas chromatography-olfactometry-mass spectrometry. Odor thresholds of these six aroma-active lactones were determined with values from 7.16 to 30.03 μg/kg using a deodorized cheddar matrix. The odor activity value approach demonstrated complicated interactions among the 15 binary mixtures of six important lactones, including additive, synergistic, or masking effects. Based on partial differential odor intensities, each lactone with similar degrees of perceptual interactions in binary mixtures tends to present synergistic or masking effects. Owing to the difference in the chemical structure and mixture composition, δ-dodecalactone and γ-dodecalactone caused promotive and inhibitory effects on the expression of lactone fruity aroma, respectively.

Visual Analysis of Odor Interaction Based on Support Vector Regression Method

The complex odor interaction between odorants makes it difficult to predict the odor intensity of their mixtures. The analysis method is currently one of the factors limiting our understanding of the odor interaction laws. We used a support vector regression algorithm to establish odor intensity prediction models for binary esters, aldehydes, and aromatic hydrocarbon mixtures, respectively. The prediction accuracy to both training samples and test samples demonstrated the high prediction capacity of the support vector regression model. Then the optimized model was used to generate extra odor data by predicting the odor intensity of more simulated samples with various mixing ratios and concentration levels. Based on these olfactory measured and model predicted data, the odor interaction was analyzed in the form of contour maps. This intuitive method showed more details about the odor interaction pattern in the binary mixture. We found that that the antagonism effect was commonly observed in these binary mixtures and the interaction degree was more intense when the components’ mixing ratio was close. Meanwhile, the odor intensity level of the odor mixture barely influenced the interaction degree. The machine learning algorithms were considered promising tools in odor researches.

Use of a modified vector model for odor intensity prediction of odorant mixtures

Odor intensity (OI) indicates the perceived intensity of an odor by the human nose, and it is usually rated by specialized assessors. In order to avoid restrictions on assessor participation in OI evaluations, the Vector Model which calculates the OI of a mixture as the vector sum of its unmixed components’ odor intensities was modified. Based on a detected linear relation between the OI and the logarithm of odor activity value (OAV—a ratio between chemical concentration and odor threshold) of individual odorants, OI of the unmixed component was replaced with its corresponding logarithm of OAV. The interaction coefficient (cosα) which represented the degree of interaction between two constituents was also measured in a simplified way. Through a series of odor intensity matching tests for binary, ternary and quaternary odor mixtures, the modified Vector Model provided an effective way of relating the OI of an odor mixture with the lnOAV values of its constituents. Thus, OI of an odor mixture could be directly predicted by employing the modified Vector Model after usual quantitative analysis. Besides, it was considered that the modified Vector Model was applicable for odor mixtures which consisted of odorants with the same chemical functional groups and similar molecular structures.