Pleasantness of Binary Odor Mixtures: Rules and Prediction

Olfactory perceptual interactions of maltol with key food odorants in binary mixtures: Scatter matrix statistical analysis of odor intensity in heterogeneous perceptual situation

To study the olfactory perceptual interaction of odorants (OPIO) in binary mixtures containing maltol, a simple and efficient analysis method was developed. This method correlated three variables of the binary mixture: two rates of change in perceived odor intensities of two odorants within the binary mixtures, and the degree of overall odor synergy exhibited by the binary mixtures. By creating a three-dimensional scatter matrix with the variables, the changes in odor intensity of the binary mixture due to OPIO were visualized. The results revealed that the proportions of mutual antagonism, opposite effect, mutual independence, and mutual synergy in the binary mixtures were 64.7%, 32.9%, 1.9%, and 0.5%, respectively. The odor of maltol was mainly masked, and those of esters (68%), aldehydes and ketones (33%) in the mixture were enhanced. In terms of overall odor intensity, 67% of cases involved partial addition, followed by 22.2% overshadowing, and 19.6% stronger component effect.

Predicting minty compounds binary mixtures' pleasantness by odor intensity in aqueous solutions

The aroma of mint is well-liked by the public, and key flavor odorants in mint aroma had been found, but how these molecules interact and form a satisfying odor remains a challenge. Quality, intensity, and pleasantness are our most basic perceptions of aromas; both intensity and pleasantness can be quantified. However, compared to intensity, research on pleasantness was lacking. Pleasantness was one of the most important indicators for formulating a satisfying mint flavor, and the study of binary mixtures was fundamental to our understanding of more complex mixtures. Therefore, the purpose of this study was to explore the characteristics of pleasantness as a function of concentration and, at the same time, to investigate the relationship between intensity and pleasantness in binary mixtures. Thirty sensory evaluation volunteers participated in the evaluation of the intensity and pleasantness of six key flavor odorants of mint and five binary mixtures. The results showed that the pleasantness increased first and then decreased or stabilized with the rising of concentration; even though the interactions in binary mixtures were not the same, their pleasantness could be predicted using the intensities of the components by Response Surface Design of Experiments, and the goodness of fit was greater than 0.92, indicating that the models had the great predictive ability. PRACTICAL APPLICATION: Whether blending flavors or evaluating them, a great deal of experience is required, yet the acquisition of this experience is a long process. Performing these tasks is difficult for the novice, and it helps to quantify the feeling for the flavor and build some mathematical models.

OWSum: algorithmic odor prediction and insight into structure-odor relationships

We derived and implemented a linear classification algorithm for the prediction of a molecule's odor, called Olfactory Weighted Sum (OWSum). Our approach relies solely on structural patterns of the molecules as features for algorithmic treatment and uses conditional probabilities combined with tf-idf values. In addition to the prediction of molecular odor, OWSum provides insights into properties of the dataset and allows to understand how algorithmic classifications are reached by quantitatively assigning structural patterns to odors. This provides chemists with an intuitive understanding of underlying interactions. To deal with ambiguities of the natural language used to describe odor, we introduced descriptor overlap as a metric for the quantification of semantic overlap between descriptors. Thus, grouping of descriptors and derivation of higher-level descriptors becomes possible. Our approach poses a large leap forward in our capabilities to understand and predict molecular features.

A New Classification of Perceptual Interactions between Odorants to Interpret Complex Aroma Systems. Application to Model Wine Aroma

Although perceptual interactions are usually mentioned and blamed for the difficulties in understanding the relationship between odorant composition and aromatic sensory properties, they are poorly defined and categorised. Furthermore, old classifications refer mainly to effects on the odour intensity of the mixture of dissimilar non-blending odours and do not consider odour blending, which is one of the most relevant and influential perceptual interactions. Beginning with the results from classical studies about odour interaction, a new and simple systematic is proposed in which odour interactions are classified into four categories: competitive, cooperative, destructive and creative. The first categories are most frequent and display a mild level of interaction, being characterised mostly by analytical processing. The last two are less frequent and activate (or deactivate) configurational processes of object recognition with deep effects on the quality and intensity of the perception. These interactions can be systematically applied to interpret the formation of sensory descriptors from the odorant composition, suggesting that qualitatively the system works. However, there is a lack of quantitative data to work with odour intensities reliably, and a pressing need to systematise the effects of creative interactions.

A dataset on odor intensity and odor pleasantness of 222 binary mixtures of 72 key food odorants rated by a sensory panel of 30 trained assessors

This paper describes data collected on a set of 222 binary mixtures, based on a set of 72 odorants chiefly found in food, rated by 30 selected and trained assessors for odor intensity and pleasantness. The data included odor intensity (IAB) and pleasantness (PAB) of the mixtures, the intensity (IA, IB) and the pleasantness (PA, PB) of the two components. Moreover, the intensity (IAmix, IBmix) of the two components’ odor perceived within the mixture are included. The quality of the dataset was evaluated by checking subjects’ performance and by testing repeatability using the 24 duplicated trials for each attribute. This set of experimental data would be especially valuable to investigate theories of odor mixture perception in human and to test new models to predict odor perception of odor mixtures.

Perceptual interactions among food odors: Major influences on odor intensity evidenced with a set of 222 binary mixtures of key odorants

This study explored the impact of perceptual interactions on the odor intensity of 222 binary mixtures designed from 72 odorants found in food products. Odor intensity was rated by 30 trained subjects. The results showed that in most cases, the components' odor was perceived within the mixture and their intensity remained the same as in the unmixed situation in 54.3% of cases. Masking was the second major effect (44.8%) and occurred more frequently when components' pleasantness was significantly different. Synergy occurred in a small number of cases (0.9%) and only for four compounds. The overall odor intensity of the mixture was determined to be equal to the strongest component in most cases (73.9%), while partial addition was observed as the second most frequent effect (21.7%), especially when the components had equal intensity. Overall, this work provides general rules about the outcome to expect when mixing key components of food aromas.