Nowadays, consumers’ well-being plays a decisive role in the purchase of cosmetic products. Although factors influencing consumers’ well-being are very subjective, companies strive to develop their products in such a way that a positive effect is likely. Therefore, methods are required to objectively explore and scientifically prove the product’s performance on humans. In this placebo-controlled study, a method was developed to evaluate relaxation or stress relief associated with one olfactory ingredient of a cosmetic product (face cream). Our experimental protocol included product testing in 25 healthy females, while an emotion questionnaire, analysis of saliva samples regarding the concentration of the hormones cortisol and α-amylase and mobile EEG measurement for quantification of the alpha brain waves before and after stress induction were conducted. It was shown that with this experimental design, the sample with the ingredient produced significant stress relief, as evidenced by significantly less negative emotion, significantly lowered cortisol levels and showed a trend towards a significant increase in alpha activity compared to placebo application. Our data provide evidence that this method is suitable for analyzing the differences between the two samples. In the future, this method can be utilized in the current or a further optimized form to evaluate the psychophysiological effects of cosmetic products on humans.

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.

Aroma compositions are usually complex mixtures of odor-active compounds exhibiting diverse molecular structures. Due to chemical interactions of these compounds in the olfactory system, assessing or even predicting the olfactory quality of such mixtures is a difficult task, not only for statistical models, but even for trained assessors. Here, we combine fast automated analytical assessment tools with human sensory data of 11 experienced panelists and machine learning algorithms. Using 16 previously analyzed whisky samples (American or Scotch origin), we apply the linear classifier OWSum to distinguish the samples based on their detected molecules and to gain insights into the key molecular structure characteristics and odor descriptors for sample type. Moreover, we use OWSum and a Convolutional Neural Network (CNN) architecture to classify the five most relevant odor attributes of each sample and predict their sensory scores with promising accuracies (up to F1: 0.71, MCC: 0.68, ROCAUC: 0.78). The predictions outperform the inter-panelist agreement and thus demonstrate previously impossible data-driven sensory assessment in mixtures.

In recent years, multisensory integration of visual and olfactory stimuli has extensively been explored resulting in the identification of responsible brain areas. As the experimental designs of previous research often include alternating presentations of unimodal and bimodal stimuli, the conditions cannot be regarded as completely independent. This could lead to effects of an expected but surprisingly missing sensory modality. In our experiment, we used a common functional magnetic resonance imaging (fMRI) study design with alternating strong unimodal and bimodal olfactory‐visual food stimuli, in addition to a slight overhang of the bimodal stimuli in an effort to examine the effects of removing a visual or olfactory congruent stimulus for older people (41–83 years). Our results suggest that the processing of olfactory and visual stimuli stays intact over a wide age‐range and that the utilization of strong stimuli does not lead to superadditive multisensory integration in accordance with the principle of inverse effectiveness. However, our results demonstrate that the removal of a stimulus modality leads to an activation of additional brain areas. For example, when the visual stimulus modality is missing, the right posterior superior temporal gyrus shows higher activation, whereas the removal of the olfactory stimulus modality leads to higher activation in the amygdala/hippocampus and the postcentral gyrus. These brain areas are related to attention, memory, and the search of the missing stimulus. Consequently, careful attention must be paid to the design of a valid, multimodal sensory experiment while also controlling for cognitive expectancy effects that might confound multimodal results.

Providing nutrition or health labels on product packaging can be an effective strategy to promote a conscious and healthier diet. However, such labels also have the potential to be counterproductive by creating obstructive expectations about the flavor of the food and influencing odor perception. Conversely, olfaction could significantly influence label perception, whereby negative expectations could be mitigated by pleasant odors. This study explored the neural processing of the interplay between odors and nutrition labels using fMRI in 63 participants of either sex, to whom we presented beverage labels with different nutrition-related statements either with or without a congruent odor. On a behavioral level, the products for which the label was presented together with the odor were in general perceived as more positive than the same labels without an odor. Neuroimaging results revealed that added odors significantly altered activity in brain regions associated with flavor and label processing as well as decision-making, with higher activations in the right amygdala/piriform cortex (Amy/pirC) and orbitofrontal cortex. The presentation of odors induced pattern-based encoding in the right dorsolateral prefrontal cortex, the left ventral striatum/nucleus accumbens, and the right Amy/pirC when accounting for behavioral differences. This suggests that odors influence the effects of labels both on a neural and behavioral level and may offer the possibility of compensating for obstructive associations. The detailed mechanisms of odor and statement interactions within the relevant brain areas should be further investigated, especially for labels that evoke negative expectations.