An important goal in researching the biology of olfaction is to link the perception of smells to the chemistry of odorants. In other words, why do some odorants smell like fruits and others like flowers? While the so-called stimulus-percept issue was resolved in the field of color vision some time ago, the relationship between the chemistry and psycho-biology of odors remains unclear up to the present day. Although a series of investigations have demonstrated that this relationship exists, the descriptive and explicative aspects of the proposed models that are currently in use require greater sophistication. One reason for this is that the algorithms of current models do not consistently consider the possibility that multiple chemical rules can describe a single quality despite the fact that this is the case in reality, whereby two very different molecules can evoke a similar odor. Moreover, the available datasets are often large and heterogeneous, thus rendering the generation of multiple rules without any use of a computational approach overly complex. We considered these two issues in the present paper. First, we built a new database containing 1689 odorants characterized by physicochemical properties and olfactory qualities. Second, we developed a computational method based on a subgroup discovery algorithm that discriminated perceptual qualities of smells on the basis of physicochemical properties. Third, we ran a series of experiments on 74 distinct olfactory qualities and showed that the generation and validation of rules linking chemistry to odor perception was possible. Taken together, our findings provide significant new insights into the relationship between stimulus and percept in olfaction. In addition, by automatically extracting new knowledge linking chemistry of odorants and psychology of smells, our results provide a new computational framework of analysis enabling scientists in the field to test original hypotheses using descriptive or predictive modeling. An important issue in olfaction sciences deals with the question of how a chemical information can be translated into percepts. This known as the stimulus-percept problem. Here, we set out to better understand this issue by combining knowledge about the chemistry and cognition of smells with computational olfaction. We also assumed that not only one, but several physicochemical models may describe a given olfactory quality. To achieve this aim, a first challenge was to set up a database with ~1700 molecules characterized by chemical features and described by olfactory qualities (e.g. fruity, woody). A second challenge consisted in developing a computational model enabling the discrimination of olfactory qualities based on these chemical features. By meeting these 2 challenges, we provided for several olfactory qualities new chemical models describing why an odorant molecule smells fruity or woody (among others). For most qualities, multiple (rather than a single) chemical models were generated. These findings provide new elements of knowledge about the relationship between odorant chemistry and perception. They also make it possible to envisage concrete applications in the aroma and fragrance field where chemical characterization of smells is an important step in the design of new products.

A major issue in human olfaction research is to characterize the main dimensions that organize the space of odors. The present study examines this question and shows that, beside pleasantness, trigeminal sensations, and particularly irritation, play an important role. These results were consistent along two different spaces constructed using semantic description and physiological responses to 105 odorants, smelled and described by human participants. Taken together, these findings suggest that salient trigeminal features, in conjunction with pleasantness, are involved in detecting relevant emotional stimuli, and modify the way organisms categorize smells. These results shed light on the importance of trigeminal sensitivity in the well-established defensive function of olfaction.

Adding saffron to dairy products represents an innovative practice to introduce them to niche markets. This paper represents a contribution to this field, as few studies have evaluated the influence of this spice on general aspects and ripening parameters of cheese. In this work, pasteurized ewe milk pressed cheeses with saffron were made to study compositional, microbiological, color, textural, and sensory characteristics in relation to saffron concentration and ripening time. The main changes were observed on sensory characteristics and color. In addition, compositional, textural, and microbiological changes could be observed; among them, saffron cheeses were firmer and more elastic but less prone to fracture. A remarkable result that could lead to further studies is that saffron addition slightly slowed down growth of total and lactic acid bacteria. This resulted in a slightly lower rate of pH decrease during pressing and, as a consequence, lower salt and water content. Compositional differences were not evident by the end of the ripening period.

During the last decades, essential oils (EOs) have been proven to be a natural alternative to additives or pasteurization for the prevention of microbial spoilage in several food matrices. In this work, we tested the antimicrobial activity of EOs from Melissa officinalis, Ocimum basilicum, and Thymus vulgaris against three different microorganisms: Escherichia coli, Clostridium tyrobutyricum, and Penicillium verrucosum. Pressed ewes’ cheese made from milk fortified with EOs (250 mg/kg) was used as a model. The carryover effect of each oil was studied by analyzing the volatile fraction of dairy samples along the cheese-making process using headspace stir bar sorptive extraction coupled to gas chromatography/mass spectrometry. Results showed that the EOs contained in T. vulgaris effectively reduced the counts of C. tyrobutyricum and inhibited completely the growth of P. verrucosum without affecting the natural flora present in the cheese. By contrast, the inhibitory effect of M. officinalis against lactic acid bacteria starter cultures rendered this oil unsuitable for this matrix.