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Although odor classification and odor quantification by e-nose have been studied for a long time, the next stage is to express a detected scent using language. The methods used to map molecular structure parameters, mass spectra, and sensor responses onto language expression are reviewed first. NLP (Natural Language Processing) is useful for that purpose. Conversely, the linguistic expression of the scent can be transformed into sensing data. The odor mixture can be generated so that the measured response pattern can be identical to that of the scent to be created. Two methods, optimization-based and generative AI-based ones, to search for the recipe of the created scent, are explained. Finally, the intended odor is generated using an olfactory display. We provide the latest information on the emerging technology of scent creation.

This book provides a valuable information source for olfaction and taste which includes a comprehensive and timely overview of the current state of knowledge of use for olfaction and taste machines. This book presents original, latest research in the field, with an emphasis on the recent development of human interfacing. It covers the full range of artificial chemical senses including olfaction and taste, from basic through to advanced level. Timely project in that mobile robots, olfactory displays and odour recorders are currently under research, driven by commercial demand.

Predicting odor impression is considered an important step towards measuring the quality of scent in the food, perfume, and cosmetic industries. In odor impression identification and classification, the main target is to predict scent impression while identifying non-target odor impressions are less significant. However, the effectiveness of predictive models depends on the quality of data distribution. Since it is difficult to collect large scale sensory data to create an evenly distributed positive (target odor) and negative (non-target odor) samples, a method is necessary to predict the individual characteristics of scent according to the number of positive samples. Moreover, it is required to predict large number of individual odor impressions from such kind of imbalanced dataset. In this study, we used mass spectrum of flavor molecules and their corresponding odor impressions which have a very disproportioned ratio of positive and negative samples. Thus, we used One-class Classification Support Vector Machine (OCSVM) and Cost-Sensitive MLP (CSMLP) to precisely classify target scent impression. Our experimental results show satisfactory performance in terms of AUC ROC to detect the olfactory impressions of 89 odor descriptors from the mass spectra of flavor molecules.

Recent studies on machine learning technology have reported successful performances in some visual and auditory recognition tasks, while little has been reported in the field of olfaction. In this paper we report computational methods to predict the odor impression of a chemical from its physicochemical properties. Our predictive model utilizes nonlinear dimensionality reduction on mass spectra data and performs the clustering of descriptors by natural language processing. Sensory evaluation is widely used to measure human impressions to smell or taste by using verbal descriptors, such as \"spicy\" and \"sweet\". However, as it requires significant amounts of time and human resources, a large-scale sensory evaluation test is difficult to perform. Our model successfully predicts a group of descriptors for a target chemical through a series of computer simulations. Although the training text data used in the language modeling is not specialized for olfaction, the experimental results show that our method is useful for analyzing sensory datasets. This is the first report to combine machine olfaction with natural language processing for odor character prediction.

Most of the olfactory perception works focused on forward prediction of odor impression, for example, given an odorant’s molecular structure parameters or the sensing data predict its odor impression. So far, mapping of mass spectrum of odorant molecules into the odor perception space (binary or continuous sensory space) has been successfully performed. However, it is difficult to predict odorant’s sensing data associated with binary odor descriptors (e.g., minty, peach, vanilla etc.). In this study, we have proposed a method to extract the corresponding sensing data (mass spectrum as sensing data) for a desired scent impression although one-to-one relationships are not usually guaranteed. Our target is to extract the sensing data for a given odor descriptor that will help perfumers to create scent. This study is first report for predicting sensing data for a given binary odor descriptor.

Recently, olfactory information on odorants has been associated with their corresponding molecular features. Such information has been obtained by predicting the sensory test evaluation scores from the molecular structure parameters or the sensing data. On the other hand, we develop a method of the prediction of molecular features corresponding to the odor impression. We utilize a machine-learning-based odor predictive model introduced in our previous research, and we propose a mathematical model for exploring the sensing data space. By using mass spectrum as sensing data in the predictive model, we can represent predicted mass spectrum as those of an odor mixture, and the mixing ratio can be obtained. We show that the mass spectrum of apple flavor with enhanced ‘fruit’ and ‘sweet’ impressions can be obtained using 59 and 60 molecules respectively by using our analysis method.

Twenty-eight quartz crystal microbalance (QCM) sensors coated with different sensing films were tested and analyzed in this work; twenty-three sensors were coated in different room temperature ionic liquids (RTILs) and five additional QCM sensors were coated with conventional films commonly used as stationary phases in gas chromatography. Four volatile organic compounds (VOCs), in gaseous phase—hexanol, butyl acetate, 2-hexanone, and hexanoic acid—were measured. Two transducer mechanisms were used; resonant frequency shift and resistance shift of a QCM Mason equivalent circuit. The sensors were characterized by their sensitivity to the VOCs and their discrimination power of the four VOCs. The highest separation among VOCs was obtained when frequency and resistance information of both RTIL and conventional films was used, a sensor array composed by two RTILs (1-butyl-1-methylpyrrolidinium bis(trifluoromethanesulfonyl)imide and 1-hexyl-3-methylimidazolium hexafluorophosphate) and two conventional films (tricresyl phosphate and apiezon-L) was found to improve the Wilks lambda separation for the tested gases two orders of magnitude compared to the Wilks lambda using only a conventional films array.

There have been recent advances in predicting odor characteristics using molecular structure parameters of chemicals. Although the molecular structure parameters are available for each chemical, they cannot be used for chemical mixtures. This study will elucidate a computational method of predicting human odor perception from the mass spectra of chemical mixtures such as essential oils. Furthermore, a method for obtaining similarity among odor descriptors has been proposed although the dataset contains binary values only. When the database indicates a set of odor descriptors for one sample, only binary data are available and the correlation between the similar descriptors disappears. Thus, the prediction performance degrades for not considering the similarity among the odor descriptors. Since mass spectra dataset is highly dimensional, we use auto-encoder to learn the compressed representation from the mass spectra of essential oils in its bottleneck hidden layer and then accomplishes the hierarchical clustering to create odor descriptor groups with similar odor impressions using a matrix of continuous value-based correlation coefficient as well as natural language processing. This work will help to expatiate the process of overcoming binary value problem and find out the similarity among odor descriptors using machine learning with natural language semantic representation of words. To overcome the problem of disproportionate ratio of positive and negative class for both the continuous value-based correlation coefficient and word similarity based models, we use Synthetic Minority Oversampling Technique (SMOTE). This model allows us to predict human odor perception through computer simulations by forming odor descriptors group. Accordingly, this study demonstrates the feasibility of ensembling machine learning with natural language processing and SMOTE approach for predicting odor descriptor group from mass spectra of essential oils.

This study examined the effects of Olfactory Virtual Reality (VR) Gaming on cognitive performance in older adults. A VR game environment (“Interactive Smellscape”) was created to enable this, and 30 participants aged 63–90 years completed both VR gaming sessions and cognitive assessments, conducted with a 6-day interval between the two sessions. Significant improvements were observed in spatial tasks of Japanese characters and words, with notable enhancements specifically in visuospatial rotation performance and word-location recall accuracy. However, no significant changes were detected in olfactory identification or other general cognitive tasks. These findings suggest potential cognitive benefits of incorporating VR and olfactory stimuli into interventions for older populations, particularly for tasks requiring attention and spatial processing. The results further underscore the importance of task-specific designs to maximize the utility of multisensory VR systems for cognitive rehabilitation.

Quartz Crystal Microbalance (QCM) is one of the many acoustic transducers. It is the most popular and widely used acoustic transducer for sensor applications. It has found wide applications in chemical and biosensing fields owing to its high sensitivity, robustness, small sized-design, and ease of integration with electronic measurement systems. However, it is necessary to coat QCM with a sensing film. Without coating materials, its selectivity and sensitivity are not obtained. At present, this is not an issue, mainly due to the advancement of oscillator circuits and dedicated measurement circuits. Since a new researcher may seek to understand QCM sensors, we provide an overview of QCM from its fundamental knowledge. Then, we explain some of the recent QCM applications both in gas-phase and liquid-phase. Next, the theory of QCM is introduced by using piezoelectric stress equations and the Mason equivalent circuit, which explains how the QCM behavior is obtained. Then, the conventional equations that govern QCM behaviors in terms of resonant frequency and resistance are described. We show the behavior of QCM with a viscous film based on the acoustic wave equation and Mason equivalent circuit. Then, we present various existing QCM electronic measurement methods. Furthermore, we describe the experiment on QCM with viscous loading and its interpretation based on the Mason equivalent circuit. Lastly, we review some theoretical models to describe QCM behavior with various models.