It's long been possible to give users outside an actual environment that environment's visual and auditory information and thus contribute to establishing presence. However, we've yet to establish much presence when users require olfactory information - such as in environments focused on foods, flowers, perfumes, or, in some cases, more offensive smells. Recently, several VR researchers have become interested in olfaction and olfactory displays that present smells in virtual environments (VEs). In this article, we describe our interactive olfactory display. One of our development goals is to confirm the assumption that users' interactions with the system increases presence. Thus, we used our interactive olfactory display to develop a cooking game in collaboration with electronic engineers and artists.

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.

The research on olfactory sense in virtual reality has gradually expanded even though the technology is still premature. We have developed an olfactory display composed of multiple solenoid valves. In the present study, an extended olfactory display, where 32 component odors can be blended in any recipe, is described; the previous version has only 8 odor components. The size was unchanged even though the number of odor components was four times larger than that in the previous display. The complexity of blending was greatly reduced because of algorithm improvement. The blending method and the fundamental experiment using a QCM (quartz crystal microbalance) sensor are described here

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.

The sense of smell arises from the perception of odors from chemicals. However, the relationship between the impression of odor and the numerous physicochemical parameters has yet to be understood owing to its complexity. As such, there is no established general method for predicting the impression of odor of a chemical only from its physicochemical properties. In this study, we designed a novel predictive model based on an artificial neural network with a deep structure for predicting odor impression utilizing the mass spectra of chemicals, and we conducted a series of computational analyses to evaluate its performance. Feature vectors extracted from the original high-dimensional space using two autoencoders equipped with both input and output layers in the model are used to build a mapping function from the feature space of mass spectra to the feature space of sensory data. The results of predictions obtained by the proposed new method have notable accuracy (R≅0.76) in comparison with a conventional method (R≅0.61).