The Mass spectrum is one of the representative physicochemical properties of chemical substances. Thus, the aim of this study is to generate a mathematical model from a limited number of samples to predict the impression of a perceived odor. In addition, since a large number of physicochemical parameters are associated with chemicals, this prevents us from obtaining a better understanding of the relationship between odor and chemicals. Since a human cannot continue a sensory test without cessation owing to adaption, conducting sensory evaluation tests to cover a large number of chemicals requires a significant amount of time and resources, and is impractical. The extraction of odor impression is essential not only in the food and cosmetic industries but also in other industries for consumer product evaluation. Sensory evaluation tests for odor have been widely adopted to obtain objective impressions quantified by verbal descriptors.
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In the simplest case, verbal descriptors could just be ‘pleasant’ or ‘unpleasant’. That is, when no suitable concrete example can be used to describe the odor, a combination of verbal descriptors of common everyday words, such as ‘floral’, ‘sweet’, and so on, is instead used. The third way may include the prior two methods. However, if the name of the chemical is unknown to the person, concrete examples such as ‘smells like apples’ or ‘smells like rotten eggs’ are often used. For example, we say ‘sulfur’, ‘ammonia’, etc. When we describe the impression of a certain chemical, if the chemical is sufficiently familiar, the first way is used. Human beings generally use the following ways to describe an impression of a perceived odor: (1) the name of a chemical, (2) the name of a representative instance, and (3) verbal descriptors.
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Previous studies have suggested that perceived chemical stimuli are associated with the complex organizational structure of the biological olfactory system, whose input corresponds to a set of olfactory receptors responding to physicochemical properties of airborne chemicals and whose output corresponds to recognition in the cerebrum. Olfaction, one of the chemical senses of human beings, enables us to understand the surrounding environment by perceiving airborne chemicals. 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). 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.
HOW TO DO MASS SPEC ON CHEM DRAW SERIES
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. As such, there is no established general method for predicting the impression of odor of a chemical only from its physicochemical properties.
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However, the relationship between the impression of odor and the numerous physicochemical parameters has yet to be understood owing to its complexity.
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The sense of smell arises from the perception of odors from chemicals.