A Short-Term Time-Series Data Analysis Algorithm for Flavor Release during the Start of Eating.

Abstract

"Retronasal aroma" refers to the aroma released from food during consumption and traveling through the nose after leaving the mouth. It is closely related to the behavior of odor compounds released from food into the mouth and plays a crucial role in our overall perception of flavor. As a result, research focusing on measuring the behavior of retronasal aroma has gained attention for exploring the relationship between sensory perception and flavor. We attempted to develop a data analysis method that specifically targets a time span of a few seconds to tens of seconds, starting from when food is placed in the mouth during eating and extending to just after swallowing. In this study, we observed a strong correlation between the periodic waveform data derived from performing the third derivative (jerk) on the detection intensity data obtained using a mass spectrometer and the behavior of the detection intensity. Furthermore, by performing a frequency analysis using a fast Fourier transform on the jerk data, it was possible to extract the frequencies that contribute to sensory perception during eating. Furthermore, the reconstructed jerk data derived from the extracted data using the inverse fast Fourier transform provided a clearer explanation of sensory perception during eating. Our algorithm suggests new short-term time-series data applications.