[1K3-OS-10a] [Organized Session] OS-10

[1K3-OS-10a-02] Multi-spectroscopic sensing of lettuce for freshness measurement using machine learning

〇Takaharu Kameoka¹, Akane Tsukahara¹, Shinichi Kameoka¹, Ryoei Ito¹, Atsushi Hashimoto¹ (1. Mie University)

Keywords:Lettuce, Freshness, Optical sensing

Many conventional freshness (quality) measurement methods are separation analysis, and there are a number of problems such as extremely time-consuming measurement etc. in this analysis. Therefore, in this study, attention was focused on elements and organic matter, and tried to quantify the process of degradation of lettuce. Furthermore, from the surface color and moisture measurement, the relationship between freshness (deterioration) evaluation by appearance quality and objective evaluation is grasped and data set and evaluation method for freshness evaluation leading to machine learning in the future were studied. As a result, it became clear that there is a relationship between surface color and internal quality. It is suggested that freshness of lettuce can be quantified and predicted using only surface color information if accumulating experimental data and constructing a relationship between color change and internal quality using machine learning and depth learning.