# Battery-Free Pork Freshness Estimation Based on Colorimetric Sensors and Machine Learning
## 4 RF Harvesting Experiment
### Kalman Filter for RF Harvesting Evaluation
In particular, when the pork is exposed to room temperature due to reasons such as transportation after purchase, it decays rapidly, and the browning process accelerates accordingly. Pork stored at room temperature reaches a state of decay within the first 6 h of measurement initiation. The data measured by the proposed system include the discoloration and changes in gas levels that occur with decay. When stored in a sealed container, eCO\({}_{2}\) is initially produced, followed by an average production of TVOC after a short time. The color change and eCO\({}_{2}\) change show a similar temporal trend, while TVOC data tend to occur slightly slower than eco data. Through this, it is suggested that the gas data produced by the decaying pork are related to eCO\({}_{2}\), and depending on the degree of decay, the red Hue and Value in HSV are suitable indicators for decay. The 1D-CNN machine learning model was then applied using the measured RGB color values to classify the color difference, achieving a classification accuracy of 98.7%. This allows for the application of smart-sensor tags based on RF energy harvesting to develop an automated classification system and successfully determine pork freshness. Conceptualization, D.-E.K. and W.-Y.C. ; methodology, D.-E.K. ; software, D.-E.K. ; validation, D.-E.K. and Y.A.N. ; visualization, Y.A.N. ; project administration, W.-Y.C. ; funding acquisition, W.-Y.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT), grant number 2019R1A2C1089139. Not applicable. The experimental data will not be disclosed for reasons such as patent preparation and security. The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c c} \hline  & **MLP** & **LSTM** & **1D-CNN** & **ConvLSTM** \\ \hline Accuracy & 0.9797 & 0.9814 & 0.9870 & 0.9676 \\ Loss & 0.0347 & 0.0314 & 0.0307 & 0.0950 \\ \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of the four machine learning models. ## References

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