# Battery-Free Pork Freshness Estimation Based on Colorimetric Sensors and Machine Learning
## 4 RF Harvesting Experiment
### Kalman Filter for RF Harvesting Evaluation
The learning process of the 1D-CNN model is shown in Figure 10:1 The highest accuracy value of 0.9870 was recorded in the 86 th epoch, with some deviations between epochs. The accuracy of the validation data increased slightly compared to the training data. The lowest loss was recorded in the 149 th epoch at 0.0307, indicating a progressively similar performance. The validation loss was slightly reduced. Compared to LSTM, the trend of accuracy for each epoch appears similar, but it can be confirmed that the validation loss performance is similar and the stability during training is higher. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Filter** & **Kernel Size** & **Learning Rate** & **Dropout Rate** & **Weight Decay** & **Train Accuracy** \\ \hline \(64,128\) & 2 & 0.001 & 0.3 & 0.0001 & 0.9768 \\ \(64,128\) & 3 & 0.001 & 0.3 & 0.0001 & 0.9851 \\ \(64,32\) & 2 & 0.001 & 0.5 & 0.0001 & 0.9870 \\ \(64,32\) & 3 & 0.001 & 0.3 & 0.001 & 0.9861 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter optimization using GRID Searching for 1D-CNN. Figure 17: Training process for the LSTM model. Figure 18: Structure of the 1D-CNN model. ### ConvLSTM Model

The convolution layer convolves the input of the layer to generate an output tensor. In order to use the HSV values obtained from the RGB data as input to the model, a reshaping process was conducted, and the features extracted through PCA feature transformation were used as input to the ConvLSTM model [39]. The employed ConvLSTM model is similar to Figure 20 and is designed with a five-layer structure. The input layer consists of 64 filters and a kernel size of (1,3), and the activation function used is ReLU. The ConvLSTM layer is followed by a flattened layer with a dropout ratio set to 0.3. The hidden layer has 32 nodes with a ReLU activation function and an L2 regularizer of 0.001. The output layer uses the SoftMax function, and the optimizer used is Adam. Additionally, grid search algorithms were used for tuning. The learning process of the ConvLSTM model is shown in Figure 21. The highest accuracy value was recorded at the 138 th epoch, which was 0.9676. In comparison to MLP, it shows less variability in performance at the beginning, with a relatively stable performance observed after 20 epochs. Additionally, the lowest loss value of 0.0950 was recorded at the 132 nd epoch. This model exhibits some deviation in accuracy and loss for each epoch, and the difference between the results learned from the validation and test data for loss is larger than that of the previous MLP model. Figure 19: Training process of the 1D-CNN model. Figure 20: Structure of the ConvLSTM model. Table 2 shows the performance comparison of machine learning models with parameters optimized using the Grid Search algorithm. After considering the performance of all the models, the 1D-CNN model is selected as the machine learning model for monitoring the status of the pork due to its superior performance. ## 8 Conclusions

This study utilized smart sensing sensor tags based on RF energy harvesting, demonstrating an effective approach for monitoring meat freshness. The system is designed to be attached anywhere for easy transplantation, and the use of low-cost commercial sensors and miniaturization technology has increased its productivity and portability. The proposed smart sensor tag system aims to observe the gas and discoloration data produced by the decomposition of pork, and based on the measured data, its purpose is to classify the freshness level. We developed an RF energy harvesting-based monitoring system to monitor the storage condition of pork at room temperature during the humid and hot month of August, which is prone to frequent cases of food poisoning accidents, and other humid summer months. Pork stored in a smart retains oxygen in the oxy-myoglobin state, resulting in a bright red color. Even during refrigerated storage, myoglobin gradually changes to met-myoglobin, progressively turning brown.