# Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics
## 3 Results and Discussion
### Establishment of Prediction Models and Comparison of Results  
Using the full variables (FVs) and variables selected by the RFECV, PI-RF, PI-LR and GA algorithms as inputs, and using passion fruit _SSC_, _Acidity_ and _PP_ as outputs, XGBoost prediction models (FV-XGBoost, RFECV-XGBoost, PI-RF-XGBoost, PI-LR-XGBoost, GA-XGBoost) and CatBoost prediction models (FV-CatBoost, RFECV-CatBoost, PI-RF-CatBoost, PI-LR-CatBoost, GA-CatBoost) were established. The model parameters were set as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Feature Filtering Methods**} & \multicolumn{1}{c}{**Physicochemical Indicators**} & \multicolumn{1}{c}{**Number of Features**} & \multicolumn{5}{c}{**Features after Filtering**} \\ \hline  & \multirow{3}{*}{_SSC_} & 10 & \(25\varepsilon^{\prime}\) & \(30\varepsilon^{\prime}\) & \(60\varepsilon^{\prime}\) & \(100\varepsilon^{\prime}\) & \(20\varepsilon^{\prime\prime}\) & \(25\varepsilon^{\prime\prime}\) & \(50\varepsilon^{\prime\prime}\) \\  & & & \(60\varepsilon^{\prime\prime}\) & \(75\varepsilon^{\prime\prime}\) & \(100\varepsilon^{\prime\prime}\) & & & \\ RFECV & _Acidity_ & 5 & \(30\varepsilon^{\prime}\) & \(20\varepsilon^{\prime\prime}\) & \(25\varepsilon^{\prime\prime}\) & \(30\varepsilon^{\prime\prime}\) & \(40\varepsilon^{\prime\prime}\) & \(50\varepsilon^{\prime\prime}\) & \\  & & \(10\varepsilon^{\prime}\) & \(12\varepsilon^{\prime}\) & \(15\varepsilon^{\prime}\) & \(20\varepsilon^{\prime}\) & \(30\varepsilon^{\prime}\) & \(40\varepsilon^{\prime}\The model results are shown in Table 3. Overall, the prediction performance of the models after feature selection is not significantly different from the full variable regression model. Among them, the PI-RF-XGBoost model demonstrates good performance in predicting \(SSC\), achieving a determination coefficient (R2p) of 0.9240 and a root mean square error (RMSEp) of 0.2595 on the prediction set. For predicting _Acidity_, the GA-CatBoost model performs well with an R2p of 0.9471 and an RMSEp of 0.1237. Similarly, the PI-RF-XGBoost model shows good performance in predicting \(PP\), with an R2p of 0.9092 and an RMSEp of 0.0014. Figure 6 shows the prediction results of the three models. It can be clearly seen that the samples are concentrated near the y = x regression line, and the prediction results are better. Furthermore, for the algorithms that performed well, their input features are mainly concentrated in the frequency range of 0.05-6 kHz, which is consistent with the previous results. From the perspective of the complexity of the regression models, the RFECV, PI-RF, PI-LR and GA algorithms have significantly simplified the computational complexity of the models. The number of variables has been reduced by 85.78%, 83.82%, 86.77% and 87.26%, respectively. Moreover, by pruning the models and reducing the number of iterations, the model size has been further reduced. The results indicate that the PI-RF-XGBoost, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Modeling Method** & **Model Parameters** & **Set Value** \\ \hline \multirow{8}{*}{XGBoost} & n\_estimators & 50 \\  & learning\_rate & 0.1 \\  & eval\_metric & ‘rmse’ \\  & max\_depth & 10 \\  & objective & ‘reg:squarederror’ \\  & booster & ‘gbtree’ \\ \hline \multirow{8}{*}{CatBoost} & iterations & 1000 \\  & learning\_rate & 0.01 \\  & eval\_metric & ‘RMSE’ \\  & loss\_function & ‘RMSE’ \\  & depth & 4 \\  & od\_type & ‘Iter’ \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model parameter settings.