# Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics
## 4 Conclusions  
In this study, a detection platform was established using the LCR digital bridge tester, and the dielectric properties of 192 passion fruit samples were accurately measured at 34 different frequencies ranging from 0.05 to 100 kHz. After removing outliers and conducting feature selection, prediction models based on dielectric properties were established for quality indicators of passion fruit. The main conclusions are as follows:  
1. There are differences in the \(\epsilon^{\prime}\) and \(\epsilon^{\prime\prime}\) parameters of passion fruit with different maturities. Moreover, significant correlations exist between the physicochemical indicators of passion fruit and the \(\epsilon^{\prime}\) and \(\epsilon^{\prime\prime}\) parameters at frequencies ranging from 0.05 to 10 kHz. The correlation coefficients range between 0.6 and 0.85.
2. The Local Outlier Factor (LOF) algorithm is used to filter out 12 outlier samples. Recursive Feature Elimination with Cross-Validation (RFECV), Permutation Importance based on Random Forest Regression (PI-RF), Permutation Importance based on Linear  
Figure 6: Comparison between measured and predicted results of fruit physicochemical parameters: (a) _SSC_, (b) _Acidity_; (c) _PP_.  
Regression (PI-LR) and Genetic Algorithm (GA) are employed to select 68 dielectric parameter features. The number of variables has been reduced by 85.78%, 83.82%, 86.77% and 87.26%, respectively.
3. The XGBoost and CatBoost algorithms are used to establish a quality prediction model for passion fruit and achieved good detection results. The determination coefficients (R\({}^{2}\)) for predicting the physicochemical parameters (_SSC_, _Acidity_, and _PP_) are 0.9240, 0.9471 and 0.9092, respectively.  
The results of this study prove the effective application of the feature screening method and the XGBoost and CatBoost algorithms in the field of agricultural science and tech-nology, especially the prediction of passion fruit quality indicators based on dielectric properties. In future work, it is possible to predict the quality characteristics of postharvest fruit, such as maturity, moisture content, hardness and vitamin C content, based on their dielectric properties. The growth status of fruit can be estimated through physicochemical indicators. By applying appropriate postharvest treatments to fruit of different maturity, the quality of the fruit can be ensured during the shelf life, thus enhancing their economic value.  
However, the study still has the following limitations: the experimental samples were of a single variety and from a limited geographical area, the equipment cost was high and the operation was complex. In order to better apply this technology to agricultural production practice, future studies could conduct: experiments on fruit of multiple varieties and from different geographical locations to identify commonalities and differences among them; fixed-frequency dielectric detection sensors could also be used in the future to create a more convenient and efficient fruit quality detection device.  
F.L.: Conceptualization, Investigation, Methodology, Writing--original draft. D.C.: Investigation, Methodology, Writing--review & editing. C.L.: Investigation, Methodology. J.H.: Funding acquisition, Resources, Supervision, Writing--review & editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Fujian Province Agricultural Key Core Technology Research Project (KLY23416 KA).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.