Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics
This study pioneered a non-destructive testing approach to evaluating the physicochemical properties of golden passion fruit by developing a platform to analyze the fruit’s electrical characteristics. By using dielectric properties, the method accurately predicted the soluble solids content (<i&g...
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/5/2200 |
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| author | Fan Lin Dengjie Chen Cheng Liu Jincheng He |
| author_facet | Fan Lin Dengjie Chen Cheng Liu Jincheng He |
| author_sort | Fan Lin |
| collection | DOAJ |
| description | This study pioneered a non-destructive testing approach to evaluating the physicochemical properties of golden passion fruit by developing a platform to analyze the fruit’s electrical characteristics. By using dielectric properties, the method accurately predicted the soluble solids content (<i>SSC</i>), <i>Acidity</i> and pulp percentage (<i>PP</i>) in passion fruit. The investigation entailed measuring the relative dielectric constant (ε′) and dielectric loss factor (ε″) for 192 samples across a spectrum of 34 frequencies from 0.05 to 100 kHz. The analysis revealed that with increasing frequency and fruit maturity, both ε′ and ε″ showed a declining trend. Moreover, there was a discernible correlation between the fruit’s physicochemical indicators and dielectric properties. In refining the dataset, 12 outliers were removed using the Local Outlier Factor (LOF) algorithm. The study employed various advanced feature extraction techniques, including Recursive Feature Elimination with Cross-Validation (RFECV), Permutation Importance based on Random Forest Regression (PI-RF), Permutation Importance based on Linear Regression (PI-LR) and Genetic Algorithm (GA). All the variables and the selected variables after screening were used as inputs to build Extreme Gradient Boosting (XGBoost) and Categorical Boosting (Cat-Boost) models to predict the <i>SSC</i>, <i>Acidity</i> and <i>PP</i> in passion fruit. The results indicate that the PI-RF-XGBoost model demonstrated superior performance in predicting both the <i>SSC</i> (R<sup>2</sup> = 0.9240, RMSE = 0.2595) and the <i>PP</i> (R<sup>2</sup> = 0.9092, RMSE = 0.0014) of passion fruit. Meanwhile, the GA-CatBoost model exhibited the best performance in predicting <i>Acidity</i> (R<sup>2</sup> = 0.9471, RMSE = 0.1237). In addition, for the well-performing algorithms, the selected features are mainly concentrated within the frequency range of 0.05–6 kHz, which is consistent with the frequency range highly correlated with the dielectric properties and quality indicators. It is feasible to predict the quality indicators of fruit by detecting their low-frequency dielectric properties. This research offers significant insights and a valuable reference for non-destructive testing methods in assessing the quality of golden passion fruit. |
| first_indexed | 2024-04-25T00:34:17Z |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-25T00:34:17Z |
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| spelling | doaj.art-5ec395193f834c7b9caf1c901a8771032024-03-12T16:40:28ZengMDPI AGApplied Sciences2076-34172024-03-01145220010.3390/app14052200Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric CharacteristicsFan Lin0Dengjie Chen1Cheng Liu2Jincheng He3College of Mechanical and Electrical Engineering, Fujian A&F University, Fuzhou 350001, ChinaCollege of Mechanical and Electrical Engineering, Fujian A&F University, Fuzhou 350001, ChinaCollege of Mechanical and Electrical Engineering, Fujian A&F University, Fuzhou 350001, ChinaCollege of Mechanical and Electrical Engineering, Fujian A&F University, Fuzhou 350001, ChinaThis study pioneered a non-destructive testing approach to evaluating the physicochemical properties of golden passion fruit by developing a platform to analyze the fruit’s electrical characteristics. By using dielectric properties, the method accurately predicted the soluble solids content (<i>SSC</i>), <i>Acidity</i> and pulp percentage (<i>PP</i>) in passion fruit. The investigation entailed measuring the relative dielectric constant (ε′) and dielectric loss factor (ε″) for 192 samples across a spectrum of 34 frequencies from 0.05 to 100 kHz. The analysis revealed that with increasing frequency and fruit maturity, both ε′ and ε″ showed a declining trend. Moreover, there was a discernible correlation between the fruit’s physicochemical indicators and dielectric properties. In refining the dataset, 12 outliers were removed using the Local Outlier Factor (LOF) algorithm. The study employed various advanced feature extraction techniques, including Recursive Feature Elimination with Cross-Validation (RFECV), Permutation Importance based on Random Forest Regression (PI-RF), Permutation Importance based on Linear Regression (PI-LR) and Genetic Algorithm (GA). All the variables and the selected variables after screening were used as inputs to build Extreme Gradient Boosting (XGBoost) and Categorical Boosting (Cat-Boost) models to predict the <i>SSC</i>, <i>Acidity</i> and <i>PP</i> in passion fruit. The results indicate that the PI-RF-XGBoost model demonstrated superior performance in predicting both the <i>SSC</i> (R<sup>2</sup> = 0.9240, RMSE = 0.2595) and the <i>PP</i> (R<sup>2</sup> = 0.9092, RMSE = 0.0014) of passion fruit. Meanwhile, the GA-CatBoost model exhibited the best performance in predicting <i>Acidity</i> (R<sup>2</sup> = 0.9471, RMSE = 0.1237). In addition, for the well-performing algorithms, the selected features are mainly concentrated within the frequency range of 0.05–6 kHz, which is consistent with the frequency range highly correlated with the dielectric properties and quality indicators. It is feasible to predict the quality indicators of fruit by detecting their low-frequency dielectric properties. This research offers significant insights and a valuable reference for non-destructive testing methods in assessing the quality of golden passion fruit.https://www.mdpi.com/2076-3417/14/5/2200golden passion fruitdielectric propertiesfeature screeningmodel establishmentnon-destructive testing |
| spellingShingle | Fan Lin Dengjie Chen Cheng Liu Jincheng He Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics Applied Sciences golden passion fruit dielectric properties feature screening model establishment non-destructive testing |
| title | Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics |
| title_full | Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics |
| title_fullStr | Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics |
| title_full_unstemmed | Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics |
| title_short | Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics |
| title_sort | non destructive detection of golden passion fruit quality based on dielectric characteristics |
| topic | golden passion fruit dielectric properties feature screening model establishment non-destructive testing |
| url | https://www.mdpi.com/2076-3417/14/5/2200 |
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