Non-invasive prediction of sweet cherry soluble solids content using dielectric spectroscopy and down-sampling techniques
The soluble solid content (SSC) in fruits significantly influences consumers' taste, aroma, and flavor preferences. It also plays a crucial role for farmers and wholesalers in determining the optimal harvest period for marketing. Dielectric spectroscopy, an innovative and non-invasive technique...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525000164 |
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| author | Kamil Sacilik Necati Cetin Burak Ozbey Fernando Auat Cheein |
| author_facet | Kamil Sacilik Necati Cetin Burak Ozbey Fernando Auat Cheein |
| author_sort | Kamil Sacilik |
| collection | DOAJ |
| description | The soluble solid content (SSC) in fruits significantly influences consumers' taste, aroma, and flavor preferences. It also plays a crucial role for farmers and wholesalers in determining the optimal harvest period for marketing. Dielectric spectroscopy, an innovative and non-invasive technique, has shown promise for various applications in the food and agriculture sectors. This study introduces an open-ended coaxial line probe measurement system to non-invasively determine the SSC of sweet cherries at different radio and microwave frequencies. Key parameters such as the dielectric constant (ε′), loss factor (ε′′), loss tangent (tan δ), and SSC of sweet cherries were measured across different harvest periods. The dielectric property frequency ranges were down-sampled from 300 MHz to 15 MHz. Using dielectric spectroscopy, we implemented predictive models: support vector regression (SVR) and multilayer perceptron (MLP), that demonstrated extremely low MAE and RMSE, with correlation coefficients (R) exceeding 0.97 for SVR and 0.96 for MLP. The down-sampled frequency ranges for dielectric properties yielded consistently high performance across all subsets, demonstrating comparable results. These findings suggest that a dielectric measurement system designed for SSC estimation using fewer frequencies could effectively reduce costs while maintaining accuracy. |
| format | Article |
| id | doaj-art-638d62e174614761bdd80fa6be5e6465 |
| institution | DOAJ |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-638d62e174614761bdd80fa6be5e64652025-08-20T02:55:45ZengElsevierSmart Agricultural Technology2772-37552025-03-011010078210.1016/j.atech.2025.100782Non-invasive prediction of sweet cherry soluble solids content using dielectric spectroscopy and down-sampling techniquesKamil Sacilik0Necati Cetin1Burak Ozbey2Fernando Auat Cheein3Department of Agricultural Machinery and Technologies Engineering, Faculty of Agriculture, Ankara University, Ankara, TürkiyeDepartment of Agricultural Machinery and Technologies Engineering, Faculty of Agriculture, Ankara University, Ankara, Türkiye; Corresponding authors.Department of Electrical and Electronics Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, TürkiyeDepartment of Engineering, Harper-Adams University, Newport, United Kingdom; Corresponding authors.The soluble solid content (SSC) in fruits significantly influences consumers' taste, aroma, and flavor preferences. It also plays a crucial role for farmers and wholesalers in determining the optimal harvest period for marketing. Dielectric spectroscopy, an innovative and non-invasive technique, has shown promise for various applications in the food and agriculture sectors. This study introduces an open-ended coaxial line probe measurement system to non-invasively determine the SSC of sweet cherries at different radio and microwave frequencies. Key parameters such as the dielectric constant (ε′), loss factor (ε′′), loss tangent (tan δ), and SSC of sweet cherries were measured across different harvest periods. The dielectric property frequency ranges were down-sampled from 300 MHz to 15 MHz. Using dielectric spectroscopy, we implemented predictive models: support vector regression (SVR) and multilayer perceptron (MLP), that demonstrated extremely low MAE and RMSE, with correlation coefficients (R) exceeding 0.97 for SVR and 0.96 for MLP. The down-sampled frequency ranges for dielectric properties yielded consistently high performance across all subsets, demonstrating comparable results. These findings suggest that a dielectric measurement system designed for SSC estimation using fewer frequencies could effectively reduce costs while maintaining accuracy.http://www.sciencedirect.com/science/article/pii/S2772375525000164Sweet cherriesDown-samplingDielectric spectroscopySoluble solid contentMachine learning |
| spellingShingle | Kamil Sacilik Necati Cetin Burak Ozbey Fernando Auat Cheein Non-invasive prediction of sweet cherry soluble solids content using dielectric spectroscopy and down-sampling techniques Smart Agricultural Technology Sweet cherries Down-sampling Dielectric spectroscopy Soluble solid content Machine learning |
| title | Non-invasive prediction of sweet cherry soluble solids content using dielectric spectroscopy and down-sampling techniques |
| title_full | Non-invasive prediction of sweet cherry soluble solids content using dielectric spectroscopy and down-sampling techniques |
| title_fullStr | Non-invasive prediction of sweet cherry soluble solids content using dielectric spectroscopy and down-sampling techniques |
| title_full_unstemmed | Non-invasive prediction of sweet cherry soluble solids content using dielectric spectroscopy and down-sampling techniques |
| title_short | Non-invasive prediction of sweet cherry soluble solids content using dielectric spectroscopy and down-sampling techniques |
| title_sort | non invasive prediction of sweet cherry soluble solids content using dielectric spectroscopy and down sampling techniques |
| topic | Sweet cherries Down-sampling Dielectric spectroscopy Soluble solid content Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525000164 |
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