Open Access
| Issue |
BIO Web Conf.
Volume 195, 2025
2025 9th International Conference on Biomedical Engineering and Bioinformatics (ICBEB 2025)
|
|
|---|---|---|
| Article Number | 02001 | |
| Number of page(s) | 10 | |
| Section | Biomedical Imaging, Modeling and Visualization Technologies | |
| DOI | https://doi.org/10.1051/bioconf/202519502001 | |
| Published online | 14 November 2025 | |
- M. M. Ali and N. Hashim, “Non-destructive methods for detection of food quality,” in Future Foods: Global Trends, Opportunities, and Sustainability Challenges, R. Bhat, Ed. (Academic Press, Cambridge, 2021), pp. 645–667. [Google Scholar]
- Food and Agriculture Organization of the United Nations, The State of Food Security and Nutrition in the World 2021 (FAO, Rome, 2021). [Google Scholar]
- M. Contreras, J. Benedito, and J. V. Garcia-Perez, “Ultrasonic characterization of salt, moisture and texture modifications in dry-cured ham during post-salting,” Meat Sci. 172, 108356 (2021). [Google Scholar]
- V. Sanchez-Jimenez, G. A. Collazos-Escobar, A. González-Mohino, T. E. Gomez Alvarez-Arenas, J. Benedito, and J. V. Garcia-Perez, “Non-invasive monitoring of potato drying by means of air-coupled ultrasound,” Food Control 148, 109653 (2023). [Google Scholar]
- M. Ghasemi-Varnamkhasti, N. Ghatreh-Samani, M. Naderi-Boldaji, M. Forina, and M. Bonyadian, “Development of two dielectric sensors coupled with computational techniques for detecting milk adulteration,” Comput. Electron. Agric. 140, 266–278 (2017). [Google Scholar]
- S. Kapse, P. Kedia, A. Kumar, S. Kausley, P. Pal, and B. Rai, “A non-invasive method for detection of freshness of packaged milk,” J. Food Eng. 346, 111424 (2023). [Google Scholar]
- G. Wang, S. Huang, H. He, J. Cheng, T. Zhang, Z. Fu, S. Zhang, Y. Zhou, H. Li, and X. Liu, “Fabrication of a ‘progress bar’ colorimetric strip sensor array by dye-mixing method as a potential food freshness indicator,” Food Chem. 373, 131434 (2022). [Google Scholar]
- S. Grimnes and Ø. Martinsen, Bioimpedance & Bioelectricity Basics, 3rd ed. (Academic Press, Oxford, 2015), ch. 1, pp. 1–7. [Google Scholar]
- P. D. da Silva and P. Bertemes-Filho, “Prototype analysis of a low-power, small-scale wearable medical device,” J. Electr. Bioimpedance 15, 169–176 (2025). [Google Scholar]
- S. Huh, H.-J. Kim, S. Lee, J. Cho, A. Jang, and J. Bae, “Utilization of electrical impedance spectroscopy and image classification for non-invasive early assessment of meat freshness,” Sensors 21, 1001 (2021). [Google Scholar]
- T. A. Minetto, B. D. França, G. S. Dariz, E. A. Veiga, A. C. Galvão, and W. S. Robazza, “Identifying adulteration of raw bovine milk with urea through electrochemical impedance spectroscopy coupled with chemometric techniques,” Food Chem. 385, 132678 (2022). [Google Scholar]
- K. Fontana, “Identificação de adulteração com óleo de soja e óleo de girassol no azeite de oliva extravirgem através da espectroscopia de impedância elétrica associada ao equilíbrio de fases,” M.S. thesis, Universidade do Estado de Santa Catarina, 2022. [Google Scholar]
- P. Ibba, C. Tronstad, R. Moscetti, T. Mimmo, G. Cantarella, L. Petti, Ø. Martinsen, S. Cesco, and P. Lugli, “Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data,” Sci. Rep. 10, (2020). [Google Scholar]
- A. Jiménez, M. Rufo, J. M. Paniagua, A. González-Mohino, and L. S. Olegario, “Authentication of pure and adulterated edible oils using non-destructive ultrasound,” Food Chem. 429, 136820 (2023). [Google Scholar]
- A. De Temmerman, M. De Ryck, T. Hellemans, and M. Verbeke, “Infrared hyperspectral analysis for non-invasive, inline fat content determination in bakery products,” in Proc. IEEE 21st Int. Conf. Ind. Informatics (INDIN), 2023, pp. 1–7. [Google Scholar]
- M. Contreras, J. Benedito, A. Quiles, J. M. Lorenzo, E. Fulladosa, P. Gou, and J. V. Garcia-Perez, “Assessing the textural defect of pastiness in dry-cured pork ham using chemical, microstructural, textural and ultrasonic analyses,” J. Food Eng. 265, 109690 (2020). [Google Scholar]
- R. Scapaticci, S. Zappia, I. Catapano, G. Ruello, G. Bellizzi, N. Pasquino, M. Cavagnaro, S. Pisa, E. Piuzzi, F. Frezza, F. Vipiana, J. A. Tobon Vasquez, M. Ricci, and L. Crocco, “Broadband electromagnetic sensing for food quality control: A preliminary experimental study,” in Proc. 15th Eur. Conf. Antennas Propag. (EuCAP), 2021, pp. 1–5. [Google Scholar]
- S. Riaz, P. Ibba, S. Nadja, A. Rasheed, P. Lugli, A. Zanella, and L. Petti, “Exploring the potential of electrical impedance spectroscopy for predicting internal browning in apples,” in Proc. IEEE Int. Workshop Metrology for Agriculture and Forestry (MetroAgriFor), 2023, pp. 414–418. [Google Scholar]
- M. Islam, K. Wahid, A. Dinh, and P. Bhowmik, “Model of dehydration and assessment of moisture content on onion using EIS,” J. Food Sci. Technol. 56, (2019). [Google Scholar]
- L. Feng, T. Hou, B. Wang, and B. Zhang, “Assessment of rice seed vigour using selected frequencies of electrical impedance spectroscopy,” Biosyst. Eng. 209, 53–63 (2021). [Google Scholar]
- D. Romero Fogué, R. Masot Peris, J. Ibáñez Civera, L. Contat Rodrigo, and N. Laguarda-Miro, “Monitoring freeze-damage in grapefruit by electric bioimpedance spectroscopy and electric equivalent models,” Horticulturae 8, 218 (2022). [Google Scholar]
- H. Arteaga, E. Robleto-Martinez, A. C. de Sousa Silva, S. Souto, J. Batista, and E. J. X. Costa, “Postharvest freezing process assessment of the blueberry structure in three acts: Bioimpedance, color, and granulometry analysis,” LWT 151, 112237 (2021). [Google Scholar]
- L. Feng, T. Hou, and B. Zhang, “A noninvasive method for detecting frozen injuries in potatoes based on electrical impedance spectroscopy,” J. Food Sci. Technol. 44, e13682 (2021). [Google Scholar]
- C. Allará, R. Moscetti, G. Bedini, M. Ciocca, A. Benelli, P. Lugli, L. Petti, and P. Ibba, “Bioimpedance-based prediction of dry matter content and potato varieties through supervised machine learning methods,” Postharvest Biol. Technol. 222, 113358 (2025). [Google Scholar]
- Y. Zhang, Y. Chen, Y. Bao, X. Wang, and J. Xian, “Tomato maturity detection based on bioelectrical impedance spectroscopy,” Comput. Electron. Agric. 227, 109553 (2024). [Google Scholar]
- Y. Zhang, Y. Chen, Z. Chang, J. Zhao, X. Wang, and J. Xian, “Detection of localized damage in tomato based on bioelectrical impedance spectroscopy,” Agronomy 14, 1822 (2024). [Google Scholar]
- Y. Leng, C. Zhang, Y. Gao, and X. Wang, “Bio-impedance measurements for meat quality determination of pork loins under repeated freeze-thaw treatments,” J. Food Compos. Anal. 125, 105779 (2024). [Google Scholar]
- B. Liang, C. Wei, X. Li, Z. Zhang, and X. Huang, “Incorporating bioimpedance technique with ensemble learning algorithm for mutton tenderness detection,” Food Bioprocess Technol. 16, (2023). [Google Scholar]
- J. Zuo, J. Liang, S. Cheng, Y. Deng, Z. Li, Q. Nie, D. Zhang, X. Zhang, Z. Li, and H. Li, “Live chicken body fat measurement technology based on bio-electrical impedance,” Comput. Electron. Agric. 220, 108890 (2024). [Google Scholar]
- I. Zabala, S. Merino, U. Eletxigerra, J. Ramiro, M. Burguera, and E. Aranzabe, “Detection of salt content in canned tuna by impedance spectroscopy: A feasibility study for distinguishing salt levels,” Foods 13, 1765 (2024). [Google Scholar]
- V. Kerzérho, F. Azaïs, S. Bernard, S. Bonhommeau, B. Brisset, L. De Knyff, M. Julien, M. Renovell, T. Rouyer, C. Saraux, and F. Soulier, “Multilinear regression analysis between local bioimpedance spectroscopy and fish morphological parameters,” Fishes 8, 88 (2023). [Google Scholar]
- J. Colman, S. Zimmerman, R. H. Hoenig, M. K. Cox, and J. D. Stieglitz, “Relationship of bioelectrical impedance to organoleptic sensory data of farm-raised olive flounder, Paralichthys olivaceus,” Aquac. Int. 33, 178 (2025). [Google Scholar]
- J. Slay, R. Sotner, T. J. Freeborn, J. Jerabek, L. Polak, J. Petrzela, and V. Vyplel, “Distinguishing liquid solutions with alcohol using electrical impedance measurements: Preliminary study for food safety applications,” IEEE Sens. J. 23, 26997–27007 (2023). [Google Scholar]
- A. C. F. de Oliveira Meira, L. C. de Morais, M. M. de Oliveira Paula, S. M. Pinto, and J. V. de Resende, “Application of electrical impedance spectroscopy for the characterisation of yoghurts,” Int. Dairy J. 141, 105625 (2023). [Google Scholar]
- D. Gomes, S. Magalhães, M. G. Rasteiro, and P. Faia, “Measuring microplastic concentrations in water by electrical impedance spectroscopy,” Water 16, 3228 (2024). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

