Optimizing Formalin Detection in Fish Using QCM Sensors with TOMAC Membrane Coatings for Product Quality Monitoring

Muthmainnah Muthmainnah, Khoirul Aini, Imam Tazi, Ninik Chamidah, Kusairi Kusairi

Abstract

Detection of formalin in fishery products is a significant concern in the food industry to ensure consumer safety. This study compared the performance of Quartz Crystal Microbalance (QCM) sensors without a membrane and with a Trioctyl methyl ammonium Chloride (TOMAC) membrane coating in detecting formalin in fish samples. The research findings indicate that QCM without a membrane for formalin samples has a lower detection limit of 150 ppm and an upper detection limit of 350 ppm with a sensitivity of 2194.171 Hz/ppm. On the other hand, QCM with a TOMAC membrane coating has a lower detection limit of 400 ppm and an upper detection limit of 550 ppm with a sensitivity of 842.7551 Hz/ppm. Meanwhile, QCM without a membrane for formalin in fish samples has a lower detection limit of 450 ppm and an upper detection limit of 650 ppm with a sensitivity of 15386.38 Hz/ppm. At the same time, QCM with a TOMAC membrane coating for formalin in fish samples has a lower detection limit of 350 ppm and an upper detection limit of 500 ppm with a sensitivity of 23108.9 Hz/ppm. Response time analysis shows that both sensors reach a steady state condition after 12 seconds. This study highlights the importance of selecting appropriate sensors for detecting formalin in fishery products, considering detection limits, sensitivity, and response time as crucial criteria. Thus, these findings can guide the fisheries industry in choosing effective and accurate formalin detection technology.

Keywords

detection limit; lipid; Quartz Crystal; response time; sensitivity

Full Text:

PDF

References

Y. Alfiko, D. Xie, R. T. Astuti, J. Wong, and L. Wang, “Insects as a feed ingredient for fish culture: Status and trends,” Aquac. Fish., vol. 7, no. 2, pp. 166–178, 2022, doi: 10.1016/j.aaf.2021.10.004. 2. Ò. Boronat et al., “Development of added-value culinary ingredients from fish waste: Fish bones and fish scales,” Int. J. Gastron. Food Sci., vol. 31, no. October 2022, pp. 0–6, 2023, doi: 10.1016/j.ijgfs.2022.100657. 3. M. Muthmainnah, M. F. Nashirudin, W. Sasmitaninghidayah, N. Chamidah, A. Mulyono, and I. Tazi, “THE DEVELOPMENT OF AN IOT-BASED AUTOMATED TEMPERATURE AND PH MONITORING SYSTEM TO ENHANCE THE MANAGEMENT OF GOURAMI FISH PONDS,” ARPN J. Eng. Appl. Sci., vol. 19, no. 5, pp. 294–300, 2024, doi: https://doi.org/10.59018/032443. 4. M. K. Alsmadi and I. Almarashdeh, “A survey on fish classification techniques,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 5, pp. 1625–1638, 2022, doi: 10.1016/j.jksuci.2020.07.005. 5. V. Kandimalla, M. Richard, F. Smith, J. Quirion, L. Torgo, and C. Whidden, “Automated Detection, Classification and Counting of Fish in Fish Passages With Deep Learning,” Front. Mar. Sci., vol. 8, no. January, pp. 1–15, 2022, doi: 10.3389/fmars.2021.823173. 6. M. Nurilmala, H. Suryamarevita, H. Husein Hizbullah, A. M. Jacoeb, and Y. Ochiai, “Fish skin as a biomaterial for halal collagen and gelatin,” Saudi J. Biol. Sci., vol. 29, no. 2, pp. 1100–1110, 2022, doi: 10.1016/j.sjbs.2021.09.056. 7. F. Fadlisyah and M. Muhathir, “Performance Evaluation Of Variations Boosting Algorithms For Classifying Formalin Fish From Photos,” J. Informatics Telecommun. Eng., vol. 6, no. 2, pp. 621–629, 2023, doi: 10.31289/jite.v6i2.6614. 8. A. Sabilah et al., “The Effectiveness of Using Direct Sunlight on the Drying Process of Salted Fish Without Formalin,” Int. J. Nat. Sci. Eng., vol. 6, no. 1, pp. 14–20, 2022, doi: 10.23887/ijnse.v6i1.41471. 9. Misbah, M. Rivai, and F. Kurniawan, “Diabetes Detection Using Carbon Nanomaterial Coated QCM Gas Sensors and a Convolutional Neural Network through Urine Sample,” Int. J. Intell. Eng. Syst., vol. 16, no. 5, pp. 417–427, 2023, doi: 10.22266/ijies2023.1031.36. 10. P. Hampitak et al., “Protein interactions and conformations on graphene-based materials mapped using quartz-crystal microbalance with dissipation monitoring (QCM-D),” Carbon N. Y., vol. 165, pp. 317–327, 2020, doi: 10.1016/j.carbon.2020.04.093. 11. G. Rudolph-Schöpping, H. Schagerlöf, A. S. Jönsson, and F. Lipnizki, “Comparison of membrane fouling during ultrafiltration with adsorption studied by quartz crystal microbalance with dissipation monitoring (QCM-D),” J. Memb. Sci., vol. 672, no. November 2022, pp. 0–7, 2023, doi: 10.1016/j.memsci.2022.121313. 12. A. Budianto, A. Y. P. Wardoyo, Masruroh, and H. A. Dharmawan, “An Airborne Fungal Spore Mass Measurement System Based on Graphene Oxide Coated QCM,” Polish J. Environ. Stud., vol. 31, no. 4, pp. 3523–3529, 2022, doi: 10.15244/pjoes/147057. 13. D. Hao, C. Hu, J. Grant, A. Glidle, and D. R. S. Cumming, “Hybrid localized surface plasmon resonance and quartz crystal microbalance sensor for label free biosensing,” Biosens. Bioelectron., vol. 100, no. August 2017, pp. 23–27, 2018, doi: 10.1016/j.bios.2017.08.038. 14. J. Hu, X. Huang, S. Xue, G. Yesilbas, A. Knoll, and O. Schneider, “Measurement of the mass sensitivity of QCM with ring electrodes using electrodeposition,” Electrochem. commun., vol. 116, no. May, p. 106744, 2020, doi: 10.1016/j.elecom.2020.106744. 15. N. Liu et al., “Flexible hydrogel non-enzymatic QCM sensor for continuous glucose monitoring,” Biosens. Bioelectron. X, vol. 10, no. January, 2022, doi: 10.1016/j.biosx.2022.100110. 16. N. P. Putri, E. Suaebah, L. Rohmawati, D. J. D. Herry Santjojo, Masruroh, and S. P. Sakti, “Implications of the Electrodeposition Scan Rate on the Morphology of Polyaniline Layer and the Impedance of a QCM Sensor,” Trends Sci., vol. 20, no. 3, 2023, doi: 10.48048/tis.2023.6411. 17. T. Wasilewski, B. Szulczyński, D. Dobrzyniewski, W. Jakubaszek, J. Gębicki, and W. Kamysz, “Development and Assessment of Regeneration Methods for Peptide-Based QCM Biosensors in VOCs Analysis Applications,” Biosensors, vol. 12, no. 5, pp. 1–14, 2022, doi: 10.3390/bios12050309. 18. L. Feng, L. Feng, Q. Li, J. Cui, and J. Guo, “Sensitive Formaldehyde Detection with QCM Sensor Based on PAAm/MWCNTs and PVAm/MWCNTs†,” Acs Omega, vol. 6, no. 22, pp. 14004–14014, 2021, doi: https://doi.org/10.1021/acsomega.0c05987. 19. N. Horzum, D. Tascioglu, C. Ozbek, S. Okur, and M. M. Demir, “VOC sensors based on a metal oxide nanofibrous membrane/QCM system prepared by electrospinning,” New J. Chem., vol. 38, no. 12, pp. 5761–5768, 2014, doi: 10.1039/c4nj00884g. 20. Y. Yao, X. Huang, Q. Chen, Z. Zhang, and W. Ling, “High Sensitivity and High Stability QCM Humidity Sensors Based on Polydopamine Coated Cellulose Nanocrystals/Graphene Oxide Nanocomposite,” Nanomaterials, vol. 10, no. 11, pp. 1–12, 2020, doi: 10.3390/nano10112210. 21. N. Misawa et al., “Construction of a Biohybrid Odorant Sensor Using Biological Olfactory Receptors Embedded into Bilayer Lipid Membrane on a Chip,” Acs Sensors, vol. 4, no. 3, pp. 711–716, 2019, doi: 10.1021/acssensors.8b01615. 22. M. F. Adak, P. Lieberzeit, P. Jarujamrus, and N. Yumusak, “Classification of alcohols obtained by QCM sensors with different characteristics using ABC based neural network,” Eng. Sci. Technol. an Int. J., vol. 23, no. 3, pp. 463–469, 2020, doi: 10.1016/j.jestch.2019.06.011. 23. J. Y. Park, R. L. Pérez, C. E. Ayala, S. R. Vaughan, I. M. Warner, and J. W. Choi, “A Miniaturized Quartz Crystal Microbalance (QCM) Measurement Instrument Based on a Phase-Locked Loop Circuit,” Electron., vol. 11, no. 3, 2022, doi: 10.3390/electronics11030358. 24. T. M. Clausen et al., “Real-time and label free determination of ligand binding-kinetics to primary cancer tissue specimens; a novel tool for the assessment of biomarker targeting,” Sens. Bio-Sensing Res., vol. 9, pp. 23–30, 2016, doi: 10.1016/j.sbsr.2016.05.003. 25. W. A. A. Sudjarwo, M. T. Dobler, and P. A. Lieberzeit, “QCM-based assay designs for human serum albumin,” Anal. Bioanal. Chem., vol. 414, no. 1, pp. 731–741, 2022, doi: 10.1007/s00216-021-03771-0. 26. C. Tonda-Turo, I. Carmagnola, and G. Ciardelli, “Quartz crystal microbalance with dissipation monitoring: A powerful method to predict the in vivo behavior of bioengineered surfaces,” Front. Bioeng. Biotechnol., vol. 6, no. OCT, pp. 1–7, 2018, doi: 10.3389/fbioe.2018.00158. 27. M. Yılmaz, M. Bakhshpour, I. Göktürk, A. K. Pişkin, and A. Denizli, “Quartz crystal microbalance (Qcm) based biosensor functionalized by her2/neu antibody for breast cancer cell detection,” Chemosensors, vol. 9, no. 4, 2021, doi: 10.3390/chemosensors9040080. 28. L. Eddaif, A. Shaban, and J. Telegdi, “Sensitive detection of heavy metals ions based on the calixarene derivatives-modified piezoelectric resonators: a review,” Int. J. Environ. Anal. Chem., vol. 99, no. 9, pp. 824–853, 2019, doi: 10.1080/03067319.2019.1616708. 29. J. Hu, S. Xue, O. Schneider, G. Yesilbas, A. Knoll, and X. Huang, “Comparison of the absolute mass sensitivity of ring electrode QCM and standard QCM using electrodeposition,” Electrochem. commun., vol. 119, no. August, p. 106826, 2020, doi: 10.1016/j.elecom.2020.106826. 30. Q. Dou et al., “Ultrasensitive poly(boric acid) hydrogel-coated quartz crystal microbalance sensor by using uv pressing-assisted polymerization for saliva glucose monitoring,” ACS Appl. Mater. Interfaces, vol. 12, no. 30, pp. 34190–34197, 2020, doi: https://doi.org/10.1021/acsami.0c08229. 31. N. Liu et al., “Flexible hydrogel non-enzymatic QCM sensor for continuous glucose monitoring,” Biosens. Bioelectron. X, vol. 10, no. January, p. 100110, 2022, doi: 10.1016/j.biosx.2022.100110. 32. Y. Gao et al., “Hydrophobic deep eutectic solvent-based ionic conductive gels with highly stretchable, fatigue-resistant and adhesive performances for reliable flexible strain sensors,” J. Appl. Polym. Sci., vol. 140, no. 2, p. e53285, 2022, doi: 10.1002/app.53285. 33. A. Hernández-Fernández et al., “Polymeric Inclusion Membranes Based on Ionic Liquids for Selective Separation of Metal Ions,” Membranes (Basel)., vol. 13, no. 795, pp. 1–18, 2023, doi: 10.3390/membranes13090795.

DOI: https://doi.org/10.13057/ijap.v14i2.92912

Refbacks

  • There are currently no refbacks.