Quartz Crystal Microbalance (QCM) Sensor Array with Varying PMMA Coatings for Coffee Roasting Aroma Monitoring

Authors

Adharul Muttaqin
Electrical Engineering, Universitas Brawijaya
Setyawan Purnomo Sakti
Agus Naba
Panca Mudjirahardjo
DOI: https://doi.org/10.52549/ijeei.v14i1.7760
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Published: 23-03-2026 • Issue: Vol. 14 No. 1: March 2026 • Section: Regular paper

Abstract

This study investigates how polymethyl methacrylate (PMMA) coating concentration (3–15 wt%) tunes the response of an eight-channel Quartz Crystal Microbalance (QCM) sensor array to real coffee roasting volatiles at 200–240 °C. One channel was left uncoated as a reference, while seven channels were coated with different PMMA concentrations to introduce controlled response diversity. Baseline-corrected frequency shifts were processed into qualitative features describing response magnitude, kinetics, and early recovery, and principal component analysis (PCA) was used to visualize multichannel pattern structure across repeated roasts. Consistent temperature-dependent response patterns were observed, while run-to-run variability increased at higher temperatures. The first two principal components captured ~75% of the total variance (PC1 dominated by integrated response magnitude and PC2 reflecting kinetic variability). Because chamber humidity increased during roasting, a supplementary robustness check was performed using recorded RH; temperature-dependent structure remained after accounting for humidity effects. Overall, discrimination arises from the collective multichannel response, suggesting potential applicability of PMMA-coated QCM arrays for qualitative coffee roasting monitoring, pending further validation with larger datasets and complementary analytical methods.
Keywords: Quartz Crystal Microbalance; Aroma Monitoring; PMMA Coating; Qualitative Monitoring; Sensor Array

References

L. V. Bork, T. Stobernack, S. Rohn, and C. Kanzler, “Browning reactions of hydroxycinnamic acids and heterocyclic Maillard reaction intermediates – Formation of phenol-containing colorants,†Food Chemistry, vol. 449, p. 139189, Aug. 2024, doi: 10.1016/j.foodchem.2024.139189.

H. Ching Lik and B. Flávio Meira, Drying and Roasting of Cocoa and Coffee, 1st ed. Boca Raton, Florida : CRC Press, [2020] | Series: Advances in drying science and technology: CRC Press, 2019. doi: 10.1201/9781315113104.

L. Poisson, I. Blank, A. Dunkel, and T. Hofmann, “The Chemistry of Roasting—Decoding Flavor Formation,†in The Craft and Science of Coffee, Elsevier, 2017, pp. 273–309. doi: 10.1016/B978-0-12-803520-7.00012-8.

M. Giordano, M. Bertolino, S. Belviso, D. Ghirardello, and G. Zeppa, “Effects of Species, Post-Harvest Treatment, and Roasting on Fibre, Volatile Compounds, and Polyphenol Contents in Coffee Silverskin,†Foods, vol. 11, no. 19, p. 3132, Oct. 2022, doi: 10.3390/foods11193132.

P. Zakidou, F. Plati, A. Matsakidou, E.-M. Varka, G. Blekas, and A. Paraskevopoulou, “Single Origin Coffee Aroma: From Optimized Flavor Protocols and Coffee Customization to Instrumental Volatile Characterization and Chemometrics,†Molecules, vol. 26, no. 15, p. 4609, July 2021, doi: 10.3390/molecules26154609.

J. Zapata, V. Londoño, M. Naranjo, J. Osorio, C. Lopez, and M. Quintero, “Characterization of aroma compounds present in an industrial recovery concentrate of coffee flavour,†CyTA - Journal of Food, vol. 16, no. 1, pp. 367–372, Jan. 2018, doi: 10.1080/19476337.2017.1406995.

T. Dippong, M. Dan, M. H. Kovacs, E. D. Kovacs, E. A. Levei, and O. Cadar, “Analysis of Volatile Compounds, Composition, and Thermal Behavior of Coffee Beans According to Variety and Roasting Intensity,†Foods, vol. 11, no. 19, p. 3146, Oct. 2022, doi: 10.3390/foods11193146.

I. Colzi, E. Marone, S. Magnelli, S. Mancuso, E. Azzarello, and C. Taiti, “Kinetics of volatile organic compounds (VOCs) released by roasted Coffee during the first ten days after processing,†Advances in Horticultural Science, pp. 145-150 Pages, Apr. 2019, doi: 10.13128/AHS-24881.

M. Honda, D. Takezaki, M. Tanaka, M. Fukaya, and M. Goto, “Effect of Roasting Degree on Major Coffee Compounds: A Comparative Study between Coffee Beans with and without Supercritical CO2 Decaffeination Treatment,†J. Oleo Sci., vol. 71, no. 10, pp. 1541–1550, 2022, doi: 10.5650/jos.ess22194.

M. Münchow, J. Alstrup, I. Steen, and D. Giacalone, “Roasting Conditions and Coffee Flavor: A Multi-Study Empirical Investigation,†Beverages, vol. 6, no. 2, p. 29, May 2020, doi: 10.3390/beverages6020029.

J. E. Istiyanto et al., “Trainer Kit for Aroma Classification Using Artificial Intelligence,†IJEEI, vol. 13, no. 2, pp. 294–308, Apr. 2025, doi: 10.52549/ijeei.v13i2.6146.

L. Sipos, Ã. Nyitrai, G. Hitka, L. F. Friedrich, and Z. Kókai, “Sensory Panel Performance Evaluation— Comprehensive Review of Practical Approaches,†Applied Sciences, vol. 11, no. 24, p. 11977, Dec. 2021, doi: 10.3390/app112411977.

H. Czech, J. Heide, S. Ehlert, T. Koziorowski, and R. Zimmermann, “Smart Online Coffee Roasting Process Control: Modelling Coffee Roast Degree and Brew Antioxidant Capacity for Real-Time Prediction by Resonance-Enhanced Multi-Photon Ionization Mass Spectrometric (REMPI-TOFMS) Monitoring of Roast Gases,†Foods, vol. 9, no. 5, p. 627, May 2020, doi: 10.3390/foods9050627.

S. Grassi et al., “Monitoring Chemical Changes of Coffee Beans During Roasting Using Real-time NIR Spectroscopy and Chemometrics,†Food Anal. Methods, vol. 16, no. 5, pp. 947–960, May 2023, doi: 10.1007/s12161-023-02473- w.

T. A. Catelani, J. R. Santos, R. N. M. J. Páscoa, L. Pezza, H. R. Pezza, and J. A. Lopes, “Real-time monitoring of a coffee roasting process with near infrared spectroscopy using multivariate statistical analysis: A feasibility study,†Talanta, vol. 179, pp. 292–299, Mar. 2018, doi: 10.1016/j.talanta.2017.11.010.

M. Wang and Y. Chen, “Electronic nose and its application in the food industry: a review,†Eur. Food Res. Technol., vol. 250, pp. 21–67, 2024, doi: 10.1007/s00217-023-04381-z.

L. Lu, Z. Hu, X. Hu, D. Li, and S. Tian, “Electronic tongue and electronic nose for food quality and safety,†Food Res. Int., vol. 162, Part B, p. 112214, Dec. 2022, doi: 10.1016/j.foodres.2022.112214.

H. Anwar, T. Anwar, and S. Murtaza, “Review on food quality assessment using machine learning and electronic nose system,†Biosens. Bioelectron.: X, vol. 14, p. 100365, Sep. 2023, doi: 10.1016/j.biosx.2023.100365.

R. L. Pérez, C. E. Ayala, J.-Y. Park, J.-W. Choi, and I. M. Warner, “Coating-Based Quartz Crystal Microbalance Detection Methods of Environmentally Relevant Volatile Organic Compounds,†Chemosensors, vol. 9, no. 7, p. 153, June 2021, doi: 10.3390/chemosensors9070153.

M. Rodríguez-Torres, V. Altuzar, C. Mendoza-Barrera, G. Beltrán-Pérez, J. Castillo-Mixcóatl, and S. MuñozAguirre, “Discrimination Improvement of a Gas Sensors’ Array Using High-Frequency Quartz Crystal Microbalance Coated with Polymeric Films,†Sensors, vol. 20, no. 23, p. 6972, Dec. 2020, doi: 10.3390/s20236972.

S. P. Sakti, T. Putro, A. Sabarudin, A. Naba, and T. N. Zafirah, “QCM sensor with polystyrene coating as a candidate for coffee aroma sensor,†presented at the THE 10TH INTERNATIONAL BASIC SCIENCE INTERNATIONAL CONFERENCE (BASIC) 2022, Malang, Indonesia, 2023, p. 040005. doi: 10.1063/5.0166856.

F. Chrismiadi Nalle, A. Sabarudin, A. I. Saputra, A. Romdhoni, N. F. Khusnah, and S. P. Sakti, “Roasted-Coffee Aroma Sensing by Using Polymethyl Methacrylate (PMMA) and Polymethyl Methacrylate-Reduced Graphene Oxide (PMMA-rGO) Composite-based Quartz Crystal Microbalance (QCM) Sensor,†Trends Sci, vol. 22, no. 3, p. 9249, Jan. 2025, doi: 10.48048/tis.2025.9249.

G. Sauerbrey, “Verwendung von Schwingquarzen zur Wägung dünner Schichten und zur Mikrowägung,†Z. Physik, vol. 155, no. 2, pp. 206–222, Apr. 1959, doi: 10.1007/BF01337937.

F. Tan and X.-H. Huang, “Relations between Mass Change and Frequency Shift of a QCM Sensor in Contact with Viscoelastic Medium,†Chinese Phys. Lett., vol. 30, no. 5, p. 050701, May 2013, doi: 10.1088/0256- 307X/30/5/050701.

O. C. Lezzar, A. Bellel, M. Boutamine, S. Sahli, Y. Segui, and P. Raynaud, “Thin Film Coated QCM-Sensors and Pattern Recognition Methods for Discrimination of VOCs,†International Journal on Smart Sensing and Intelligent Systems, vol. 7, no. 5, pp. 1–6, Jan. 2014, doi: 10.21307/ijssis-2019-015.

T. Julian, S. N. Hidayat, A. Rianjanu, A. B. Dharmawan, H. S. Wasisto, and K. Triyana, “Intelligent Mobile Electronic Nose System Comprising a Hybrid Polymer-Functionalized Quartz Crystal Microbalance Sensor Array,†ACS Omega, vol. 5, no. 45, pp. 29492–29503, Nov. 2020, doi: 10.1021/acsomega.0c04433.

Zulfikar et al., “Development array sensor for detecting Robusta coffee aroma,†presented at the II INTERNATIONAL SCIENTIFIC AND PRACTICAL SYMPOSIUM “MATERIALS SCIENCE AND TECHNOLOGY†(MST-II-2022), Dushanbe, Republic of Tajikistan, 2023, p. 020003. doi: 10.1063/5.0132947.

T. N. Zafirah, "Polistiren (PS), Poly(methyl methacrylate) (PMMA), dan Styrene-block Butadiene-block Styrene (SBS) sebagai Lapisan Fungsional Sensor VOC Berbasis Quartz Crystal Microbalance (QCM) dan Pengamatan Efek Pembengkakan (Swelling Effect) Menggunakan Analisis Impedansi," Ph.D. dissertation, Dept. Physics, Universitas Brawijaya, Malang, Indonesia, 2024.

How to Cite

[1]
A. Muttaqin, S. P. Sakti, A. Naba, and P. Mudjirahardjo, “Quartz Crystal Microbalance (QCM) Sensor Array with Varying PMMA Coatings for Coffee Roasting Aroma Monitoring”, IJEEI, vol. 14, no. 1, pp. 153–166, Mar. 2026, doi: 10.52549/ijeei.v14i1.7760.