| Title: | Intelligent Mobile Electronic Nose System Comprising a Hybrid Polymer-Functionalized Quartz Crystal Microbalance Sensor Array |
| Author(s): | Julian T; Hidayat SN; Rianjanu A; Dharmawan AB; Wasisto HS; Triyana K; |
| Address: | "Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara, PO Box BLS 21, Yogyakarta 55281, Indonesia. PT. Nanosense Instrument Indonesia, Umbulharjo, Yogyakarta 55167, Indonesia. Department of Materials Engineering, Institut Teknologi Sumatera, Terusan Ryacudu, Way Hui, Jati Agung, Lampung 35365, Indonesia. Research and Innovation Center for Advanced Materials, Institut Teknologi Sumatera, Terusan Ryacudu, Way Hui, Jati Agung, Lampung 35365, Indonesia. Institute of Semiconductor Technology (IHT), Technische Universitat Braunschweig, Hans-Sommer-Strasse 66, Braunschweig 38106, Germany. Laboratory for Emerging Nanometrology (LENA), Technische Universitat Braunschweig, Langer Kamp 6, Braunschweig 38106, Germany. Faculty of Information Technology, Universitas Tarumanagara, Jl. Letjen S. Parman No. 1, Jakarta 11440, Indonesia. Institute of Halal Industry and System (IHIS), Universitas Gadjah Mada, Sekip Utara, Yogyakarta 55281, Indonesia" |
| ISSN/ISBN: | 2470-1343 (Electronic) 2470-1343 (Linking) |
| Abstract: | "We devised a low-cost mobile electronic nose (e-nose) system using a quartz crystal microbalance (QCM) sensor array functionalized with various polymer-based thin active films (i.e., polyacrylonitrile, poly(vinylidene fluoride), poly(vinyl pyrrolidone), and poly(vinyl acetate)). It works based on the gravimetric detection principle, where the additional mass of the adsorbed molecules on the polymer surface can induce QCM resonance frequency shifts. To collect and process the obtained sensing data sets, a multichannel data acquisition (DAQ) circuitry was developed and calibrated using a function generator resulting in a device frequency resolution of 0.5 Hz. Four prepared QCM sensors demonstrated various sensitivity levels with high reproducibility and consistency under exposure to seven different volatile organic compounds (VOCs). Moreover, two types of machine learning algorithms (i.e., linear discriminant analysis and support vector machine models) were employed to differentiate and classify those tested analytes, in which classification accuracies of up to 98 and 99% could be obtained, respectively. This high-performance e-nose system is expected to be used as a versatile sensing platform for performing reliable qualitative and quantitative analyses in complex gaseous mixtures containing numerous VOCs for early disease diagnosis and environmental quality monitoring" |
| Notes: | "PubMed-not-MEDLINEJulian, Trisna Hidayat, Shidiq Nur Rianjanu, Aditya Dharmawan, Agus Budi Wasisto, Hutomo Suryo Triyana, Kuwat eng 2020/11/24 ACS Omega. 2020 Nov 4; 5(45):29492-29503. doi: 10.1021/acsomega.0c04433. eCollection 2020 Nov 17" |
