Kombo Othman Kombo, Nasrul Ihsan, Tri Siswandi Syahputra, Shidiq Nur Hidayat, Mayumi Puspita, Wahyono, Roto Roto, Kuwat Triyana
This study introduced a metal-oxide-semiconductor (MOS) based electronic nose (E-nose) to perform on-the-spot classification of superior-quality black tea. A piecewise feature method based on a line-fitting model was introduced to extract comprehensive features of E-nose sensor response curves. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used for data dimensionality reduction and structure visualization. Support vector machine (SVM) with a Radial kernel function was used to assess the performance of E-nose. The results indicated that the SVM model coupled with the piecewise feature method performed better and achieved the best classification rates of 99.50 %, 95.30 %, and 96.50 %, for training, validation, and testing datasets respectively, with testing sensitivity and specificity of up to 98.60 % and 99.10 %. The E-nose result was further correlated with compound concentrations in the black tea, measured using gas chromatography-mass spectrometry (GC–MS). Based on its enhanced performance evaluation, the introduced lab-built E-nose system yielded promising results in assessing superior-quality black tea. © 2024 The Authors
Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara, BLS 21, Yogyakarta, 55281, Indonesia; Department of Computer Science and Electronics, Universitas Gadjah Mada, Sekip Utara BLS 21, Yogyakarta, 55281, Indonesia; Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara PO Box BLS 21, Yogyakarta, 55281, Indonesia; Department of Natural Sciences, College of Science and Technical Education, Mbeya University of Science and Technology, P.O.Box 131, Mbeya, Tanzania; Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Makassar, Makassar, Indonesia; Department of Physics, Faculty of Science, Institut Teknologi Sumatera, Jalan Terusan Ryacudu, Lampung, 35365, Indonesia
