Enhancing Classification Rate of Electronic Nose System and Piecewise Feature Extraction Method to Classify Black Tea with Superior Quality
Penulis/Author
KOMBO, KOMBO (1); NASRUL IHSAN (2); TRI SISWANDI S (3); SHIDIQ NUR HIDAYAT (4); MAYUMI PUSPITA (5); Wahyono, Ph.D. (6); Prof. Drs. Roto, M.Eng, Ph.D. (7); Prof. Dr. Eng. Kuwat Triyana, M.Si. (8)
Tanggal/Date
30 2024
Kata Kunci/Keyword
Abstrak/Abstract
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 98.6% 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.
Rumpun Ilmu
Fisika
Bahasa Asli/Original Language
English
Level
Internasional
Status
Dokumen Karya
No
Judul
Tipe Dokumen
Aksi
1
L1 Karya Sudah Terbit_SA_Prof_ Kuwat signed.pdf
Dokumen Pendukung Karya Ilmiah (Hibah, Publikasi, Penelitian, Pengabdian)
