Kombo, Kombo Othman and Ihsan, Nasrul and Syahputra, Tri Siswandi and Hidayat, Shidiq Nur and Puspita, Mayumi and Wahyono, Wahyono and Roto, Roto and Triyana, Kuwat (2024) Enhancing classification rate of electronic nose system and piecewise feature extraction method to classify black tea with superior quality. Scientific African, 24: e02153. ISSN 24682276
Enhancing classification rate of electronic nose system and.pdf - Published Version
Restricted to Registered users only
Download (7MB) | Request a copy
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 98.60 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
Item Type: | Article |
---|---|
Additional Information: | Cited by: 0; All Open Access, Gold Open Access |
Uncontrolled Keywords: | Electronic nose,Superior-quality,Line-fitting model,Support vector machine,Chromatography-mass spectrometry |
Subjects: | Q Science > QC Physics |
Divisions: | Faculty of Mathematics and Natural Sciences > Physics Department |
Depositing User: | Yulistiarini Kumaraningrum KUMARANINGRUM |
Date Deposited: | 22 Nov 2024 09:16 |
Last Modified: | 22 Nov 2024 09:16 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/10652 |