Enhancing Classification Rate of Electronic Nose System and Piecewise Feature Extraction Method to Classify Black Tea with Superior Quality

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.