Advisor(s)
Abstract(s)
An electronic nose (E-nose), comprising eight metal oxide semiconductor (MOS) gas
sensors, was used in situ for real-time classification of black tea according to its quality level.
Principal component analysis (PCA) coupled with signal preprocessing techniques (i.e., time set
value preprocessing, F1; area under curve preprocessing, F2; and maximum value preprocessing,
F3), allowed grouping the samples from seven brands according to the quality level. The E-nose
performance was further checked using multivariate supervised statistical methods, namely, the
linear and quadratic discriminant analysis, support vector machine together with linear or radial
kernels (SVM-linear and SVM-radial, respectively). For this purpose, the experimental dataset
was split into two subsets, one used for model training and internal validation using a repeated
K-fold cross-validation procedure (containing the samples collected during the first three days of tea
production); and the other, for external validation purpose (i.e., test dataset, containing the samples
collected during the 4th and 5th production days). The results pointed out that the E-nose-SVM-linear
model together with the F3 signal preprocessing method was the most accurate, allowing 100%
of correct predictive classifications (external-validation data subset) of the samples according to
their quality levels. So, the E-nose-chemometric approach could be foreseen has a practical and
feasible classification tool for assessing the black tea quality level, even when applied in-situ, at the
harsh industrial environment, requiring a minimum and simple sample preparation. The proposed
approach is a cost-e ective and fast, green procedure that could be implemented in the near future by
the tea industry.
Description
Keywords
Black tea Electronic nose Multivariate statistical tools Preprocessing
Pedagogical Context
Citation
Hidayat, Shidiq Nur; Triyana, Kuwat; Fauzan, Inggrit; Julian, Trisna; Lelono, Danang; Yusuf, Yusril; Ngadiman, N.; Veloso, Ana C.A.; Peres, António M. (2019). The electronic nose coupled with chemometric tools for discriminating the quality of black tea samples in situ. Chemosensors. ISSN 2227-9040. 7, p. 1-14