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The Electronic Nose Coupled with Chemometric Tools for Discriminating the Quality of Black Tea Samples In Situ

1
Department of Physics, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
2
Institute of Halal Industry and System (IHIS), Universitas Gadjah Mada, Sekip Utara, Yogyakarta 55281, Indonesia
3
Computer and Electronic Department, Universitas Gadjah Mada, Sekip Utara, Yogyakarta 55281, Indonesia
4
Agricultural Microbiology Department, Universitas Gadjah Mada, Bulaksumur, Yogyakarta 55281, Indonesia
5
PT. Pagilaran, Jl. Faridan M. Noto. 11 Kotabaru, Yogyakarta 55281, Indonesia
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Instituto Politécnico de Coimbra, ISEC, DEQB, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal
7
CEB—Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
8
Centro de Investigação de Montanha (CIMO), ESA, Instituto Politécnico de Bragança, Campus Sta Apolónia, 5300-253 Bragança, Portugal
9
Laboratory of Separation and Reaction Engineering—Laboratory of Catalysis and Materials (LSRE-LCM), ESA, Instituto Politécnico de Bragança, Campus Santa Apolónia, 5300-253 Bragança, Portugal
*
Author to whom correspondence should be addressed.
Chemosensors 2019, 7(3), 29; https://doi.org/10.3390/chemosensors7030029
Received: 4 June 2019 / Revised: 1 July 2019 / Accepted: 6 July 2019 / Published: 9 July 2019
(This article belongs to the Special Issue Advanced Electronic Noses and Chemical Detection Systems)
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Abstract

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-effective and fast, green procedure that could be implemented in the near future by the tea industry. View Full-Text
Keywords: electronic nose; black tea; preprocessing; multivariate statistical tools electronic nose; black tea; preprocessing; multivariate statistical tools
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Hidayat, S.N.; Triyana, K.; Fauzan, I.; Julian, T.; Lelono, D.; Yusuf, Y.; Ngadiman, N.; Veloso, A.C.; Peres, A.M. The Electronic Nose Coupled with Chemometric Tools for Discriminating the Quality of Black Tea Samples In Situ. Chemosensors 2019, 7, 29.

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