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

Authors :
Shidiq Nur Hidayat
Kuwat Triyana
Inggrit Fauzan
Trisna Julian
Danang Lelono
Yusril Yusuf
N. Ngadiman
Ana C.A. Veloso
António M. Peres
Source :
Chemosensors, Vol 7, Iss 3, p 29 (2019)
Publication Year :
2019
Publisher :
MDPI AG, 2019.

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.

Details

Language :
English
ISSN :
22279040
Volume :
7
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Chemosensors
Publication Type :
Academic Journal
Accession number :
edsdoj.b822fc6ceee34670a30a69292c00686d
Document Type :
article
Full Text :
https://doi.org/10.3390/chemosensors7030029