The electronic nose coupled with chemometric tools for discriminating the quality of black tea samples in situ
| Autor: | António M. Peres, Danang Lelono, Inggrit Fauzan, Ana C. A. Veloso, Shidiq Nur Hidayat, Kuwat Triyana, Yusril Yusuf, N. Ngadiman, Trisna Julian |
|---|---|
| Přispěvatelé: | Universidade do Minho |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
electronic nose
Multivariate statistics black tea multivariate statistical tools Medicina Básica [Ciências Médicas] ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Multivariate statistical tools 01 natural sciences Signal Electronic nose Analytical Chemistry lcsh:Biochemistry Set (abstract data type) Black tea 0404 agricultural biotechnology Hardware_INTEGRATEDCIRCUITS Preprocessor lcsh:QD415-436 Physical and Theoretical Chemistry preprocessing Preprocessing Mathematics Science & Technology business.industry 010401 analytical chemistry Pattern recognition 04 agricultural and veterinary sciences Quadratic classifier 040401 food science 0104 chemical sciences Support vector machine Ciências Médicas::Medicina Básica Principal component analysis Artificial intelligence business |
| Zdroj: | Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP Chemosensors Volume 7 Issue 3 Chemosensors, Vol 7, Iss 3, p 29 (2019) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|