Electronic Nose for Black Tea Classification and Correlation of Measurements With “Tea Taster” Marks

Bhattacharyya, Nabarun; Bandyopadhyay, Rajib; Bhuyan, Manabendra; Tudu, Bipan; Ghosh, Devanita; Jana, Arun

doi:10.1109/tim.2008.917189

Cited by 158 publications

(71 citation statements)

References 11 publications

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“…Finally, only the first PCs explaining a sufficient amount of the overall variance are retained, the remaining ones being disregarded. By carefully retaining an adequate number of PCs and splitting the overall set of samples into two sets, the calibration and the training set, overfitting can be avoided [38].…”

Section: Feature Extraction and Classification Methodsmentioning

confidence: 99%

Identification of NR and EPDM Samples by Means of Thermogravimetric Analysis and Multivariate Methods

2016

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Section: Feature Extraction and Classification Methodsmentioning

confidence: 99%

Identification of NR and EPDM Samples by Means of Thermogravimetric Analysis and Multivariate Methods

2016

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“…The gas flow rate of the dynamic sampling was set to 50 ml/min. The electronic nose measurement sequence started with sample equilibration at 558C for 25 min according to previous experiments and tea scientists who established that optimum volatile emission takes place from the tea at around 60 AE 58C [33]. Then reference air was pumped over the sensor surfaces for 10 s (baseline) followed by the puree headspace for 30 s (sampling time) while the sensor signals were recorded.…”

Section: Measurement By Electronic Nosementioning

confidence: 99%

Comparison of novel sensory panel performance evaluation techniques with e‐nose analysis integration

Sípos

Kovács

Szöllősi

et al. 2011

Journal of Chemometrics

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Reliability and validity of sensory data is an important issue in scientific researches. If sensory analysis is performed in an analytical approach, the resulting data will show a similar structure to the chemical analyses. In the present paper the authors have used a complex approach to evaluate the performance of a sensory panel. The tested samples were black tea batches from different plantations of Sri Lanka. Profile analysis was applied to identify the odor profiles of the samples. Sensory profile data was submitted to two novel techniques of panel performance evaluation. GCAP (Gravity Center Area/Perimeter) is based on the profile polygons of the individual assessors. If the area/perimeter ratio of two panelists' profiles is similar and the gravity center is located near to each other, the panelists performed the tests consistently. CRRN (Compare Ranks with Random Numbers) is applicable not only to sensory data but also to other field of chemometrics. The essence of CRRN method is based on the evaluation of an 'average' vector, corresponding to the coordinate-averages of the measured points, and on a produced random vector series of the same dimension as the measured points. Sensory and e-nose data were evaluated with principal component analysis, cluster analysis and linear discriminant analysis. Partial least square regression and support vector machine regression were used to predict sensory data with electronic nose results. Prediction by support vector machine gave close correlation between the results of electronic nose measurement and odor attributes.

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“…The gas flow rate of the dynamic sampling was set to 50 mL/min. The electronic nose measurement sequence started with sample equilibration at 558C for 25 min, since tea scientists have established that optimum volatile emission takes place from the tea at 60 AE 58C [19]. Then reference air was pumped over the sensor surfaces for 10 s (baseline) followed by the tea infusion head space for 30 s (sampling time) while the sensor signals were recorded.…”

Section: Electronic Nose Analysismentioning

confidence: 99%

Geographical origin identification of pure Sri Lanka tea infusions with electronic nose, electronic tongue and sensory profile analysis

Kovács

Dalmadi

Lukács³

et al. 2010

Journal of Chemometrics

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a Tea quality used to be traditionally evaluated by sensory panels. Their expertise is considerably better than that of the average consumers. However, there can be definite subjectivity in the judgment of sensory panels. A new direction is advanced sensory study that integrates sensory assessment with instrumental evaluation. The aim of this paper is to distinguish five Sri Lanka black teas from plantations of different geographical origins. From the tea samples, infusions were prepared with standard procedure. Three different test methods were applied: electronic nose, electronic tongue and sensory assessment. In the case of the electronic nose only the volatile components were evaluated. The Alpha Astree II potentiometric electronic tongue analyzed the liquid phase, while in the sensory profiling all perceptible attributes were tested. Data were analyzed with principle component analysis (PCA), linear discriminant analysis (LDA) and in the case of sensory analysis with one-way ANOVA. The partial least square regression (PLS) was used to estimate the sensory attributes. As a final result, we found that LDA maps show the quality differences of teas grown at different altitudes and geographical location of growing regions.

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Electronic Nose for Black Tea Classification and Correlation of Measurements With “Tea Taster” Marks

Cited by 158 publications

References 11 publications

Identification of NR and EPDM Samples by Means of Thermogravimetric Analysis and Multivariate Methods

Identification of NR and EPDM Samples by Means of Thermogravimetric Analysis and Multivariate Methods

Comparison of novel sensory panel performance evaluation techniques with e‐nose analysis integration

Geographical origin identification of pure Sri Lanka tea infusions with electronic nose, electronic tongue and sensory profile analysis

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