Qualitative identification of tea categories by near infrared spectroscopy and support vector machine

Zhao, Jiewen; Chen, Quansheng; Huang, Xingyi; Fang, Chao

doi:10.1016/j.jpba.2006.02.053

Cited by 110 publications

(58 citation statements)

References 30 publications

(36 reference statements)

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“…Three quarters of the samples taken on different days were selected at random as the training set, other samples as the prediction set for building the SVM model. RBF kernel function was selected as the kernel function of SVM because it has been applied widely and its theoristical system is also more mature than other kernel functions (Zhao et al 2006). The performance of the SVM model is particularly vulnerable to the parameter g of RBF kernel function and the regularisation constant c which determines the tradeoff between minimising the training error and minimising the model complexity (Chen et al 2007).…”

Section: Resultsmentioning

confidence: 99%

A nondestructive method for fish freshness determination with electronic tongue combined with linear and non-linear multivariate algorithms

et al. 2014

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Han F., Huang X., Teye E., Gu H., Dai H., Yao L. (2014): A nondestructive method for fish freshness determination with electronic tongue combined with linear and non-linear multivariate algorithms. Czech J. Food Sci., 32: 532-537.Electronic tongue coupled with linear and non-linear multivariate algorithms was attempted to address the drawbacks of fish freshness detection. Parabramis pekinensis fish samples stored at 4°C were used. Total volatile basic nitrogen (TVB-N) and total viable count (TVC) of the samples were measured. Fisher liner discriminant analysis (Fisher LDA) and support vector machine (SVM) were applied comparatively to classify the samples stored at different days. The results revealed that SVM model was better than Fisher LDA model with a higher identification rate of 97.22% in the prediction set. Partial least square (PLS) and support vector regression (SVR) were applied comparatively to predict the TVB-N and TVC values. The quantitative models were evaluated by the root mean square error of prediction (RMSEP) and the correlation coefficient in the prediction set (R pre ). The results revealed that SVR model was superior to PLS model with RMSEP = 5.65 mg/100 g, R pre = 0.9491 for TVB-N prediction and RMSEP = 0.73 log CFU/g, R pre = 0.904 for TVC prediction. This study demonstrated that the electronic tongue together with SVM and SVR has a great potential for a convenient and nondestructive detection of fish freshness.

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Section: Resultsmentioning

confidence: 99%

A nondestructive method for fish freshness determination with electronic tongue combined with linear and non-linear multivariate algorithms

et al. 2014

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“…Therefore, it will lead to 'bad' results when some new samples are predicted by this model. Generally, the non-linear pattern recognition method has better abilities than the linear pattern recognition method in self-organisation and self-learning (Zhao et al 2006), therefore, the identification results obtained from ANN model are a little better than those from LDA model.…”

Section: Identification Results Of Ann Modelmentioning

confidence: 99%

Nondestructive identification of tea (Camellia sinensis L.) varieties using FT-NIR spectroscopy and pattern recognition

Chen¹,

Zhao²,

MuHua³

et al. 2008

Czech J. Food Sci.

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Chen Q., Zhao J., Liu M., Cai J. (2008): Nondestructive identification of tea (Camellia sinensis L.) varieties using FT-NIR spectroscopy and pattern recognition. Czech J. Food Sci., 26: 360-367.Due to more and more tea varieties in the current tea market, rapid and accurate identification of tea (Camellia sinensis L.) varieties is crucial to the tea quality control. Fourier Transform Near-Infrared (FT-NIR) spectroscopy coupled with the pattern recognition was used to identify individual tea varieties as a rapid and non-invasive analytical tool in this work. Seven varieties of Chinese tea were studied in the experiment. Linear Discriminant Analysis (LDA) and Artificial Neural Network (ANN) were compared to construct the identification models based on Principal Component Analysis (PCA). The number of principal components factors (PCs) was optimised in the constructing model. The experimental results showed that the performance of ANN model was better than LDA models. The optimal ANN model was achieved when four PCs were used, identification rates being all 100% in the training and prediction sets. The overall results demonstrated that FT-NIR spectroscopy technology with ANN pattern recognition method can be successfully applied as a rapid method to identify tea varieties.

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“…Tea can be broadly classified according to the processing methods as; un-aerated tea (green tea), semi-aerated tea (Oolong tea), fully aerated tea (black tea) or post-aerated tea (pu-erh tea) [3]. The beverage has over time gained popularity as a "health drink" due to the numerous beneficial medicinal properties that have been attributed to its polyphenolic content as evidenced by in vitro and animal studies [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Estimation of <i>γ</i>-Glutamylethylamide in Commercially Available Made Teas [<i>Camellia sinensis</i> (L.) O. Kuntze, Theaceae] in Kenya

Too¹,

Wanyoko²,

Kinyanjui³

et al. 2016

AJPS

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A study was carried out to quantitatively estimate the L-theanine content in 19 teas commercially available in the Kenyan market by High Performance Liquid Chromatography (HPLC). The test tea samples analyzed were green (n = 4), black (n = 8) and flavored (n = 7) teas from different origins viz., Kenya (n = 4), Uganda (n = 2), Tanzania (n = 5), Rwanda (n = 4), Cameroon (n = 1) and Sri-Lanka (n = 2) commercially available in the Kenyan market. The estimated Limit of Detection (LOD) of the current method was 0.01% L-theanine. The L-theanine content ranged from below the detection limit (<0.01% L-theanine) to 1.60% L-theanine on a dry weight (d.w) basis. Statistically significant differences (p < 0.05) were observed in the L-theanine contents of black, green and flavoured teas. Rwandan green tea contained the highest L-theanine content with 1.60% d.w. whereas six of the seven flavoured teas had very low theanine levels (<0.01%) that could not be quantified by the current method.

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Qualitative identification of tea categories by near infrared spectroscopy and support vector machine

Cited by 110 publications

References 30 publications

A nondestructive method for fish freshness determination with electronic tongue combined with linear and non-linear multivariate algorithms

A nondestructive method for fish freshness determination with electronic tongue combined with linear and non-linear multivariate algorithms

Nondestructive identification of tea (Camellia sinensis L.) varieties using FT-NIR spectroscopy and pattern recognition

Quantitative Estimation of <i>γ</i>-Glutamylethylamide in Commercially Available Made Teas [<i>Camellia sinensis</i> (L.) O. Kuntze, Theaceae] in Kenya

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Qualitative identification of tea categories by near infrared spectroscopy and support vector machine

Cited by 110 publications

References 30 publications

A nondestructive method for fish freshness determination with electronic tongue combined with linear and non-linear multivariate algorithms

A nondestructive method for fish freshness determination with electronic tongue combined with linear and non-linear multivariate algorithms

Nondestructive identification of tea (Camellia sinensis L.) varieties using FT-NIR spectroscopy and pattern recognition

Quantitative Estimation of &lt;i&gt;γ&lt;/i&gt;-Glutamylethylamide in Commercially Available Made Teas [&lt;i&gt;Camellia sinensis&lt;/i&gt; (L.) O. Kuntze, Theaceae] in Kenya

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Quantitative Estimation of <i>γ</i>-Glutamylethylamide in Commercially Available Made Teas [<i>Camellia sinensis</i> (L.) O. Kuntze, Theaceae] in Kenya