Study of herbal tea beverage discrimination method using electronic nose

Jin, Jiaojiao; Deng, Shanggui; Ying, Xiaoguo; Ye, Xiao; Lu, Tingting; Hui, Guohua

doi:10.1007/s11694-014-9209-0

Cited by 21 publications

(15 citation statements)

References 23 publications

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“…Principal component analysis (PCA) and linear discrimination analysis (LDA) are the most commonly used methods in electronic nose data processing. PCA is a method to analyze some principal components (PCs) from experimental data, which can transform some obvious related variates into unrelated variates and permutate these new variates according to the decrease in variance . In applying linear discriminant analysis (LDA), it is necessary that the number of variables is not too large, to prevent overloading of the computations and overfitting problems.…”

Section: Results and Discussionmentioning

confidence: 99%

“…principal components (PCs) from experimental data, which can transform some obvious related variates into unrelated variates and permutate these new variates according to the decrease in variance. 30 In applying linear discriminant analysis (LDA), it is necessary that the number of variables is not too large, to prevent overloading of the computations and overfitting problems. Therefore, when the variable/object ratio is too high, a variable reduction must be performed.…”

Section: Electronic Nosementioning

confidence: 99%

See 1 more Smart Citation

Drying of restructured chips made from the old stalks of Asparagus officinalis : impact of different drying methods

Liu

Zhang

Wang

2015

J. Sci. Food Agric.

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Section: Results and Discussionmentioning

confidence: 99%

Section: Electronic Nosementioning

confidence: 99%

Drying of restructured chips made from the old stalks of Asparagus officinalis : impact of different drying methods

Liu

Zhang

Wang

2015

J. Sci. Food Agric.

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“…With the rapid development of modern science and technology, various high and new technologies are continuously applied to the detection and improvement of rice grain quality to make up for the inadequacies of traditional detection and analysis methods. In addition to the above mentioned high-tech technologies (such as high-throughput sequencing, genome editing technology, NIRS technology and SEM technology), there are also some high-tech technologies have been used for genetic improvement of rice grain quality, such as electronic nose detection technology, computer vision technology, texture analysis technology, differential scanning calorimetry technology and so on (Jin et al, 2015;Xu et al, 2014;Park et al, 2012;Parnsakhorn et al, 2012;Zheng et al, 2009;Roberto et al, 2006;Zhou et al, 2010;Yu et al, 2009;Thorpe et al, 2010). Electronic nose detection technology could rapidly detect and analyze odor molecules in rice grains based on the contact of odor molecules with metal oxides or biofilms (Zheng et al, 2009).…”

Section: Application Of Other High-tech Technologies In Genetic Impromentioning

confidence: 99%

“…Electronic nose detection technology could rapidly detect and analyze odor molecules in rice grains based on the contact of odor molecules with metal oxides or biofilms (Zheng et al, 2009). At the same time, electronic nose detection technology colud also be used to predict fungal infections in grains and even the extent of contamination (Jin et al, 2015;Roberto et al, 2006). Differential scanning calorimetry technology has also been reported to detect the gelatinization temperature of starch in rice.…”

Section: Application Of Other High-tech Technologies In Genetic Impromentioning

confidence: 99%

Genetic Improvement of Grain Quality Promoted by High and New Technology in Rice

Peng¹,

Li²,

Kong³

et al. 2018

JAS

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Rice (Oryza sativa L.) is considered as one of the most important food crops all over the world. Because of the improvement of the actual living standards, there is more and more demand for improved grain quality. Although, the grain quality is a very complex quantitative trait in rice, it has great scientific significance and practical application value for the improvement of the quality of rice grain using high and up to date techniques. Here, we focus on the new advances in the application of the genetic improvement of grain quality by high throughput sequencing, genomic editing, near infrared spectroscopy, scanning electron microscopy, and other new techniques. Simultaneously, the application prospects of these high and up to date techniques have also been expected in the genetic improvement of rice quality. Thus, this will provide important information for genetic improvement of grain quality in rice breeding.

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“…With the rapid development of sensors, the wide application of electronic nose (E-nose), electronic tongue, and near-infrared technology [15][16][17][18][19][20][21][22] has made tea quality estimation easier. Especially, electronic nose technology has the convenience and objectivity of detecting food taste, which has been successfully applied to many aspects of tea research by simulating the human olfactory system, including in the tea fermentation process [23,24], tea classification [25][26][27][28], tea storage [29], and tea components [30].…”

Section: Introductionmentioning

confidence: 99%

Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery

Yang

Wang

et al. 2019

Sensors

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Tea polyphenols are important ingredients for evaluating tea quality. The rapid development of sensors provides an efficient method for nondestructive detection of tea polyphenols. Previous studies have shown that features obtained from single or multiple sensors yield better results in detecting interior tea quality. However, due to their lack of external features, it is difficult to meet the general evaluation model for the quality of the interior and exterior of tea. In addition, some features do not fully reflect the sensor signals of tea for several categories. Therefore, a feature fusion method based on time and frequency domains from electronic nose (E-nose) and hyperspectral imagery (HSI) is proposed to estimate the polyphenol content of tea for cross-category evaluation. The random forest and the gradient boosting decision tree (GBDT) are used to evaluate the feature importance to obtain the optimized features. Three models based on different features for cross-category tea (black tea, green tea, and yellow tea) were compared, including grid support vector regression (Grid-SVR), random forest (RF), and extreme gradient boosting (XGBoost). The results show that the accuracy of fusion features based on the time and frequency domain from the electronic nose and hyperspectral image system is higher than that of the features from single sensor. Whether based on all original features or optimized features, the performance of XGBoost is the best among the three regression algorithms (R2 = 0.998, RMSE = 0.434). Results indicate that the proposed method in this study can improve the estimation accuracy of tea polyphenol content for cross-category evaluation, which provides a technical basis for predicting other components of tea.

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Study of herbal tea beverage discrimination method using electronic nose

Cited by 21 publications

References 23 publications

Drying of restructured chips made from the old stalks of Asparagus officinalis : impact of different drying methods

Drying of restructured chips made from the old stalks of Asparagus officinalis : impact of different drying methods

Genetic Improvement of Grain Quality Promoted by High and New Technology in Rice

Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery

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