Evaluating green tea quality based on multisensor data fusion combining hyperspectral imaging and olfactory visualization systems

Li, Luqing; Xie, Shimeng; Ning, Jingming; Chen, Quansheng; Zhang, Zhengzhu

doi:10.1002/jsfa.9371

Cited by 98 publications

(47 citation statements)

References 31 publications

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“…Hyperspectral imaging is widely studied in the fields of agriculture [12,13], food [14], medical [15], and pharmaceutical [16], to name a few. Moreover, hyperspectral imaging is also studied in the tea industry for tea quality inspection [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…In general, a certain amount of tea samples (contains tens or more tea leaves in a sample) is used for VINRS and HSI analysis and overlap of tea leaves occurs in this situation [6][7][8][9][10][17][18][19]. Thus it is difficult to identify adulterations in this amount of tea when some amounts of adulterations are mixed into the normal sample.…”

Section: Introductionmentioning

confidence: 99%

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Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

Hong

2020

Applied Sciences

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Longjing tea is one of China’s protected geographical indication products with high commercial and nutritional value. The geographical origin of Longjing tea is an important factor influencing its commercial and nutritional value. Hyperspectral imaging systems covering the two spectral ranges of 380–1030 nm and 874–1734 nm were used to identify a single tea leaf of Longjing tea from six geographical origins. Principal component analysis (PCA) was conducted on hyperspectral images to form PCA score images. Differences among samples from different geographical origins were visually observed from the PCA score images. Support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) models were built using the full spectra at the two spectral ranges. Decent classification performances were obtained at the two spectral ranges, with the overall classification accuracy of the calibration and prediction sets over 84%. Furthermore, prediction maps for geographical origins identification of Longjing tea were obtained by applying the SVM models on the hyperspectral images. The overall results illustrate that hyperspectral imaging at both spectral ranges can be applied to identify the geographical origin of single tea leaves of Longjing tea. This study provides a new, rapid, and non-destructive alternative for Longjing tea geographical origins identification.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

Hong

2020

Applied Sciences

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“…Presently, the application of stacked generalization for establishing classification models of different medicinal plants or herbs is rather scarce. On the contrary, another modeling approach, data fusion strategy, has been widely used for classification and geographical origin traceability of herbs and foods [48,49,53,54]. Some researches stated that spectra data fusion, such as low-level and mid-level fusion strategies, could improve the discrimination capacity of the classification models and those strategies were usually more efficient than single spectroscopic techniques for modeling [48,49].…”

Section: Are Model Stacking Better Than Data Fusion For Gentiana Specmentioning

confidence: 99%

“…As we know, the data fusion (low-level and mid-level) approaches present a fusion of all variables or most important variables (feature variables) to create a model in order to exploit the synergy of the multispectral information to obtain an optimized model [53][54][55][56]. However, the calculation time might be higher when increasing variables.…”

Section: Are Model Stacking Better Than Data Fusion For Gentiana Specmentioning

confidence: 99%

Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization

Shen

Wang

2020

Molecules

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Gentiana, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical treatment. Because of the phytochemical diversity and difference of bioactive compounds among species, which makes it crucial to accurately identify authentic Gentiana species. In this paper, the feasibility of using the infrared spectroscopy technique combined with chemometrics analysis to identify Gentiana and its related species was studied. A total of 180 batches of raw spectral fingerprints were obtained from 18 species of Gentiana and Tripterospermum by near-infrared (NIR: 10,000–4000 cm−1) and Fourier transform mid-infrared (MIR: 4000–600 cm−1) spectrum. Firstly, principal component analysis (PCA) was utilized to explore the natural grouping of the 180 samples. Secondly, random forests (RF), support vector machine (SVM), and K-nearest neighbors (KNN) models were built while using full spectra (including 1487 NIR variables and 1214 FT-MIR variables, respectively). The MIR-SVM model had a higher classification accuracy rate than the other models that were based on the results of the calibration sets and prediction sets. The five feature selection strategies, VIP (variable importance in the projection), Boruta, GARF (genetic algorithm combined with random forest), GASVM (genetic algorithm combined with support vector machine), and Venn diagram calculation, were used to reduce the dimensions of the data variable in order to further reduce numbers of variables for modeling. Finally, 101 NIR and 73 FT-MIR bands were selected as the feature variables, respectively. Thirdly, stacking models were built based on the optimal spectral dataset. Most of the stacking models performed better than the full spectra-based models. RF and SVM (as base learners), combined with the SVM meta-classifier, was the optimal stacked generalization strategy. For the SG-Ven-MIR-SVM model, the accuracy (ACC) of the calibration set and validation set were both 100%. Sensitivity (SE), specificity (SP), efficiency (EFF), Matthews correlation coefficient (MCC), and Cohen’s kappa coefficient (K) were all 1, which showed that the model had the optimal authenticity identification performance. Those parameters indicated that stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and avoid overfitting. The study result can provide a valuable reference for the safety and effectiveness of the clinical application of medicinal Gentiana.

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“…Reasonable pretreatment technologies for fresh tea leaves contribute to increasing the content of tea quality compositions, and improve the characteristic aroma, astringent, strong, bitter and fresh taste of tea brews. 13,14 In recent years, research on the preprocessing methods of fresh tea leaves has mainly involved the design of tea processing single machinery, 15,16 the optimization of processing a particular kind of tea, 17,18 dynamic changes in the major chemical components of tea, 19,20 changes in tea aroma component contents and impact on tea quality. [21][22][23] There have been no reports of a multifunctional preprocessing device synchronously used for spreading of green tea, withering of black tea, and shaking of oolong tea.…”

Section: Introductionmentioning

confidence: 99%

Applicability of multifunctional preprocessing device for simultaneous estimation of spreading of green tea, withering of black tea and shaking of oolong tea

Ren

Fan

et al. 2019

J Sci Food Agric

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BACKGROUND Preprocessing technologies of fresh tea leaves have a great influence on tea quality. A multifunctional preprocessing device for tea raw materials has been designed and utilized as a novel item of equipment to synchronously meet the process needs of spreading of green tea, withering of black tea and shaking of oolong tea. RESULTS The preprocessing parameters of fresh tea leaves for spreading of green tea, withering of black tea and shaking of oolong tea were optimized by orthogonal experiments. Sensory assessment combined with statistical tools was employed as an analytical method to evaluate the pretreatment effect of processing different sorts of tea. The range analysis and variance analysis of tea sensory evaluation combined with chemical components (total polyphenols, free amino acids and soluble sugar) showed that A3B2C3 (70%, 25 °C, 8 h), A1B3C1 (60%, 28 °C, 18 h) and A1B1C3 (5 min, 20 °C, discontinuous leaf turning c) were considered to be the optimum schemes for the best pretreatment conditions of the above three major processing types of tea, respectively. The verification experiment of the proposed schemes was performed with satisfactory performance. CONCLUSION This study demonstrated that a multifunctional preprocessing device for fresh tea leaves can be successfully applied to simultaneously estimate spreading of green tea, withering of black tea and shaking of oolong tea. © 2019 Society of Chemical Industry

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Evaluating green tea quality based on multisensor data fusion combining hyperspectral imaging and olfactory visualization systems

Abstract: Overall, it can be concluded that multisensory data accurately identify six grades of tea. © 2018 Society of Chemical Industry.

Cited by 98 publications

References 31 publications

Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization

Applicability of multifunctional preprocessing device for simultaneous estimation of spreading of green tea, withering of black tea and shaking of oolong tea

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