A novel strategy of near-infrared spectroscopy dimensionality reduction for discrimination of grades, varieties and origins of green tea

Liu, Peng; Wen, Yangping; Huang, Jun-shi; Xiong, Aihua; Wen, Jing; Li, Hong; Huang, Yifeng; Zhu, Xiaoyu; Ai, Shirong; Wu, Ruimei

doi:10.1016/j.vibspec.2019.102984

Cited by 25 publications

(10 citation statements)

References 33 publications

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“…Jia et al [31] used near-infrared spectroscopy (spectral range: 1350-1800 nm) to identify the growing area of Longjing tea, and the classification accuracy of two growing areas was 97.3%. Liu et al [32] used near-infrared spectroscopy to identify grades, varieties, and geographical origins of green tea. The classification accuracy all reached 100%.…”

Section: Calibration Setmentioning

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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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: Calibration Setmentioning

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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“…Infrared (IR) spectroscopy, as a detection technology, has some advantages such as rapid, convenience, high efficiency, nondestructive, and low cost (Pereira, Leite, Andrade, Bell, & Anjos, 2019; Sun et al., 2017; Yang, Li, Song, Gao, & Zhou, 2018). IR spectroscopy technology has been widely applied in many fields such as food inspection (Jiang & Chen, 2015; Liu et al., 2019; Sun, Ge, Wu, Dai, & Yang, 2018; Tahir et al., 2017), classification of agricultural products (Chen, Lin, & Tan, 2019; Ferreiro‐González et al., 2018; Wu, Wu, Sun, & Yang, 2017; Wu, Yang, & Wang, 2018), and pharmaceutical analysis(Mazivila, Castro, Leitão, & Esteves da Silva, 2019; Oparin & Kiselev, 2020). Especially for the identification of tea varieties, IR spectroscopy technology combined with pattern recognition plays an importance role.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of tea varieties based on FTIR spectroscopy and an adaptive improved possibilistic c‐means clustering

Zhou

et al. 2020

J. Food Process. Preserv.

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“…Disparities between the origin labeling information and the actual origin of tea may occur; however, these findings demonstrate the importance of the discriminating teas with different origins. Compared to the previous literatures with NIR spectroscopy in tea analysis [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 39 , 40 , 51 ], this study is the first to trace partially fermented tea samples from different countries and regions simultaneously. In terms of future research, the partially fermented tea samples from more countries or regions may be included to validate the effectiveness of this method.…”

Section: Resultsmentioning

confidence: 99%

“…RBF kernel function parameters, namely penalty coefficient (c) and gamma (γ), were optimized by a grid-search procedure. The parameter c was applied to minimize the fitting error and the complexity of the model, while parameter γ was employed to characterize the nonlinear mapping from the input space to a high-dimensional feature space [ 20 ]. The SVM analyses of tea leaves and powders were performed using the MATLAB programs (MATLAB R2010a; The MathWorks, Inc., Natick, MA, USA) developed by our research group.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, NIR has been widely applied in the inspection and analysis of food and agricultural products [ 15 , 16 ]. While application of NIR for origin discrimination of tea leaves appears promising, available literature still focuses mainly on green teas [ 17 , 18 , 19 , 20 ], black teas [ 21 , 22 ], or the partially fermented teas in a single country/region [ 23 , 24 , 25 ]. To date, the origin discrimination of Taiwanese and imported partially fermented teas have not been investigated using NIR.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Analysis and Discrimination of Partially Fermented Teas from Different Origins Using Visible/Near-Infrared Spectroscopy Coupled with Chemometrics

Tung

Hsu

et al. 2020

Sensors

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Partially fermented tea such as oolong tea is a popular drink worldwide. Preventing fraud in partially fermented tea has become imperative to protect producers and consumers from possible economic losses. Visible/near-infrared (VIS/NIR) spectroscopy integrated with stepwise multiple linear regression (SMLR) and support vector machine (SVM) methods were used for origin discrimination of partially fermented tea from Vietnam, China, and different production areas in Taiwan using the full visible NIR wavelength range (400–2498 nm). The SMLR and SVM models achieved satisfactory results. Models using data from chemical constituents’ specific wavelength ranges exhibited a high correlation with the spectra of teas, and the SMLR analyses improved discrimination of the types and origins when performing SVM analyses. The SVM models’ identification accuracies regarding different production areas in Taiwan were effectively enhanced using a combination of the data within specific wavelength ranges of several constituents. The accuracy rates were 100% for the discrimination of types, origins, and production areas of tea in the calibration and prediction sets using the optimal SVM models integrated with the specific wavelength ranges of the constituents in tea. NIR could be an effective tool for rapid, nondestructive, and accurate inspection of types, origins, and production areas of teas.

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A novel strategy of near-infrared spectroscopy dimensionality reduction for discrimination of grades, varieties and origins of green tea

Cited by 25 publications

References 33 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 tea varieties based on FTIR spectroscopy and an adaptive improved possibilistic c‐means clustering

Quantitative Analysis and Discrimination of Partially Fermented Teas from Different Origins Using Visible/Near-Infrared Spectroscopy Coupled with Chemometrics

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