Green analytical assay for the quality assessment of tea by using pocket-sized NIR spectrometer

Wang, Yujie; Li, Menghui; Li, Luqing; Ning, Jingming; Zhang, Zhengzhu

doi:10.1016/j.foodchem.2020.128816

Cited by 68 publications

(24 citation statements)

References 22 publications

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“…PLSR is one of the most widely used linear regression algorithms (Wang et al., 2021a , 2021b ) and an optimal choice for constructing a prediction model. It has the advantage of considering both matrices x (spectral data) and y (starch content).…”

Section: Methodsmentioning

confidence: 99%

Study on starch content detection and visualization of potato based on hyperspectral imaging

Wang

Song

et al. 2021

Food Science & Nutrition

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Potatoes contain nutrients such as vitamin C, vitamin B6, folic acid, potassium, iron, and magnesium (Ji et al., 2019). With an annual global output of approximately 400 million metric tons, potatoes have become one of the four major global food crops, according to the United Nations Food and Agriculture Organization. They are mainly processed into various products, including frozen or dehydrated potatoes, potato chips, and potato starch (Rady et al., 2015).

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Section: Methodsmentioning

confidence: 99%

Study on starch content detection and visualization of potato based on hyperspectral imaging

Wang

Song

et al. 2021

Food Science & Nutrition

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“…It is difficult for consumers to distinguish the types of tea only with the naked eye, and they often buy cheap tea at a high price, which enables the occurrence of fraud from time to time. To address this situation, this study will use fluorescence hyperspectral technology and machine learning to distinguish Oolong tea [8].…”

Section: Introductionmentioning

confidence: 99%

“…Distinguishing different types of tea or studying tea adulteration can summarize the problem of whether the authenticity of tea can be properly identified [2]. There are many methods of tea classification in current research, which mainly rely on the experience of professionals [8] who, through sensory evaluation, appreciate the color, smell, shape and flavor of tea [9]. This method is not only tedious and time-consuming, but also destroys the structure of tea and causes waste [7].…”

Section: Introductionmentioning

confidence: 99%

“…This method is not only tedious and time-consuming, but also destroys the structure of tea and causes waste [7]. Conventional analytical techniques have also been used for the classification of tea, such as gas chromatography coupled mass spectrometry (GC-MS) [8,9] and high-performance liquid chromatography (HPLC). The aforementioned chemical methods are costly and time-consuming too, which hinders their prevalence.…”

Section: Introductionmentioning

confidence: 99%

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Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning

Huang

et al. 2021

Agriculture

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A rapid and nondestructive tea classification method is of great significance in today’s research. This study uses fluorescence hyperspectral technology and machine learning to distinguish Oolong tea by analyzing the spectral features of tea in the wavelength ranging from 475 to 1100 nm. The spectral data are preprocessed by multivariate scattering correction (MSC) and standard normal variable (SNV), which can effectively reduce the impact of baseline drift and tilt. Then principal component analysis (PCA) and t-distribution random neighborhood embedding (t-SNE) are adopted for feature dimensionality reduction and visual display. Random Forest-Recursive Feature Elimination (RF-RFE) is used for feature selection. Decision Tree (DT), Random Forest Classification (RFC), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are used to establish the classification model. The results show that MSC-RF-RFE-SVM is the best model for the classification of Oolong tea in which the accuracy of the training set and test set is 100% and 98.73%, respectively. It can be concluded that fluorescence hyperspectral technology and machine learning are feasible to classify Oolong tea.

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“…6 In addition, in tea, Wang et al used micro-near-infrared spectrometry to qualitatively and quantitatively evaluate tea, which provided a new method for in situ evaluation of tea quality. 7 As a new remote sensing method, unmanned aerial vehicle (UAV) remote sensing not only has the advantages of high flexibility, high speed, and lossless and high resolution, but also has the advantages of real-time and high throughput, which can effectively make up for some defects of ground measurement. [8][9][10][11] At present, many physical and chemical parameter-monitoring technologies have been developed using UAV remote sensing technology.…”

Section: Introductionmentioning

confidence: 99%

Using UAV image data to monitor the effects of different nitrogen application rates on tea quality

et al. 2021

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BACKGROUND: Accurate and efficient evaluation of the effect of nitrogen application rate on tea quality is of great significance for nitrogen management in a tea garden. However, previous methods were all through soil or leaf sampling, using biochemical methods for laboratory testing. These methods are not only less one-time detection samples, but also time-consuming, laborious and inefficient. Therefore, the development of fast, efficient and non-destructive diagnostic methods is an important goal in this field.RESULTS: We obtained spectral information on the tea canopy using a multispectral camera carried by an unmanned aerial vehicle (UAV), and extracted the average DN value of the experimental plot by environmental visual imagery (ENVI); we finally obtained 28 spectral parameters. By analyzing the correlation between spectral parameters and ground parameters measured synchronously, five spectral parameters with high correlation were selected. Finally, the prediction models of tea nitrogen, polyphenol and amino acid content were established by using support vector machine (SVM), partial least squares and backpropagation neural network. Through modeling comparison and coefficient verification, the results show that the ground parameters measured in the laboratory were in good agreement with the results estimated by the model. The SVM model had the best performance in predicting nitrogen and tea polyphenol content, with R 2 = 0.7583 and 0.7533, root mean square error of prediction (RMSEP) = 0.4086 and 0.3392, and normalized RMSEP (NRMSEP) = 1.23 and 1.28, respectively. The partial least squares regression model had the best performance in predicting amino acid content, with R 2 = 0.7597, RMSEP = 0.1176 and NRMSEP = 4.10. CONCLUSION:The results show that the model based on UAV image data and machine learning algorithm can effectively detect the main biochemical components of the tea plant, which provides an important basis for tea garden management.

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Green analytical assay for the quality assessment of tea by using pocket-sized NIR spectrometer

Cited by 68 publications

References 22 publications

Study on starch content detection and visualization of potato based on hyperspectral imaging

Study on starch content detection and visualization of potato based on hyperspectral imaging

Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning

Using UAV image data to monitor the effects of different nitrogen application rates on tea quality

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