Predictive geographical authentication of green tea with protected designation of origin using a random forest model

Deng, Xunfei; Liu, Zhi; Zhan, Yu; Ni, Kang; Zhang, Yongzhi; Ma, Wenqi; Shao, Shuai; Lv, Xiaonan; Yuan, Yujie; Rogers, Karyne M.

doi:10.1016/j.foodcont.2019.106807

Cited by 72 publications

(29 citation statements)

References 49 publications

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“…Moreover, several prior studies have shown that stable isotopic fingerprints in tea leaves (e.g., C, H, O, N, B, and Sr) combined with mineral multi‐elements were effective for the tracing the geographical origins of tea leaves . Some new multivariate statistical tools such as decision tree, random forest model, support vector machine, and k‐nearest‐neighbor analysis were also successfully used to authenticate the geographical origins of agro‐products. These new multivariate statistical models and the stable isotopic fingerprints in tea leaves may therefore be used to trace the geographical origin of tea leaves in future work.…”

Section: Resultsmentioning

confidence: 99%

Use of mineral multi‐elemental analysis to authenticate geographical origin of different cultivars of tea in Guizhou, China

Zhang

Yang

et al. 2020

J Sci Food Agric

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BACKGROUND The geographical origin of tea (Camellia sinensis) can be traced using mineral elements in its leaves as fingerprints. However, the role that could be played by soil mineral elements in the geographical authentication of tea leaves has been unclear. In this study, 22 mineral elements in 73 pairs of tea leaves and soils from three regions (Pu'an, Duyun, and Liping) in Guizhou, China, were determined using inductively coupled plasma mass spectrometry (ICP‐MS) and inductively coupled plasma atomic emission spectrometry (ICP‐AES). The mineral element concentrations were processed by multivariate statistical analysis, including one‐way analysis of variance (ANOVA), correlation analysis, principal component analysis (PCA), and stepwise linear discriminant analysis (S‐LDA). RESULTS Based on a one‐way ANOVA, tea leaves and soils with different origins possessed unique mineral element fingerprints. Sixteen mineral element concentrations in tea leaves were significantly correlated with those in soils (P < 0.05). The geographical origins of tea leaves were effectively differentiated using the 16 correlated mineral elements combined with PCA. The S‐LDA model offered a 100% differentiation rate, and six indicative elements (phosphorus, Sr, U, Pb, Cd, and Cr) were selected as important fingerprinting markers for the geographic traceability of tea leaves. The accurate discrimination rate of geographical origin was unaffected by the cultivars of tea in the S‐LDA model. CONCLUSIONS Mineral elements in soils played an important role in the geographical authentication of tea leaves. Mineral elemental concentrations with significant correlations between tea leaves and soils could be robust, and could be used to trace the geographical origins of tea leaves. © 2020 Society of Chemical Industry

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Section: Resultsmentioning

confidence: 99%

Use of mineral multi‐elemental analysis to authenticate geographical origin of different cultivars of tea in Guizhou, China

Zhang

Yang

et al. 2020

J Sci Food Agric

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“…Therefore, we think the RF model is sufficient for tea grade classification. Deng et al used the RF model to discriminate Xihulongjing tea from other regions with an accuracy of 97.6% and correctly identified green tea from surrounding regions with an accuracy of 97.9% [ 26 ]. Wang et al, based on the joint information from the NIR and UV-Vis spectra, established a successful classification model with RF.…”

Section: Discussionmentioning

confidence: 99%

Rapid Identification of Different Grades of Huangshan Maofeng Tea Using Ultraviolet Spectrum and Color Difference

et al. 2020

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Tea is an important beverage in humans’ daily lives. For a long time, tea grade identification relied on sensory evaluation, which requires professional knowledge, so is difficult and troublesome for laypersons. Tea chemical component detection usually involves a series of procedures and multiple steps to obtain the final results. As such, a simple, rapid, and reliable method to judge the quality of tea is needed. Here, we propose a quick method that combines ultraviolet (UV) spectra and color difference to classify tea. The operations are simple and do not involve complex pretreatment. Each method requires only a few seconds for sample detection. In this study, famous Chinese green tea, Huangshan Maofeng, was selected. The traditional detection results of tea chemical components could not be used to directly determine tea grade. Then, digital instrument methods, UV spectrometry and colorimetry, were applied. The principal component analysis (PCA) plots of the single and combined signals of these two instruments showed that samples could be arranged according to grade. The combined signal PCA plot performed better with the sample grade descending in clockwise order. For grade prediction, the random forest (RF) model produced a better effect than the support vector machine (SVM) and the SVM + RF model. In the RF model, the training and testing accuracies of the combined signal were all 1. The grades of all samples were correctly predicted. From the above, the UV spectrum combined with color difference can be used to quickly and accurately classify the grade of Huangshan Maofeng tea. This method considerably increases the convenience of tea grade identification.

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“…Zhou et al [ 60 ] use RF for small and unbalanced datasets to create a risk prediction model for decision-making tool. Deng et al [ 61 ] propose an authentication method for protecting high-value food products by RF. The forecast for agricultural products by RF is proposed by [ 62 ].…”

Section: Related Workmentioning

confidence: 99%

“…At each instant n, the action probability vector pi(n) is updated by the linear learning algorithm given in equation ( 13) if the chosen action ai(k) is rewarded by the environment, and it is updated according to equation ( 14) if the chosen action is penalized [104]. [11], [12], [13] Global problem [26], [27], [28] Healthcare [32], [33], [34], [35], [36], [41], [98], [37], [39], [40], [42], [43], [45], [46], [47], [48], [49], [50], [51], Industrial [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62] Network [63], [67], [68], [69], [99], [100] Physics [71], [72] Text processing …”

Section: Learning Automatamentioning

confidence: 99%

A New Random Forest Algorithm Based on Learning Automata

Savargiv

Masoumi

Keyvanpour

2021

Computational Intelligence and Neuroscience

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The goal of aggregating the base classifiers is to achieve an aggregated classifier that has a higher resolution than individual classifiers. Random forest is one of the types of ensemble learning methods that have been considered more than other ensemble learning methods due to its simple structure, ease of understanding, as well as higher efficiency than similar methods. The ability and efficiency of classical methods are always influenced by the data. The capabilities of independence from the data domain, and the ability to adapt to problem space conditions, are the most challenging issues about the different types of classifiers. In this paper, a method based on learning automata is presented, through which the adaptive capabilities of the problem space, as well as the independence of the data domain, are added to the random forest to increase its efficiency. Using the idea of reinforcement learning in the random forest has made it possible to address issues with data that have a dynamic behaviour. Dynamic behaviour refers to the variability in the behaviour of a data sample in different domains. Therefore, to evaluate the proposed method, and to create an environment with dynamic behaviour, different domains of data have been considered. In the proposed method, the idea is added to the random forest using learning automata. The reason for this choice is the simple structure of the learning automata and the compatibility of the learning automata with the problem space. The evaluation results confirm the improvement of random forest efficiency.

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Predictive geographical authentication of green tea with protected designation of origin using a random forest model

Cited by 72 publications

References 49 publications

Use of mineral multi‐elemental analysis to authenticate geographical origin of different cultivars of tea in Guizhou, China

Use of mineral multi‐elemental analysis to authenticate geographical origin of different cultivars of tea in Guizhou, China

Rapid Identification of Different Grades of Huangshan Maofeng Tea Using Ultraviolet Spectrum and Color Difference

A New Random Forest Algorithm Based on Learning Automata

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