A comparative UHPLC-Q/TOF-MS-based metabolomics approach coupled with machine learning algorithms to differentiate Keemun black teas from narrow-geographic origins

Peng, Chuanyi; Ren, Yin‐feng; Ye, Zhi-hao; Zhu, Hucheng; Liu, Xiaoqian; Chen, Xiaotong; Hou, Ruyan; Granato, Daniel; Cai, Hao

doi:10.1016/j.foodres.2022.111512

Cited by 25 publications

(11 citation statements)

References 41 publications

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“…Zhao et al (2021) obtained the stability coefficients of soil-rock mixture slopes using FLAC 3D and strength reduction methods, revealing the influence of rock content on the stability coefficients and damage modes. Peng et al (2022) coupled discontinuous deformation analysis and smooth particle hydrodynamics to investigate the mechanical properties of soil-rock mixture slopes. Qiu et al (2022) using the physical deterministic model-Scoops3D model to analyze landslide stability.…”

Section: Introductionmentioning

confidence: 99%

Stability prediction for soil-rock mixture slopes based on a novel ensemble learning model

Zhang²,

Wang³

et al. 2023

Front. Earth Sci.

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Soil-rock mixtures are geological materials with complex physical and mechanical properties. Therefore, the stability prediction of soil-rock mixture slopes using machine learning methods is an important topic in the field of geological engineering. This study uses the soil-rock mixture slopes investigated in detail as the dataset. An intelligent optimization algorithm-weighted mean of vectors algorithm (INFO) is coupled with a machine learning algorithm. One of the new ensemble learning models, which named IN-Voting, is coupled with INFO and voting model. Twelve single machine learning models and sixteen novel IN-Voting ensemble learning models are built to predict the stability of soil-rock mixture slopes. Then, the prediction accuracies of the above models are compared and evaluated using three evaluation metrics: coefficient of determination (R2), mean square error (MSE), and mean absolute error (MAE). Finally, an IN-Voting ensemble learning model based on five weak learners is used as the final model for predicting the stability of soil-rock mixture slopes. This model is also used to analyze the importance of the input parameters. The results show that: 1) Among 12 single machine learning models for the stability prediction of soil-rock mixture slopes, MLP (Multilayer Perceptron) has the highest prediction accuracy. 2) The IN-Voting model has higher prediction accuracy than single machine learning models, with an accuracy of up to 0.9846) The structural factors affecting the stability of soil-rock mixture slopes in decreasing order are the rock content, bedrock inclination, slope height, and slope angle.

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Section: Introductionmentioning

confidence: 99%

Stability prediction for soil-rock mixture slopes based on a novel ensemble learning model

Zhang²,

Wang³

et al. 2023

Front. Earth Sci.

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“…Tea (Camellia sinensis) is one of the most widely consumed beverages in the world, due to an array of nutritional and health benefits. Regional provenance is among the most important attributes that tea consumers associate with high-quality tea products [4][5][6][7][8] . Many teas are named based on the geographic regions where they are grown.…”

Section: Introductionmentioning

confidence: 99%

“…However, both measurements require laborious sample preparation and are hence technically demanding. Metabolomics in conjunction with chemometrics has emerged as one of the most promising methods to distinguish tea origins because of a wide metabolite coverage, high sensitivity and high throughput [5][6][7][8]12,13 . In particular, gas chromatography-mass spectrometry (GC-MS), a technique well-recognized for its capability to simultaneously monitor a large number of volatile organic compounds (VOCs) with proven reproducibility, has become a central platform for tea metabolomics research [13][14][15][16][17][18] .…”

Section: Introductionmentioning

confidence: 99%

“…In particular, its surging popularity for food authentication, origin identification and quality control is amply demonstrated by the increasing number of publications on this topic over the past few years, highlighting the great potential for food inspection and classification [27][28][29][30][31][32] . Although metabolomics has been widely applied in tea research, the application of ML techniques in their analyses has just emerged 5,13 . Nonetheless, the sample sizes in existing studies are typically small.…”

Section: Introductionmentioning

confidence: 99%

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Metabolomics integrated with machine learning to discriminate the geographic origin of Rougui Wuyi rock tea

et al. 2023

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The geographic origin of agri-food products contributes greatly to their quality and market value. Here, we developed a robust method combining metabolomics and machine learning (ML) to authenticate the geographic origin of Wuyi rock tea, a premium oolong tea. The volatiles of 333 tea samples (174 from the core region and 159 from the non-core region) were profiled using gas chromatography time-of-flight mass spectrometry and a series of ML algorithms were tested. Wuyi rock tea from the two regions featured distinct aroma profiles. Multilayer Perceptron achieved the best performance with an average accuracy of 92.7% on the training data using 176 volatile features. The model was benchmarked with two independent test sets, showing over 90% accuracy. Gradient Boosting algorithm yielded the best accuracy (89.6%) when using only 30 volatile features. The proposed methodology holds great promise for its broader applications in identifying the geographic origins of other valuable agri-food products.

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“…They are now used to process a wide variety of analytical issues relating to food authenticity, food quality, and food safety. These include, for example, methods for verifying the geographical origin [2–7], the cultivation or the production method [8–10] as well as the biological identity [11–13]. In addition, knowledge about the influence of various exogenous factors on the composition of food or the detection of various contaminants or residues is increasingly important [14].…”

Section: Introductionmentioning

confidence: 99%

Food metabolomics: Latest hardware—developments for nontargeted food authenticity and food safety testing

Creydt¹,

Fischer²

2022

Electrophoresis

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The analytical requirements for food testing have increased significantly in recent years. On the one hand, because food fraud is becoming an ever-greater challenge worldwide, and on the other hand because food safety is often difficult to monitor due to the far-reaching trade chains. In addition, the expectations of consumers on the quality of food have increased, and they are demanding extensive information. Cutting-edge analytical methods are required to meet these demands. In this context, non-targeted metabolomics strategies using mass and nuclear magnetic resonance spectrometers (mass spectrometry [MS]) have proven to be very suitable. MS-based approaches are of particular importance as they provide a comparatively high analytical coverage of the metabolome.Accordingly, the efficiency to address even challenging issues is high. A variety of hardware developments, which are explained in this review, have contributed to these advances. In addition, the potential of future developments is highlighted, some of which are currently not yet commercially available or only used to a comparatively small extent but are expected to gain in importance in the coming years.

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A comparative UHPLC-Q/TOF-MS-based metabolomics approach coupled with machine learning algorithms to differentiate Keemun black teas from narrow-geographic origins

Cited by 25 publications

References 41 publications

Stability prediction for soil-rock mixture slopes based on a novel ensemble learning model

Stability prediction for soil-rock mixture slopes based on a novel ensemble learning model

Metabolomics integrated with machine learning to discriminate the geographic origin of Rougui Wuyi rock tea

Food metabolomics: Latest hardware—developments for nontargeted food authenticity and food safety testing

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