Evaluation of the physiochemical and metabolite of different region coffee beans by using UHPLC-QE-MS untargeted-metabonomics approaches

Miao, Yue; Zou, Qingfei; Wang, Qiuping; Gong, Jiashun; Tan, Chao; Peng, Chunxiu; ZeLin, Li

doi:10.1016/j.fbio.2022.101561

Cited by 15 publications

(7 citation statements)

References 24 publications

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“…Tyrosine, tryptophan, and phenylalanine are involved in protein synthesis and are precursors of many natural products, such as alkaloids and cell wall components. Tryptophan is a precursor of several secondary metabolites such as glucosinolates and alkaloids, which enhance the biosynthesis of aromatic compounds ( Miao et al., 2022 ). In addition to their bioactive composition, the volatiles of edible and nonedible Ferula species differ based on the type of organic acid; however, in terms of bioactive composition, nonedible F. feurlaeoides and edible Ferula have similar biosynthetic pathways and produce similar metabolites.…”

Section: Discussionmentioning

confidence: 99%

Quality evaluation of four Ferula plants and identification of their key volatiles based on non-targeted metabolomics

Jiang,

Peng,

et al. 2024

Front. Plant Sci.

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IntroductionFerula is a traditional, edible, and important medicinal plant with high economic value. The distinction between edible and non-edible Ferula remains unclear.MethodsIn this study, headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry (HS-SPME/GC-MS) and ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) non-targeted metabolomics techniques were used to systematically and comprehensively analyse secondary metabolites in the leaves and roots of four species of Ferula, considering their edibility.ResultsA total of 166 leaf volatile organic compounds (VOCs) and 1,079 root metabolites were identified. Additionally, 42 potential VOCs and 62 differential root metabolites were screened to distinguish between edible and non-edible Ferula. Twelve volatile metabolites were specific to F. feurlaeoides, and eight compounds were specific to the three edible Ferula species. The results showed that compounds containing sulphur, aldehydes, and ketones, which produce pungent odours, were the primary sources of the strong odour of Ferula. The root differential metabolites include 13 categories, among which the high concentration group is organic acids, amino acids, terpenoids and fatty acids. The bioactive metabolites and VOCs in the roots exhibited species-specific characteristics. VOCs with various odors were linked to the distribution of root metabolites in both edible and non-edible Ferula plants. The screened root markers may contribute to the formation of characteristic VOCs.DiscussionThis study identified the difference in flavour between edible and non-edible Ferula plants and, for the first time, demonstrated the contribution of the efficacy of Ferula root to the unique flavour of the above-ground parts of Ferula. These results provide a theoretical basis for selecting Ferula for consumption and help evaluate the quality of different species of Ferula. Our findings may facilitate food processing and the further development of Ferula.

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Section: Discussionmentioning

confidence: 99%

Quality evaluation of four Ferula plants and identification of their key volatiles based on non-targeted metabolomics

Jiang,

Peng,

et al. 2024

Front. Plant Sci.

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Section: Authenti C Ati On Of G Reen Coffee Throug H Foodomi C S Appr...mentioning

confidence: 99%

“…The results revealed excellent discrimination between different geographical origins of green coffee beans, highlighting lipid, C24:0, C22:0, C18:3, C17:0, C18:0, C20:0, C16:0, and protein as discriminatory features. Miao et al (2022) used an untargeted metabolomic approach based on UHPLC‐QE‐MS combined with fingerprint analysis to reveal an effective method for differentiating coffee beans from 18 green coffee bean samples sourced from Chinese importing companies and chosen to represent a variety of geographical origins. Likewise, Demianová et al (2022) recently reported that arabica coffee volatiles correctly identified 100% of testing samples and predicted an accuracy of 86.96% in cross‐validation.…”

Section: Authentication Of Green Coffee Through Foodomics Approachmentioning

confidence: 99%

Metabolomics: A suitable foodomics approach to the geographical origin traceability of Ethiopian Arabica specialty coffees

Urugo

Tola

Kebede

et al. 2023

Food Science & Nutrition

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Coffee, next to petroleum, is the most widely consumed and traded commodity internationally (ICO, 2015;Núñez et al., 2020). Coffea arabica (Arabica) and Coffea canephora (Robusta) are the two most dominant coffee species with economic and commercial importance globally (Nunez et al., 2021;Núñez et al., 2020). Mainly, Coffea arabica, which originated in Ethiopia, accounts for 70% of the world's coffee production, and it is considered a quality bean for its high sensory properties and has a special price in the world coffee market

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“…Other studies used better data modeling, such as supervised machine learning. Several algorithms can provide insights into feature selection, such as Linear Discriminant Analysis (LDA) [18,36,76], Partial Least Square (PLS)-Discriminant Analysis (DA) [51,77], PLS Regression [58], Orthogonal PLS-DA [78,29,32], Random Forest (RF) [53], support vector machine (SVM) [61,74,79], and k-nearest neighbour (k-NN) [74,79].…”

Section: Multivariate Model and Data Analysismentioning

confidence: 99%

Coffee Origin Determination Based on Analytical and Nondestructive Approaches –A Systematic Literature Review

Aurum

Imaizumi

Thammawong

et al. 2022

RAS

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Coffee attracts consumers worldwide for its unique sensorial properties. Its unique flavor is affected by numerous factors. The biochemical properties associated with geographical features are among the essential aspects that may modulate coffee's distinct sensorial profiles, and may be employed for its origin determination and authentication. This systematic literature review served to assess the newest techniques for coffee authentication, origin determination, and adulteration detection from analytical and nondestructive approaches. This study focused on the last 10 years' high-quality research in the field. Accordingly, 78 articles using both analytical and nondestructive methods for the determination of coffee origin and fraud detection were found in leading journal databases. Apart from the compound profile and instrumentation, data analyses including statistics, machine learning, and multivariate models which have been commonly used are discussed as well. In addition, other important information, such as data validation methods and the predictive capability of the above techniques are also reflected in this review.

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Evaluation of the physiochemical and metabolite of different region coffee beans by using UHPLC-QE-MS untargeted-metabonomics approaches

Cited by 15 publications

References 24 publications

Quality evaluation of four Ferula plants and identification of their key volatiles based on non-targeted metabolomics

Quality evaluation of four Ferula plants and identification of their key volatiles based on non-targeted metabolomics

Metabolomics: A suitable foodomics approach to the geographical origin traceability of Ethiopian Arabica specialty coffees

Coffee Origin Determination Based on Analytical and Nondestructive Approaches –A Systematic Literature Review

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