Black tea volatiles fingerprinting by comprehensive two-dimensional gas chromatography – Mass spectrometry combined with high concentration capacity sample preparation techniques: Toward a fully automated sensomic assessment

Magagna, Federico; Cordero, Chiara Emilia Irma; Cagliero, Cecilia; Liberto, Erica; Sgorbini, Barbara; Bicchi, Carlo

doi:10.1016/j.foodchem.2017.01.003

Cited by 69 publications

(27 citation statements)

References 34 publications

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“…At present, HS‐SPME/GC‐MS is being successfully applied to trace of agricultural products such as wine . Likewise, HS‐SPME/GC‐MS has also been thoroughly effective with respect to identifying tea volatility . In particular, some other studies have used HS‐SPME/GC‐MS to distinguish oolong tea, as well as other tea varieties; in addition, it helps to improve the degree of fermentation of the same tea …”

Section: Introductionmentioning

confidence: 99%

Identification of geographical origin of Keemun black tea based on its volatile composition coupled with multivariate statistical analyses

et al. 2019

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BACKGROUND: Keemun black tea (KBT) is one of the most popular tea beverages in China as a result of its unique flavor and potential health benefits. The geographical origin of KBT influences its quality and price. The present study aimed to apply a head-space solid phase microextraction approach and gas chromatography-mass spectrometry combined with chemometric analysis to profile the volatile compounds of KBT collected from five production areas.RESULTS: Thirty-one peaks were detected in 61 KBT samples. Hierarchical cluster analysis, principal component analysis (PCA), k-nearest neighbor (k-NN) and stepwise linear discriminant analysis (SLDA) were employed to visualize the volatile fractions. The results of unsupervised statistical tools were compared using a test for similarities and distinctions, which showed that different sources may be associated. A satisfying combination of average recognition (91.7%) and cross-validation prediction abilities (84.6%) was obtained for the PCA-k-NN. Among all of the statistical tools, SLDA provided promising results, with 100% recognition and 96.4% prediction ability. CONCLUSION:The results obtained in the present study indicate that the volatile compounds can be used as indicators to identify the geographical origin of KBT.

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Section: Introductionmentioning

confidence: 99%

Identification of geographical origin of Keemun black tea based on its volatile composition coupled with multivariate statistical analyses

et al. 2019

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“…Tea is one of the most popular beverages in the world owing to its attractive aroma, taste, and health benefits [ 1 , 2 ]. According to the way of processing, teas are usually classified into three big groups based on their fermentation degrees: non-fermented (green and white), semi-fermented (oolong) and fully fermented (black tea including pu-erh tea) [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis

et al. 2018

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In the present work, a novel infrared-assisted extraction coupled to headspace solid-phase microextraction (IRAE-HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) was developed for rapid determination of the volatile components in green tea. The extraction parameters such as fiber type, sample amount, infrared power, extraction time, and infrared lamp distance were optimized by orthogonal experimental design. Under optimum conditions, a total of 82 volatile compounds in 21 green tea samples from different geographical origins were identified. Compared with classical water-bath heating, the proposed technique has remarkable advantages of considerably reducing the analytical time and high efficiency. In addition, an effective classification of green teas based on their volatile profiles was achieved by partial least square-discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). Furthermore, the application of a dual criterion based on the variable importance in the projection (VIP) values of the PLS-DA models and on the category from one-way univariate analysis (ANOVA) allowed the identification of 12 potential volatile markers, which were considered to make the most important contribution to the discrimination of the samples. The results suggest that IRAE-HS-SPME/GC-MS technique combined with multivariate analysis offers a valuable tool to assess geographical traceability of different tea varieties.

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“…Similarly, for non‐alcoholic beverages such as tea and coffee, HS‐SPME has been extensively applied for the analysis of infusions or dried plant material [82–84]. Nevertheless, also the direct immersion approach (DI‐SPME) [82], simultaneous distillation extraction (SDE) [85], and derivatization [86] have been reported.…”

Section: Non‐targeted Analysis Applicationsmentioning

confidence: 99%

The role of sample preparation in multidimensional gas chromatographic separations for non‐targeted analysis with the focus on recent biomedical, food, and plant applications

Franchina

Zanella

Dubois

et al. 2020

J of Separation Science

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In this review, we consider and discuss the affinity and complementarity between a generic sample preparation technique and the comprehensive two‐dimensional gas chromatography process. From the initial technical development focus (e.g., on the GC×GC and solid‐phase microextraction techniques), the trend is inevitably shifting toward more applied challenges, and therefore, the preparation of the sample should be carefully considered in any GC×GC separation for an overreaching research. We highlight recent biomedical, food, and plant applications (2016–July 2020), and specifically those in which the combination of tailored sample preparation methods and GC×GC–MS has proven to be beneficial in the challenging aspects of non‐targeted analysis. Specifically on the sample preparation, we report on gas‐phase, solid‐phase, and liquid‐phase extractions, and derivatization procedures that have been used to extract and prepare volatile and semi‐volatile metabolites for the successive GC×GC analysis. Moreover, we also present a milestone section reporting the early works that pioneered the combination of sample preparation techniques with GC×GC for non‐targeted analysis.

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Black tea volatiles fingerprinting by comprehensive two-dimensional gas chromatography – Mass spectrometry combined with high concentration capacity sample preparation techniques: Toward a fully automated sensomic assessment

Cited by 69 publications

References 34 publications

Identification of geographical origin of Keemun black tea based on its volatile composition coupled with multivariate statistical analyses

Identification of geographical origin of Keemun black tea based on its volatile composition coupled with multivariate statistical analyses

Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis

The role of sample preparation in multidimensional gas chromatographic separations for non‐targeted analysis with the focus on recent biomedical, food, and plant applications

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