Keemun black tea: Tracing its narrow-geographic origins using comprehensive elemental fingerprinting and chemometrics

Ren, Yin‐feng; Feng, Chao; Ye, Zhi-hao; Zhu, Hucheng; Hou, Ruyan; Granato, Daniel; Cai, Hao; Peng, Chuanyi

doi:10.1016/j.foodcont.2021.108614

Cited by 20 publications

(5 citation statements)

References 32 publications

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“…It is important to highlight that several studies have indicated that machine learningbased analysis and supervised methods exhibit high discrimination performance, particularly in the context of food authentication. These methods include Random Forest (RF), Support Vector Machines (SVM), Feedforward Neural Networks (FNN), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), Linear Discriminant Analysis (LDA) [68,69]. These techniques have been shown to enhance the effectiveness of PCA in various applications [68,70].…”

Section: Classification Of Honeys By Multivariate Techniquesmentioning

confidence: 99%

“…These methods include Random Forest (RF), Support Vector Machines (SVM), Feedforward Neural Networks (FNN), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), Linear Discriminant Analysis (LDA) [68,69]. These techniques have been shown to enhance the effectiveness of PCA in various applications [68,70].…”

Section: Classification Of Honeys By Multivariate Techniquesmentioning

confidence: 99%

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Honey Origin Authentication via Mineral Profiling Combined with Chemometric Approaches

Pavlin

Kočar

Imperl

et al. 2023

Foods

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In the present study, the potential of elemental analysis combined with statistical tools to identify honey origin was evaluated by mineral characterization of 173 honeys of 13 floral types (acacia, fir, spruce, linden, chestnut, lavender, coriander, thistle, honeydew, rosemary, sage, euphorbia and ziziphus plant species) collected from five geographical regions (Slovenia, Croatia, Bulgaria, Turkey, and Morocco). The objective of the study was to accurately and reliably differentiate the mineral composition among honey varieties. The aim was to establish traceability, to ensure product authenticity and to improve quality control measures within the honey industry. For this purpose, 18 major, minor and trace elements were quantified using microwave digestion, followed by ICP-MS measurement. Statistical evaluation of elemental concentration was undertaken using principal component analysis (PCA) to distinguish honey floral types. The research give light on the specific elements that can serve as indicators for determining the geographical and botanical source of honey. Our findings indicate that certain elements, such as Mn, K, and Ca, are primarily influenced by the type of pollen present in the honey, making them indicative of the floral source. On the other hand, levels of Na, Mg, and Fe were found to be more strongly influenced by environmental factors and can be considered as markers of geographical origin. One novel aspect of this research is the exploration of the relationship between honey minerals and honey botanical source. This was achieved through the analysis of chestnut tree samples and a subsequent comparison with the composition of chestnut honey.

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Section: Classification Of Honeys By Multivariate Techniquesmentioning

confidence: 99%

Section: Classification Of Honeys By Multivariate Techniquesmentioning

confidence: 99%

Honey Origin Authentication via Mineral Profiling Combined with Chemometric Approaches

Pavlin

Kočar

Imperl

et al. 2023

Foods

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“…The accuracy of the test set was improved from 86.98% to 95.35% by screening feature variables combined with an optimal classification algorithm. Compared with recent narrow regional tea traceability studies, 4,7,14,31,32 we collected the largest number of sample batches at 114 and covered almost the entire tea season and production area of TPHK (Table 4). In addition, the sample collection was entrusted to modern tea processing manufacturers in accordance with national standards to ensure the reliability and relevance of the analyzed samples.…”

Section: Model Comparison and Discussionmentioning

confidence: 99%

Rapid identification of the geographic origin of Taiping Houkui green tea using near‐infrared spectroscopy combined with a variable selection method

Jin

Cui

et al. 2022

J Sci Food Agric

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BACKGROUND Most studies focus on the geographically larger production areas in tea traceability. However, famous high‐quality tea is often produced in a narrow range of origins, which makes traceability a challenge. In this study, Taiping Houkui (TPHK) green tea of narrow geographical origin was rapidly identified using Fourier‐transform near‐infrared (FT‐NIR) spectroscopy. RESULTS First, spectral information of 114 TPHK samples from four production areas was acquired. Second, the synthetic minority over‐sampling technique (SMOTE) was used to balance the sample data set, and three different spectral pre‐processing methods were compared. Third, three feature variable selection algorithms were used to obtain the pre‐processed spectral features. Finally, extreme learning machine (ELM) models based on the variables obtained from the selected features were established to trace the TPHK origin. The optimized ELM model achieves 95.35% classification accuracy in the test set. CONCLUSION The present study demonstrates that the optimized variable selection method in combination with NIR spectroscopy represents a suitable strategy for tea traceability in narrow regions. © 2022 Society of Chemical Industry.

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“…Meanwhile, the quality and price of tea are usually judged by the sensory assessment of professional tea tasters, which is highly subjective and lacks unified objective data support [3]. The phenomenon of fraud in the tea trade has become more and more serious due to the huge profits and subjective evaluations [4], QTMJ tea is no exception. For instance, deliberate mislabeling of tea cultivars or counterfeiting with inferior tea cultivars often appear in QTMJ tea and damage consumer trust.…”

Section: Introductionmentioning

confidence: 99%

Varietal Authenticity Assessment of QTMJ Tea Using Non-Targeted Metabolomics and Multi-Elemental Analysis with Chemometrics

Liu,

Wu,

Zhao

et al. 2023

Foods

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In this paper, a combination of non-targeted metabolomics and multi-element analysis was used to investigate the impact of five different cultivars on the sensory quality of QTMJ tea and identify candidate markers for varietal authenticity assessment. With chemometric analysis, a total of 54 differential metabolites were screened, with the abundances significantly varied in the tea cultivars. By contrast, the QTMJ tea from the Yaoshan Xiulv (XL) monovariety presents a much better sensory quality as result of the relatively more abundant anthocyanin glycosides and the lower levels of 2′-o-methyladenosine, denudatine, kynurenic acid and L-pipecolic acid. In addition, multi-elemental analysis found 14 significantly differential elements among the cultivars (VIP > 1 and p < 0.05). The differences and correlations of metabolites and elemental signatures of QTMJ tea between five cultivars were discussed using a Pearson correlation analysis. Element characteristics can be used as the best discriminant index for different cultivars of QTMJT, with a predictive accuracy of 100%.

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Keemun black tea: Tracing its narrow-geographic origins using comprehensive elemental fingerprinting and chemometrics

Cited by 20 publications

References 32 publications

Honey Origin Authentication via Mineral Profiling Combined with Chemometric Approaches

Honey Origin Authentication via Mineral Profiling Combined with Chemometric Approaches

Rapid identification of the geographic origin of Taiping Houkui green tea using near‐infrared spectroscopy combined with a variable selection method

Varietal Authenticity Assessment of QTMJ Tea Using Non-Targeted Metabolomics and Multi-Elemental Analysis with Chemometrics

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