Geographical origin traceability of tea based on multi-element spatial distribution and the relationship with soil in district scale

Li, Lei; Wen, Bo; Zhang, Xiaolei; Zhao, Yue; Duan, Yu; Song, Xiangfei; Ren, Shuang; Wang, Yuhua; Fang, Wanping; Zhu, Xujun

doi:10.1016/j.foodcont.2018.02.031

Cited by 47 publications

(23 citation statements)

References 35 publications

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“…By comparison, greater concentrations of Al (896 mg kg −1 ), Ba (14 mg kg −1 ), Ca (4316 mg kg −1 ), and Sr (15.7 mg kg −1 ) and lower concentrations of Fe (77 mg kg −1 ), Zn (21 mg kg −1 ), Ni (3.2 mg kg −1 ), and P (2940 mg·kg −1 ) were observed in tea leaves from Taiwan as compared to those from the three geographic origins that were investigated here . The concentrations of Zn (52.4 mg kg −1 ), Cr (0.81 mg kg −1 ), and Pb (3.87 mg kg −1 ) in tea leaves from Lishui in Nanjing, China, were greater and those of Ca (2745 mg kg −1 ) and K (15 394 mg kg −1 ) were lower in comparison to those from the three geographical origins from Guizhou investigated here . The standard deviations of concentrations of some elements (e.g., Cs, Co, Ce, Cr, Mn, and Y) in tea leaves with the same origins were high, indicating that great spatial variation existed for these elements in tea leaves with the same origin.…”

Section: Resultsmentioning

confidence: 72%

“…Only the mean concentrations of Cd and Cu in soils from Pu'an exceeded their risk screening values with regard to the Chinese standard for soil contamination of agricultural land . By contrast, the concentrations of Ni (31.6 mg kg −1 ), Mn (774 mg kg −1 ), Pb (34.5 mg kg −1 ), and Ca (2577 mg kg −1 ) in soils from Lishui of Nanjing of China were greater, and the concentrations of Al (45 357 mg kg −1 ) and K (5259 mg kg −1 ) were lower than those from the three locations that were studied . The lower concentrations of Cd and Zn and the greater concentration of Pb in soils from the three locations were observed in comparison to those from north Iran (0.36 mg kg −1 for Cd, 121.8 mg kg −1 for Zn, and 11.4 mg kg −1 for Pb) .…”

Section: Resultsmentioning

confidence: 72%

“…Some trace metals from sources other than the soil (e.g., pesticides, organic fertilizers, and chemical fertilizers) may be absorbed by the plant . By comparison, significantly positive correlations were reported between the concentrations of Mn and Ca in tea leaves and in the soils . A negative correlation was observed for the concentrations of Cr between tea leaves and corresponding soils .…”

Section: Resultsmentioning

confidence: 95%

“…Principal component analysis, a multivariate statistical tool, has been used widely to authenticate the geographical origins of agro‐products . In particular, it has usually been performed to gain the loadings of variables and the scores of observations .…”

Section: Resultsmentioning

confidence: 99%

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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: 72%

Section: Resultsmentioning

confidence: 72%

Section: Resultsmentioning

confidence: 95%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

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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“…They are accurate, sensitive, stable, and easy to operate . Recently, these multi‐element analyses have been applied in geographical authenticity studies of several plant‐derived products, such as table grapes, cherries, rice, tea, kiwi fruit, and wine, with high discrimination rates. However, until now, few studies have reported on the geographical origin of Chinese apples.…”

Section: Introductionmentioning

confidence: 99%

Geographical origin of Chinese apples based on multiple element analysis

et al. 2019

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BACKGROUND Apples from different regions of China show different qualities and internal characteristics, and appeal to different customers. However, these aspects have not been studied in depth. We characterized the profiles of 14 elements in 317 apple samples collected from five regions of China. Principal component analysis (PCA), linear discriminant analysis (LDA), and back‐propagation artificial neural networks analysis (BP‐ANN) were used to build models for apple authentication. RESULTS Fourteen elements were successfully identified in apple samples by performing graphite furnace atomic absorption spectrometry (GFAAS) and inductively coupled plasma atomic emission spectroscopy (ICP‐AES) analyses. Comparative analysis showed significantly different element profiles in samples from different regions. The first five principal components obtained by PCA accounted for 71.8% of the total variance. The LDA obtained 70.0% classification rates. The BP‐ANN obtained 82.7% classification rates. CONCLUSION This study indicated the possibility that apples could be authenticated based on differences in their element profiles, and provided a basis for further geographical origin studies. © 2019 Society of Chemical Industry

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Using artificial neural network in predicting the key fruit quality of loquat

Huang

Wang²,

et al. 2021

Food Science & Nutrition

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The formation and regulation of loquat fruit quality have always been an important research field to improve fruit quality, commodities, and market value. Fruit size, soluble solids content, and titratable acid content represent the most important quality factors in loquat. Mineral nutrients in abundance or deficiency are among the most important key factor that affect fruit quality. In the present study, we use artificial neural network (ANN) to explore the effects of mineral nutrients in soil and leaves on the key fruit quality of loquat. The results show that the ANN model with the structure of 12–12–1 can predict the single fruit weight with the highest accuracy (R2 = .91), the ANN model with the structure of 10–11–1 can predict the soluble solid content with the highest accuracy (R2 = .91), and the ANN model with the structure of 9–10–1 can predict the titratable acid content with the highest accuracy (R2 = .95). Meanwhile, we also conduct sensitivity analysis to analyze the relative contribution of mineral nutrients in soils and leaves to determine of the key fruit quality. In terms of relative contribution, Ca, Fe, and Mg content in soils and Zn, K, and Ca content in leaves contribute relatively largely to a single fruit weight, Mn, Fe, and Mg content in soils and the N content in leaves contribute relatively largely to the soluble solid content, and the P, Ca, N, Mg, and Fe in leaves contribute relatively largely to the titratable acid content of loquat. The established artificial neural network prediction models can improve the quality of loquat fruit by optimizing the content of mineral elements in soils and leaves.

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Geographical origin traceability of tea based on multi-element spatial distribution and the relationship with soil in district scale

Cited by 47 publications

References 35 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

Geographical origin of Chinese apples based on multiple element analysis

Using artificial neural network in predicting the key fruit quality of loquat

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