The classification of tea according to region of origin using pattern recognition techniques and trace metal data

Moreda-Piñeiro, Antonio; Fisher, Andrew; Hill, Steve J.

doi:10.1016/s0889-1575(02)00163-1

Cited by 197 publications

(109 citation statements)

References 13 publications

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“…This fingerprint can be established using samples organic, [8][9][10][11] inorganic 6,[12][13][14][15][16][17][18][19] or isotopic [20][21][22][23][24] profiles which are related to the plant growth conditions such as climatic conditions and elements availability in the soil. Although each one of these strategies provide very interesting and powerful results, the use of a set of variables independents from each other would be able to provides complementary information which could make it a distinct feature in the sourcing their origins.…”

Section: Introductionmentioning

confidence: 99%

“…This feature allows the using of plant samples as environmental pollution biomonitor, hence they can appear as element accumulator, mainly for toxic metals. 26,27 There are many articles in the literature that show the feasibility of using these chemical profile to source the origin of agricultural materials such as wine, 18 wheat, 19 tea 17,28 and barley 25 however data related to Cannabis samples are still scarce.…”

Section: Introductionmentioning

confidence: 99%

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Multivariate classification based on chemical and stable isotopic profiles in sourcing the origin of marijuana samples seized in Brazil

Shibuya¹,

Sarkis²,

Neto³

et al. 2007

J. Braz. Chem. Soc.

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Este trabalho teve como principal objetivo verificar as diferenças existentes na composição elementar de amostras de Cannabis, apreendidas em diferentes regiões produtoras do país, previamente separadas através da análise de isótopos estáveis de carbono e nitrogênio, e utilizar estas diferenças para classificar as amostras de acordo com sua origem geográfica. A técnica analítica empregada foi a de espectrometria de massas de dupla focalização com fonte de plasma acoplado indutivamente (HR-ICP-MS), e a avaliação dos dados foi realizada utilizando-se a análise de agrupamentos (hierarchical cluster analysis) e de discriminantes linear. Os resultados indicam que amostras provenientes de regiões diferentes apresentam perfis químicos diferentes e estes por sua vez parecem estar relacionadas com as características geológicas das regiões consideradas. Os parâmetros mais importantes nesta avaliação foram os isótopos estáveis de carbono e nitrogênio e os níveis de cobre, cobalto, bário, lantânio, zinco, ferro, ítrio e manganês. Este trabalho demonstrou a possibilidade de se utilizar amostras apreendidas nas ruas para a criação de um banco de dados para o monitoramento da maconha consumida no país.The main task of this work was to verify the existing differences in the elemental composition of samples seized in the main Brazilian regions of marijuana production, previously separated by their carbon and nitrogen stable isotope results, and to use these differences to classify the samples according to their geographical origins. The sector field inductively coupled plasma mass spectrometry technique (HR-ICP-MS) was the analytical technique applied and the data analysis was performed using hierarchical cluster and linear discriminant analysis. Results indicate that samples from different regions present different chemical profiles that appear in accordance to the geological characteristics of each region. The most relevant parameters were carbon and nitrogen stable isotopes, copper, cobalt, barium, lanthanum, zinc, iron, yttrium and manganese. An interesting product of this work was to demonstrate the possibility of using seized samples to create a national databank for monitoring the geographical origin of marijuana consumed in the country.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multivariate classification based on chemical and stable isotopic profiles in sourcing the origin of marijuana samples seized in Brazil

Shibuya¹,

Sarkis²,

Neto³

et al. 2007

J. Braz. Chem. Soc.

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“…This procedure was used as it extracts the dominant patterns in the data and permits us to find the correlation structure of the variables and investigating how many components are necessary to explain the greater part of variance with a minimum loss of information. Several techniques other than TLS have been used in conjunction with PCA to discriminate between teas [2,11,14,17,18]. Electronic nose and electronic tongues have been use in conjunction with PCA for food applications without having to know the specific compounds responsible for differences [17,18,29,30].…”

Section: Resultsmentioning

confidence: 99%

“…Teas have been classified according to their metal content [2,11,12], chemical composition and colour difference [13] volatile components [14], physico chemical properties [15], Fourier transform infrared spectroscopy [16], and also using an electronic nose [17] and electronic tongue [18].…”

Section: Introductionmentioning

confidence: 99%

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Discrimination of teas based on total luminescence spectroscopy and pattern recognition

et al. 2006

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This paper reports on the application of total luminescence spectroscopy to discriminate between seven different types of tea. Bottled liquid teas; Oolong tea, Green tea, Houji tea, Japanese black tea and tea leaves (Kenya tea, Assam tea and Ceylon tea). Total luminescence spectra were recorded by measuring the emission spectra in the range 300nmto 700 nm at excitation wavelengths from 250 nm to 590nm. Discrimination of teas based on their spectral data was performed by principal component analysis (PCA) a common chemometric technique used for data reduction and visualisation. The results demonstrate the ability of fluorescence techniques to differentiate between green tea (Unfermented), black tea (fermented) and oolong tea (partially fermented).

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Elemental composition analysis of food by FAES and ICP‐OES

Martínez

Gil

Pacheco

et al. 2015

Handbook of Mineral Elements in Food

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The classification of tea according to region of origin using pattern recognition techniques and trace metal data

Cited by 197 publications

References 13 publications

Multivariate classification based on chemical and stable isotopic profiles in sourcing the origin of marijuana samples seized in Brazil

Multivariate classification based on chemical and stable isotopic profiles in sourcing the origin of marijuana samples seized in Brazil

Discrimination of teas based on total luminescence spectroscopy and pattern recognition

Elemental composition analysis of food by FAES and ICP‐OES

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