Tracing the Geographical Origin of Rice Based on Mineral Elements and GSA-SVM

Background Place of origin is an important factor when determining the quality and authenticity of Angelica sinensis for medicinal use. It is important to trace the origin and confirm the regional characteristics of medicinal products for sustainable industrial development. Effectively tracing and confirming the material’s origin may be accomplished by detecting stable isotopes and mineral elements. Methods We studied 25 A. sinensis samples collected from three main producing areas (Linxia, Gannan, and Dingxi) in southeastern Gansu Province, China, to better identify its origin. We used inductively coupled plasma mass spectrometry (ICP-MS) and stable isotope ratio mass spectrometry (IRMS) to determine eight mineral elements (K, Mg, Ca, Zn, Cu, Mn, Cr, Al) and three stable isotopes (δ13C, δ15N, δ18O). Principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were used to verify the validity of its geographical origin. Results K, Ca/Al, δ13C, δ15N and δ18O are important elements to distinguish A. sinensis sampled from Linxia, Gannan and Dingxi. We used an unsupervised PCA model to determine the dimensionality reduction of mineral elements and stable isotopes, which could distinguish the A. sinensis from Linxia. However, it could not easily distinguish A. sinensis sampled from Gannan and Dingxi. The supervised PLS-DA and LDA models could effectively distinguish samples taken from all three regions and perform cross-validation. The cross-validation accuracy of PLS-DA using mineral elements and stable isotopes was 84%, which was higher than LDA using mineral elements and stable isotopes. Conclusions The PLS-DA and LDA models provide a theoretical basis for tracing the origin of A. sinensis in three regions (Linxia, Gannan and Dingxi). This is significant for protecting consumers’ health, rights and interests.

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Effects of fertilizers and pesticides on the mineral elements used for the geographical origin traceability of rice

Journal of Food Composition and Analysis ◽

10.1016/j.jfca.2019.103276 ◽

2019 ◽

Vol 83 ◽

pp. 103276 ◽

Cited By ~ 1

Author(s):

Lili Qian ◽

Caidong Zhang ◽

Feng Zuo ◽

Lina Zheng ◽

Dan Li ◽

...

Keyword(s):

Geographical Origin ◽

Mineral Elements ◽

Geographical Origin Traceability

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Multielemental Analysis Associated with Chemometric Techniques for Geographical Origin Discrimination of Tea Leaves (Camelia sinensis) in Guizhou Province, SW China

Molecules ◽

10.3390/molecules23113013 ◽

2018 ◽

Vol 23 (11) ◽

pp. 3013 ◽

Cited By ~ 15

Author(s):

Jian Zhang ◽

Ruidong Yang ◽

Rong Chen ◽

Yuncong Li ◽

Yishu Peng ◽

...

Keyword(s):

Discriminant Analysis ◽

Geographical Origin ◽

Principal Component ◽

Mineral Elements ◽

Guizhou Province ◽

Correct Classification ◽

Tea Leaves ◽

Linear Discriminant ◽

Discriminant Models ◽

Origin Identification

This study aimed to construct objective and accurate geographical discriminant models for tea leaves based on multielement concentrations in combination with chemometrics tools. Forty mineral elements in 87 tea samples from three growing regions in Guizhou Province (China), namely Meitan and Fenggang (MTFG), Anshun (AS) and Leishan (LS) were analyzed. Chemometrics evaluations were conducted using a one-way analysis of variance (ANOVA), principal component analysis (PCA), linear discriminant analysis (LDA), and orthogonal partial least squares discriminant analysis (OPLS-DA). The results showed that the concentrations of the 28 elements were significantly different among the three regions (p < 0.05). The correct classification rates for the 87 tea samples were 98.9% for LDA and 100% for OPLS-DA. The variable importance in the projection (VIP) values ranged between 1.01–1.73 for 11 elements (Sb, Pb, K, As, S, Bi, U, P, Ca, Na, and Cr), which can be used as important indicators for geographical origin identification of tea samples. In conclusion, multielement analysis coupled with chemometrics can be useful for geographical origin identification of tea leaves.

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Fingerprint Approaches Coupled with Chemometrics to Discriminate Geographic Origin of Imported Salmon in China’s Consumer Market

Foods ◽

10.3390/foods10122986 ◽

2021 ◽

Vol 10 (12) ◽

pp. 2986

Author(s):

Xianshu Fu ◽

Xuezhen Hong ◽

Jinyan Liao ◽

Qingge Ji ◽

Chaofeng Li ◽

...

Keyword(s):

Near Infrared ◽

Geographical Origin ◽

Geographic Origin ◽

Nir Spectroscopy ◽

Mineral Elements ◽

Mineral Element ◽

Consumer Market ◽

Linear Discriminant ◽

Discrimination Rate ◽

Rapid Discrimination

Of the salmon sold in China’s consumer market, 92% was labelled as Norwegian salmon, but was in fact was mainly imported from Chile. The aim of this study was to establish an effective method for discriminating the geographic origin of imported salmon using two fingerprint approaches, Near-infrared (NIR) spectroscopy and mineral element fingerprint (MEF). In total, 80 salmon (40 from Norway and 40 from Chile) were tested, and data generated by NIR and MEF were analysed via various chemometrics. Four spectral preprocessing methods, including vector normalization (VN), Savitzky Golay (SG) smoothing, first derivative (FD) and second derivative (SD), were employed on the raw NIR data, and a partial least squares (PLS) model based on the FD + SG9 pretreatment could successfully differentiate Norwegian salmons from Chilean salmons, with a R2 value of 98.5%. Analysis of variance (ANOVA) and multiple comparative analysis were employed on the contents of 16 mineral elements including Pb, Fe, Cu, Zn, Al, Sr, Ni, As, Cr, V, Se, Mn, K, Ca, Na and Mg. The results showed that Fe, Zn, Al, Ni, As, Cr, V, Se, Ca and Na could be used as characteristic elements to discriminate the geographical origin of the imported salmon, and the discrimination rate of the linear discriminant analysis (LDA) model, trained on the above 10 elements, could reach up to 98.8%. The results demonstrate that both NIR and MEF could be effective tools for the rapid discrimination of geographic origin of imported salmon in China’s consumer market.

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