Journal of Food Quality

Journal of Food Quality / 2019 / Article

Research Article | Open Access

Volume 2019 |Article ID 8396865 |

Lili Qian, Feng Zuo, Hongyan Liu, Caidong Zhang, Xiaoxing Chi, Dongjie Zhang, "Determination of Geographical Origin of Wuchang Rice with the Geographical Indicator by Multielement Analysis", Journal of Food Quality, vol. 2019, Article ID 8396865, 7 pages, 2019.

Determination of Geographical Origin of Wuchang Rice with the Geographical Indicator by Multielement Analysis

Guest Editor: Rommel M. Barbosa
Received22 Jan 2019
Revised06 Mar 2019
Accepted12 Mar 2019
Published01 Apr 2019


The study aims to investigate whether the multielement analysis result can be used as a fingerprint to identify the geographical origin of Wuchang rice. The element contents of rice and soil samples from three regions in China (Wuchang, Qiqihar, and Jiamusi) were analyzed. The concentrations of 16 elements (Na, Mg, Al, K, Ca, V, Mn, Fe, Co, Cu, Zn, As, Rb, Sr, Cd, and Pb) in 194 rice samples and 112 soil samples from the harvest season in 2013 and 2014 were determined. The analysis of variance and linear discriminant analysis were performed to analyze the variation among regions and rice genotypes and classify the geographical origins of rice. Only the element of Cu showed significant differences among different genotypes. In the discriminant analysis, the overall correct identification rates of the rice samples obtained in 2013 and 2014 were, respectively, 96.6% and 89.6% and the overall correct identification rate for Wuchang rice reached 100%.

1. Introduction

Rice (Oryza sativa L.) is a staple food for nearly half of the world population. According to FAO statistics, the rice production of China is 144,850,000 metric tons and ranks first in the world. It is necessary to discriminate geographical origins of rice in order to prevent mislabeling and adulteration problems [1].

Wuchang is a city located in Heilongjiang Province of China, and its rice cultivation history is more than 200 years. Based on its good quality and taste, Wuchang rice has gained many titles, such as “Green Food,” “Organic Food,” “Chinese Famous Brand,” “Certified Products by American Food Nutrition Association,” and “Chinese Protected Designation of Origin”. Because of its limited production, Wuchang rice is frequently subjected to fraud practices, such as partial or total substitution by lower quality varieties [2].

Many techniques have been developed to distinguish cultivation areas of rice and other cereals [3], such as the stable isotope technique [49], near-infrared technique [10, 11], Raman spectroscopy [12], 1H-NMR spectroscopy [13], and multielement analysis [1416]. However, it is still difficult to discriminate the geographical origins, especially the adjacent production areas with the same climate type and similar geologic bedrock, because the differences in “fingerprints” are relatively small and the geographical traceability is more difficult [17, 18].

The elements in plants are mainly from the surroundings (water and soil). Due to the differences in hydrological characteristics and geological background, different plants have different element profiles, which provide the possibility of the geographical traceability for foodstuff including both plants and animals [19]. This technique had been applied to identify the geographical origins of cereals including wheat [16] and rice [1, 15, 20, 21]. The special quality and flavor of Wuchang rice come from its unique origin and climate. It is necessary to figure out the characteristics of element fingerprints of Wuchang rice in order to protect this famous brand.

In this study, we collected rice and soil samples from Wuchang and other two adjacent regions (Qiqihar and Jiamusi) in 2013 and 2014. Different genotypes of rice were also collected in order to investigate whether the differences in multielements among genotypes existed. Furthermore, the discrimination model was established and assessed by the selected elements displaying significant differences among three regions.

2. Materials and Methods

2.1. Sample Collection

Totally, 182 rice samples were collected in 2013 and 2014 from Wuchang City, Qiqihar City, and Jiamusi City (Table 1). At the same time, 112 soil samples (0–20 cm and 20–40 cm) were also collected after harvesting the rice.

YearNumberLongitude (E)Latitude (N)Type of soilGeologyAverage temperature (°C)Annual rainfall (mm)Sunshine (h)

Qiqihar201332124°05′–124°15′48˚07′–48˚13′Black soilUpper Pleistocene3.96522861
Wuchang201328126°41′–127°46′44°25′–45°12′Sandy loam soilGuadalupian4.17752194

2.2. Sample Pretreatment

Collected rice grains were washed by deionized water thoroughly and dried in an oven (DHG-9123A, Jinghong, China) at 38°C until the weight was unchanged. All the rice kernel samples were ground with a sample miller (LM-3100, Perten, Sweden) to obtain fine powder. Meanwhile, soil samples were air-dried, finely ground with a ball mill (QM-3SP2, Planetary Ball Mill, Nanjing, Nanjing Nanda Instrument Plant), and sieved (the sieve pore diameter of 0.075 mm).

2.3. Multielement Analysis of Samples

The digestion methods of each rice powder sample and the certified reference material of rice flour (GBW10011) were described as follows. Firstly, 0.25 g homogenized sample was treated in 6 mL of concentrated HNO3 (Beijing Institute of Chemical Reagents, Beijing, China) for 2 h in the Teflon digestion vessel. Then, 2 mL of BV-III grade of H2O2 (Beijing Institute of Chemical Reagents, Beijing, China) was added into each vessel. After 30 min digestion for releasing nitrogen oxides, the vessels were transferred into microwave digestion instrument (CEM MARS Xpress, CEM, Matthews, USA) and the temperature was gradually increased to 180°C within 40 min for digestion. As for soil samples, approximately 0.10 g of the homogenized sample was treated with 8 mL of concentrated HNO3 (Beijing Institute of Chemical Reagents, Beijing, China) and then 2 mL HF (Beijing Institute of Chemical Reagents, Beijing, China) into each vessel to digest the soil sample by gradually increasing the temperature to 185°C within 50 min. After digestion, all the liquid was cooled and diluted into a plastic vase with 18.2 MΩ·cm ultrapure water (Milli-Q, Millipore, USA) until the total solution weight was approximately 120 g. To avoid cross-contamination, Teflon digestion vessels were cleaned in a bath of 10% (v/v) nitric solution for 48 h in advance.

The concentrations of 16 isotopes (23Na, 24Mg, 27Al, 39K, 43Ca, 51V, 55Mn, 56Fe, 59Co, 60Cu, 63Zn, 75As, 85Rb, 88Sr, 111Cd, and 208Pb) in rice were determined with a high-resolution inductively coupled plasma mass spectrometer (HR-ICP-MS) (Agilent 7700 Series, Agilent, Santa Clara, USA). An online internal standard solution of 72Ge, 115In, and 209Bi was used to correct matrix effects and compensate for possible deviations in the instrument performance. The operation conditions for ICP-MS were provided as follows: radio frequency power, 1280 W; the temperature of the atomizing chamber, 2°C; the sampling depth, 8 mm. The flow rates of cooling gas, carrier gas, and auxiliary gas were, respectively, 1.47 L·min−1, 1 L·min−1, and 1 L·min−1.

The CRM of wheat flour (GBW10011) was digested and determined according to the above method to verify the determination procedure. The recoveries of all the elements of CRM ranged from 80% to 120%, and the linear range, LOD, and LOQ are listed in Table 2. The determination procedure of all the samples was carried out in three replicates. If the relative standard deviation of internal standard concentration was higher than 5%, the sample was remeasured. The measured data were corrected with the water content measured before digestion to obtain the element concentrations based on dry matters.

Calibration range (μg/L)R2LOD (ppb)LOQ (ppb)

Na0–4 ppm0.99823.813.6
Mg0–4 ppm0.99860.93.0
Al0–40 ppb0.99531.55.0
K0–4 ppm0.99733.314.1
Ca0–4 ppm0.99924.615.3
V0–40 ppb0.99860.010.03
Mn0–40 ppb0.99961.55.0
Fe0–40 ppb0.99881.86.1
Co0–40 ppb0.99900.00150.0048
Cu0–40 ppb0.99941.24.0
Zn0–40 ppb0.99931.474.89
As0–40 ppb0.99910.0090.029
Rb0–40 ppb0.99950.060.21
Sr0–40 ppb0.99970.080.26
Cd0–40 ppb0.99930.0030.01
Pb0–40 ppb0.99930.0240.081

2.4. Statistical Analysis

One-way analysis of variance (one-way ANOVA) was carried out to access the statistically significant differences in the element contents of rice and soil among different regions and different genotypes. Linear discriminant analysis was performed to establish a discriminant model for identifying the geographical origin of rice samples with SPSS for Windows version 18.0 (SPSS Inc., Chicago, IL, USA).

3. Results

3.1. Elements in Rice from Different Regions

The elements of Fe, Cu, As, Cd, or Pb were not significantly different among the samples obtained in 2013. The elements of Ca, Fe, Sr, or Pb were not significantly different among the samples obtained in 2014. The samples from Wuchang had the lowest contents of Mg, K, Fe, Co, and Zn, and the concentrations of Na, Mg, K, Ca, Co, Zn, and As in the samples from Jiamusi are the highest among three regions. The concentration of Fe in the rice samples from Qiqihar was the highest among the rice samples from three regions (Table 3).


Na18.9 ± 3.67b13.1 ± 2.80c22.1 ± 6.01a
Mg188 ± 37.1c345 ± 55.9b399 ± 75.3a
Al16.7 ± 2.09a12.0 ± 6.89ab8.33 ± 2.21b
K733 ± 90.1c909 ± 103b1.03 ± 0.174E3a
Ca104 ± 27.6b74.0 ± 14.8c123 ± 44.2a
V0.0244 ± 0.00695b0.0299 ± 0.00595a0.0249 ± 0.00530b
Mn13.2 ± 1.74a8.67 ± 1.98c10.3 ± 2.99b
Fe9.21 ± 3.1812.5 ± 11.912.0 ± 4.10
Co0.00820 ± 0.00443b0.00785 ± 0.00297b0.0149 ± 0.00695a
Cu1.85 ± 0.2912.36 ± 2.232.25 ± 0.833
Zn12.0 ± 0.950c13.6 ± 2.11b16.4 ± 3.32a
As0.148 ± 0.06230.137 ± 0.08480.184 ± 0.110
Rb1.713 ± 0.855a0.680 ± 0.258b1.742 ± 0.969a
Sr0.152 ± 0.046b0.154 ± 0.039b0.235 ± 0.104a
Cd0.00760 ± 0.004730.0123 ± 0.03770.0132 ± 0.0171
Pb0.0313 ± 0.01190.0371 ± 0.05250.0485 ± 0.0153

Na4.73 ± 2.33a3.71 ± 1.78b5.08 ± 1.75a
Mg216 ± 49.0b319 ± 30.0a324 ± 47.8a
Al38.6 ± 29.0a14.8 ± 11.7b20.4 ± 21.4b
K653 ± 96.7b817 ± 55.8a831 ± 79.6a
Ca84.4 ± 15.570.8 ± 12.685.2 ± 43.2
V0.0165 ± 0.00765a0.0124 ± 0.00374b0.0171 ± 0.00606a
Mn15.5 ± 3.29a9.21 ± 2.00b8.17 ± 2.05b
Fe5.80 ± 4.226.74 ± 3.376.07 ± 2.30
Co0.00437 ± 0.00214b0.00541 ± 0.00179b0.00854 ± 0.00287a
Cu2.43 ± 0.679a2.19 ± 0.726ab1.90 ± 0.552b
Zn13.2 ± 1.42b13.9 ± 1.68ab14.2 ± 1.75a
As0.127 ± 0.0544ab0.108 ± 0.0397b0.140 ± 0.0270a
Rb1.55 ± 0.764a0.524 ± 0.185b0.755 ± 0.346b
Sr0.151 ± 0.1650.113 ± 0.01850.143 ± 0.071
Cd0.0223 ± 0.0219a0.0632 ± 0.0295b0.0712 ± 0.0148b
Pb0.0308 ± 0.04420.0164 ± 0.01050.0248 ± 0.0310

Different letters in rows are statistically significantly different at .
3.2. Elements in Soil from Different Regions

The concentrations of elements were expressed as mean ± standard deviation (SD) for soil samples from the depths of 0–20 cm and 20–40 cm (Table 4). Soil in Qiqihar was rich in Mg, Al, K, Ca, Fe, Co, Cu, Zn, As, Sr, and Cd. The soil samples from Jiamusi showed the lowest concentrations of Mg, Ca, Fe, Zn, and Cd, and Wuchang soil had the lowest contents of Na, Al, K, V, Mn, Co, As, Rb, and Pb.


Soil (0–20 cm)
Na1.08 ± 0.454E41.10 ± 0.364E41.30 ± 0.104E4
Mg6.49 ± 2.62E3b8.29 ± 1.79E3a4.40E3 ± 0.538E3c
Al5.60 ± 1.87E46.32 ± 1.47E45.70 ± 0.421E4
K1.70 ± 0.609E41.90 ± 0.509E41.84 ± 0.00811E4
Ca1.09 ± 0.716E4b1.62 ± 1.08E4a6.54 ± 1.01E3b
V75.3 ± 29.2b94.5 ± 23.7a87.7 ± 4.87ab
Mn542 ± 261675 ± 176660 ± 410
Fe2.99 ± 1.17E4b3.74 ± 0.938E4a2.90 ± 0.282E4b
Co11.3 ± 5.17b14.9 ± 3.64a13.0 ± 3.28ab
Cu19.6 ± 7.80b23.6 ± 5.19a20.5 ± 1.66ab
Zn64.7 ± 21.0b76.3 ± 15.6a50.3 ± 6.93c
As9.05 ± 3.81b11.5 ± 3.11a10.6 ± 1.473b
Rb72.1 ± 23.9b80.9 ± 22.1ab89.0 ± 20.1a
Sr140 ± 51.1b172 ± 45.7a147 ± 9.05ab
Cd108 ± 30.3a118 ± 45.4a71.0 ± 19.2b
Pb20.2 ± 7.01b23.7 ± 6.20ab25.7 ± 1.41a

Soil (20–40 cm)
Na1.09 ± 0.446E4b1.63 ± 0.348E4a1.47 ± 0.230E4a
Mg6.70 ± 2.72E3a8.19 ± 2.11E3a5.03 ± 1.37E3b
Al5.88 ± 1.72E4b7.32 ± 1.76E4a5.97 ± 0.819E4b
K1.720 ± 0.541E4b2.37 ± 0.515E4a1.99 ± 0.185E4b
Ca1.17 ± 0.780E4a1.05 ± 0.26E4a6.66 ± 1.45E3b
V81.0 ± 27.994.4 ± 28.289.5 ± 5.85
Mn620 ± 303634 ± 194633 ± 342
Fe3.25 ± 1.13E43.63 ± 1.14E43.05 ± 0.367E4
Co12.6 ± 5.1213.2 ± 4.1012.9 ± 3.07
Cu20.2 ± 7.51b26.9 ± 9.87a19.5 ± 2.23b
Zn68.5 ± 18.1a79.2 ± 18.5a54.1 ± 16.1b
As10.5 ± 4.1510.8 ± 3.5210.5 ± 1.76
Rb76.1 ± 22.1b99.7 ± 23.8a87.1 ± 7.25ab
Sr147 ± 50.7b184 ± 37.5a147 ± 21.6b
Cd95.4 ± 33.8b162.0 ± 66.0a71.6 ± 28.1b
Pb21.1 ± 6.42b26.6 ± 7.16a25.3 ± 1.50a

Different letters in rows are statistically significantly different at .
3.3. Elements in Rice among Different Genotypes in One Region

The concentrations of elements in rice samples of the three genotypes (Longjing 31, Longjing 39, and Kongyu 131) in the region of Jiamusi from the year of 2014 were expressed as the mean and standard deviation (SD) for each of the tested categories (Table 5). There are 11 samples of Longjing 31, 5 samples of Longjing 39, and 5 samples of Kongyu 131, respectively. Significant differences in Cu were found between Longjing 39 and Kongyu 131, but no significant difference in other elements was observed among different genotypes.

Longjing 31Longjing 39Kongyu 131

Na4.65 ± 1.786.22 ± 2.195.71 ± 1.62
Mg341 ± 32.7319 ± 25.9294 ± 65.7
Al12.6 ± 11.614.3 ± 6.7932.5 ± 43.4
K838 ± 63.1877 ± 50.179.8 ± 130
Ca66.6 ± 8.2193.7 ± 43.070.0 ± 11.8
V0.0162 ± 0.03850.0184 ± 0.005920.0191 ± 0.00741
Mn7.62 ± 1.598.75 ± 2.538.43 ± 2.36
Fe5.07 ± 1.406.98 ± 3.707.58 ± 2.52
Co0.00717 ± 0.001820.00884 ± 0.004360.00898 ± 0.00173
Cu1.84 ± 0.515ab1.41 ± 0.553b2.36 ± 0.604a
Zn14.3 ± 1.7113.7 ± 2.9214.0 ± 1.27
As0.133 ± 0.02560.159 ± 0.02930.134 ± 0.0311
Rb0.654 ± 0.2140.933 ± 0.5140.746 ± 0.408
Sr0.123 ± 0.0140.135 ± 0.0340.113 ± 0.012
Cd0.00579 ± 0.002590.00987 ± 0.01150.00490 ± 0.00154
Pb0.0132 ± 0.01310.0170 ± 0.01740.0191 ± 0.0255

Different letters in rows are statistically significantly different at .
3.4. Elements in Rice between Different Years

The concentrations of elements in rice samples of two years including three regions were expressed as the mean and standard deviation (SD) for each of the tested categories (Table 6). Except the elements of V, Cu, Cd, and Pb, other elements found significant difference between the harvest year of 2013 and 2014 ().


Na17.9 ± 5.84a4.43 ± 1.89b
Mg318 ± 105a289 ± 64.3b
Al12.4 ± 12.9b24.2 ± 23.9a
K897 ± 175a770 ± 112b
Ca100 ± 37a80.1 ± 28.4b
V0.0263 ± 0.006210.0154 ± 0.00633
Mn10.6 ± 2.93b10.8 ± 4.05a
Fe11.3 ± 7.84a6.00 ± 2.65b
Co0.0104 ± 0.00604b0.00616 ± 0.00292a
Cu2.19 ± 1.442.17 ± 0.687
Zn14.01 ± 2.94a13.8 ± 1.67b
As0.157 ± 0.0908a0.126 ± 0.0430b
Rb1.38 ± 0.904a0.942 ± 0.656b
Sr0.181 ± 0.080a0.126 ± 0.0502b
Cd0.00109 ± 0.002470.00116 ± 0.00148
Pb0.0394 ± 0.03410.0234 ± 0.0315

Different letters in rows are statistically significantly different at .
3.5. Linear Discriminant Analysis

According to the element determination results of the samples in 2013 and 2014, we established the geographical origin discrimination model with the elements (Na, Mg, Al, K, V, Mn, Co, Zn, Rb, and Cd) with significant differences among various geographical origins for both years. The overall correct identification rates of the rice samples from three regions obtained in 2013 and 2014 were, respectively, 96.6% and 89.6%. Although the cross-validation rates decreased slightly, all the samples in Wuchang were significantly isolated from rice samples in the other two geographical origins (Table 7). According to the scatter plots, all the rice samples in Wuchang can be separated clearly from the rice samples from other regions based on multielement analysis results (Figure 1).

RegionPredicted group membership

Original count (%)280028
Cross-validated count (%)270128

Original count (%)310031
Cross-validated count (%)301031

4. Discussion

According to previous studies, the element profiles in grain from three regions had their own characteristics and were closely related to the geologic background, soil type, and climate [22, 23]. Wuchang city belongs to temperate continental monsoon climate; Qiqihar belongs to the cold temperate continental monsoon climate; Jiamusi belongs to temperate humid monsoon climate. The differences in climate showed different annual temperatures and precipitation, which influenced the uptake of elements. In addition, the contents of mineral elements in rice samples are also affected by factors such as variety and agricultural practices [16, 24].

The concentrations of elements in soil from Qiqihar and Jiamusi were consistent with previous results. Cao et al. [25] reported that the concentration ranges of As, Cu, Pb, and Zn in rice soil of Sanjiang Plain were, respectively, 1.36–80.98, 1.97–665.62, 0.03–27.86, and 4.61–125.50 mg/kg. The mean values obtained in this study falls within these ranges. In addition, the soil types of Qiqihar and Jiamusi are, respectively, black soil and planosol. The contents of As and Zn in black soil are higher than those in planosol [25]. Our study also gave the consistent results.

In our study, only the element of Cu showed significant differences among the samples of different genotypes. The similar results had been reported in wheat genotypes [26, 27]. They found that, compared with the environment or region, the genotype contributed most to the variation of Cu in rice, indicating that the genotype of rice also affected the accumulation of Cu in grains. When discriminating the geographical origin of rice by the element of Cu, it would be better to consider only one genotype in case of variety interference.

Apart from the factor of the genotype, the concentrations of most elements (Na, Mg, Al, K, Ca, Mn, Fe, Co, Zn, As, Rb, and Sr) were found to be significantly different among two years. In previous reports, the contents of Mg, Al, Ca, Fe, Zn, and As in wheat were most affected by the harvest year comparing with the region or genotype according to the result of multiway variance analysis [19], and the harvest year was the main source of the variations of the concentrations of Fe and Zn in maize [28]; these results are consistent with ours. The significant differences for some element contents could be explained by the changing weather conditions such as the annual temperature, precipitation, and sunshine.

According to the result of linear discriminant analysis, the overall correct identification rate of the rice samples from three regions was higher than 89.6% for single year samples. Satisfactory results were obtained for distinguishing Wuchang rice from rice produced from other regions (correct identification rate: 100%) based on mineral profiles in two years, and there was no significant concentration difference in the elements (except Cu) among the three genotypes in the same region. It is promising and reliable to identify rice according to their geographic origins by using multielement analysis in combination with statistical analysis.

There are also some other methods for rice geographical traceability, such as stable isotopic ratios, near-infrared technique, and volatile component. Among them, the multielement method is time-saving because all the elements can be determined once for each sample. In addition, the concentration of element in samples is very stable compared with organic composition since some organic composition in food may change with the temperature, sunshine, and storage time. As a result, multielement analysis is a promising method for Wuchang rice geographical traceability and is more suitable for application in marketing.

Data Availability

The data used to support the findings of this study are included within the article.


Lili Qian and Feng Zuo are the co-first authors.

Conflicts of Interest

The authors declare that they have no conflicts of interest.


This work was funded by the Heilongjiang Province Applied Technology Research and Development Project (GA14B104), Start Plan of Scientific Research on Talent Introduction (XDB201810), Heilongjiang Province Reclamation Bureau “Thirteen Five-Year Plan” Key Scientific and Technological Projects (HNK135-06-06), and School Project (XZR2016-07).


  1. P. Cheajesadagul, C. Arnaudguilhem, J. Shiowatana, A. Siripinyanond, and J. Szpunar, “Discrimination of geographical origin of rice based on multi-element fingerprinting by high resolution inductively coupled plasma mass spectrometry,” Food Chemistry, vol. 141, no. 4, pp. 3504–3509, 2013. View at: Publisher Site | Google Scholar
  2. S. Muthayya, J. D. Sugimoto, S. Montgomery, and G. F. Maberly, “An overview of global rice production, supply, trade, and consumption,” Annals of the New York Academy of Sciences, vol. 1324, no. 1, pp. 7–14, 2014. View at: Publisher Site | Google Scholar
  3. C. Maione and R. M. Barbosa, “Recent applications of multivariate data analysis methods in the authentication of rice and the most analyzed parameters: a review,” Critical Reviews in Food Science and Nutrition, pp. 1–12, 2018. View at: Publisher Site | Google Scholar
  4. D. G. Asfaha, C. R. QuéTel, F. Thomas et al., “Combining isotopic signatures of n(87Sr)/n(86Sr) and light stable elements (C, N, O, S) with multi-elemental profiling for the authentication of provenance of European cereal samples,” Journal of Cereal Science, vol. 53, no. 2, pp. 170–177, 2011. View at: Publisher Site | Google Scholar
  5. A. Kawasaki, H. Oda, and T. Hirata, “Determination of strontium isotope ratio of brown rice for estimating its provenance,” Soil Science and Plant Nutrition, vol. 48, no. 5, pp. 635–640, 2002. View at: Publisher Site | Google Scholar
  6. C. Kukusamude and S. Kongsri, “Elemental and isotopic profiling of Thai jasmine rice (Khao Dawk Mali 105) for discrimination of geographical origins in Thung Kula Rong Hai area, Thailand,” Food Control, vol. 91, pp. 357–364, 2018. View at: Publisher Site | Google Scholar
  7. D. Rashmi, P. Shree, and D. K. Singh, “Stable isotope ratio analysis in determining the geographical traceability of Indian wheat,” Food Control, vol. 79, pp. 169–176, 2017. View at: Publisher Site | Google Scholar
  8. Y. Wu, D. Luo, H. Dong et al., “Geographical origin of cereal grains based on element analyser-stable isotope ratio mass spectrometry (EA-SIRMS),” Food Chemistry, vol. 174, pp. 553–557, 2015. View at: Publisher Site | Google Scholar
  9. S. Yaeko, C. Yoshito, O. O. Nanako, O. Naohiko, and K. Takashi, “Geographical origin of polished rice based on multiple element and stable isotope analyses,” Food Chemistry, vol. 109, no. 2, pp. 470–475, 2008. View at: Publisher Site | Google Scholar
  10. S. S. Kim, M.-R. Rhyu, J. M. Kim, and S.-H. Lee, “Authentication of rice using near-infrared reflectance spectroscopy,” Cereal Chemistry Journal, vol. 80, no. 3, pp. 346–349, 2003. View at: Publisher Site | Google Scholar
  11. B. G. Osborne, B. Mertens, M. Thompson, and T. Fearn, “The authentication of Basmati rice using near infrared spectroscopy,” Journal of Near Infrared Spectroscopy, vol. 1, no. 2, pp. 77–83, 1993. View at: Publisher Site | Google Scholar
  12. L. Zhu, J. Sun, G. Wu et al., “Identification of rice varieties and determination of their geographical origin in China using Raman spectroscopy,” Journal of Cereal Science, vol. 82, pp. 175–182, 2018. View at: Publisher Site | Google Scholar
  13. Y. Huo, G. M. Kamal, J. Wang et al., “1 H NMR-based metabolomics for discrimination of rice from different geographical origins of China,” Journal of Cereal Science, vol. 76, pp. 243–252, 2017. View at: Publisher Site | Google Scholar
  14. I.-M. Chung, J.-K. Kim, J.-K. Lee, and S.-H. Kim, “Discrimination of geographical origin of rice (Oryza sativa L.) by multielement analysis using inductively coupled plasma atomic emission spectroscopy and multivariate analysis,” Journal of Cereal Science, vol. 65, pp. 252–259, 2015. View at: Publisher Site | Google Scholar
  15. G. Li, L. Nunes, Y. Wang et al., “Profiling the ionome of rice and its use in discriminating geographical origins at the regional scale, China,” Journal of Environmental Sciences, vol. 25, no. 1, pp. 144–154, 2013. View at: Publisher Site | Google Scholar
  16. H. Zhao, B. Guo, Y. Wei et al., “Determining the geographic origin of wheat using multielement analysis and multivariate statistics,” Journal of Agricultural and Food Chemistry, vol. 59, no. 9, pp. 4397–4402, 2011. View at: Publisher Site | Google Scholar
  17. M. A. Yan, G. Di Martino, C. Guillou, F. Reniero, A. Sacco, and F. Serra, “Differentiation of the geographical origin of durum wheat semolina samples on the basis of isotopic composition,” Rapid Communications in Mass Spectrometry, vol. 16, no. 24, pp. 2286–2290, 2002. View at: Publisher Site | Google Scholar
  18. H. Liu, Y. Wei, H. Lu et al., “Combination of the 87Sr/86Sr ratio and light stable isotopic values (δ13C, δ15N and δD) for identifying the geographical origin of winter wheat in China,” Food Chemistry, vol. 212, pp. 367–373, 2016. View at: Publisher Site | Google Scholar
  19. H. Liu, Y. Wei, Y. Zhang, S. Wei, S. Zhang, and B. Guo, “The effectiveness of multi-element fingerprints for identifying the geographical origin of wheat,” International Journal of Food Science and Technology, vol. 52, no. 4, pp. 1018–1025, 2017. View at: Publisher Site | Google Scholar
  20. T. Chen, Y. Zhao, W. Zhang, S. Yang, Z. Ye, and G. Zhang, “Variation of the light stable isotopes in the superior and inferior grains of rice (Oryza sativa L.) with different geographical origins,” Food Chemistry, vol. 209, pp. 95–98, 2016. View at: Publisher Site | Google Scholar
  21. M. Du, Y. Fang, F. Shen et al., “Multiangle discrimination of geographical origin of rice based on analysis of mineral elements and characteristic volatile components,” International Journal of Food Science and Technology, vol. 53, no. 9, pp. 2088–2096, 2018. View at: Publisher Site | Google Scholar
  22. M. Karami, M. Afyuni, A. H. Khoshgoftarmanesh, A. Papritz, and R. Schulin, “Grain zinc, iron, and copper concentrations of wheat grown in Central Iran and their relationships with soil and climate variables,” Journal of Agricultural and Food Chemistry, vol. 57, no. 22, pp. 10876–10882, 2009. View at: Publisher Site | Google Scholar
  23. X. L. Zhao, “Effects of nitrogen and phosphorus fertilization and sowing date on dynamic changes of grain sedimentation value during grain filling stage of spring wheat,” Chinese Journal of Applied Ecology, vol. 17, pp. 640–646, 2006, in Chinese. View at: Google Scholar
  24. Y. Zhang, Q. Song, J. Yan et al., “Mineral element concentrations in grains of Chinese wheat cultivars,” Euphytica, vol. 174, no. 3, pp. 303–313, 2010. View at: Publisher Site | Google Scholar
  25. H. J. Cao, L. M. Wang, C. Y. Luo, J. Z. Zhang, and H. W. Ni, “Spatial distribution of heavy metals in agricultural soil in Sanjiang Plain,” Journal of Ecology and Rural Environment, vol. 30, no. 2, pp. 155–161, 2014, in Chinese. View at: Google Scholar
  26. H. F. Gomez-Becerra, A. Yazici, L. Ozturk et al., “Genetic variation and environmental stability of grain mineral nutrient concentrations in triticum dicoccoides under five environments,” Euphytica, vol. 171, no. 1, pp. 39–52, 2010. View at: Publisher Site | Google Scholar
  27. H. Zhao, B. Guo, Y. Wei, and B. Zhang, “Effects of wheat origin, genotype, and their interaction on multielement fingerprints for geographical traceability,” Journal of Agricultural and Food Chemistry, vol. 60, no. 44, pp. 10957–10962, 2012. View at: Publisher Site | Google Scholar
  28. S. O. Oikeh, A. Menkir, B. Maziya-Dixon, R. M. Welch, R. P. Glahn, and G. Gauch, “Environmental stability of iron and zinc concentrations in grain of elite early-maturing tropical maize genotypes grown under field conditions,” The Journal of Agricultural Science, vol. 142, no. 5, pp. 543–551, 2004. View at: Publisher Site | Google Scholar

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