Journal of Food Quality
Volume 2019 (2019), Article ID 8396865, 7 pages
https://doi.org/10.1155/2019/8396865

Research Article

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

Lili Qian,1,2 Feng Zuo,¹ Hongyan Liu,³ Caidong Zhang,¹ Xiaoxing Chi,1,2 and Dongjie Zhang1,2

¹College of Food Science, Heilongjiang Bayi Agricultural University, Daqing 163319, China
²Key Laboratory of Agro-Products Processing and Quality Safety of Heilongjiang Province, Daqing 163319, China
³State Key Laboratory of Animal Nutrition, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China

Correspondence should be addressed to Dongjie Zhang; byndzdj@126.com

Received 22 January 2019; Revised 6 March 2019; Accepted 12 March 2019; Published 1 April 2019

Guest Editor: Rommel M. Barbosa

Copyright © 2019 Lili Qian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

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 [4–9], near-infrared technique [10, 11], Raman spectroscopy [12], ¹H-NMR spectroscopy [13], and multielement analysis [14–16]. 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.

Table 1: The information of rice samples from three regions.

Year
Number
Longitude (E)
Latitude (N)
Type of soil
Geology
Average temperature (°C)
Annual rainfall (mm)
Sunshine (h)

Qiqihar
2013
32
124°05′–124°15′
48˚07′–48˚13′
Black soil
Upper Pleistocene
3.9
652
2861

2014
32
124°05′–124°14′
48˚07′–48˚12′
4.7
593
3015

Jiamusi
2013
30
132°45′–134°20′
47˚02′–47˚59′
Planosol
Neoproterozoic
2.7
572
2305

2014
30
132°40′–134°25′
47˚01′–47˚58′
3.2
454
2477

Wuchang
2013
28
126°41′–127°46′
44°25′–45°12′
Sandy loam soil
Guadalupian
4.1
775
2194

2014
30
126°44′–127°46′
44°24′–45°13′
4.9
562
2149

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.

Table 2: Quality control for determining the elements of rice.

Calibration range (μg/L)
R²
LOD (ppb)
LOQ (ppb)

Na
0–4 ppm
0.9982
3.8
13.6

Mg
0–4 ppm
0.9986
0.9
3.0

Al
0–40 ppb
0.9953
1.5
5.0

K
0–4 ppm
0.9973
3.3
14.1

Ca
0–4 ppm
0.9992
4.6
15.3

V
0–40 ppb
0.9986
0.01
0.03

Mn
0–40 ppb
0.9996
1.5
5.0

Fe
0–40 ppb
0.9988
1.8
6.1

Co
0–40 ppb
0.9990
0.0015
0.0048

Cu
0–40 ppb
0.9994
1.2
4.0

Zn
0–40 ppb
0.9993
1.47
4.89

As
0–40 ppb
0.9991
0.009
0.029

Rb
0–40 ppb
0.9995
0.06
0.21

Sr
0–40 ppb
0.9997
0.08
0.26

Cd
0–40 ppb
0.9993
0.003
0.01

Pb
0–40 ppb
0.9993
0.024
0.081

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).

Table 3: Elements for all rice samples harvested in the year of 2013 and 2014 among three different regions (µg·g⁻¹).

Region

Wuchang
Qiqihar
Jiamusi

2013

Na
18.9 ± 3.67b
13.1 ± 2.80c
22.1 ± 6.01a

Mg
188 ± 37.1c
345 ± 55.9b
399 ± 75.3a

Al
16.7 ± 2.09a
12.0 ± 6.89ab
8.33 ± 2.21b

K
733 ± 90.1c
909 ± 103b
1.03 ± 0.174E3a

Ca
104 ± 27.6b
74.0 ± 14.8c
123 ± 44.2a

V
0.0244 ± 0.00695b
0.0299 ± 0.00595a
0.0249 ± 0.00530b

Mn
13.2 ± 1.74a
8.67 ± 1.98c
10.3 ± 2.99b

Fe
9.21 ± 3.18
12.5 ± 11.9
12.0 ± 4.10

Co
0.00820 ± 0.00443b
0.00785 ± 0.00297b
0.0149 ± 0.00695a

Cu
1.85 ± 0.291
2.36 ± 2.23
2.25 ± 0.833

Zn
12.0 ± 0.950c
13.6 ± 2.11b
16.4 ± 3.32a

As
0.148 ± 0.0623
0.137 ± 0.0848
0.184 ± 0.110

Rb
1.713 ± 0.855a
0.680 ± 0.258b
1.742 ± 0.969a

Sr
0.152 ± 0.046b
0.154 ± 0.039b
0.235 ± 0.104a

Cd
0.00760 ± 0.00473
0.0123 ± 0.0377
0.0132 ± 0.0171

Pb
0.0313 ± 0.0119
0.0371 ± 0.0525
0.0485 ± 0.0153

2014

Na
4.73 ± 2.33a
3.71 ± 1.78b
5.08 ± 1.75a

Mg
216 ± 49.0b
319 ± 30.0a
324 ± 47.8a

Al
38.6 ± 29.0a
14.8 ± 11.7b
20.4 ± 21.4b

K
653 ± 96.7b
817 ± 55.8a
831 ± 79.6a

Ca
84.4 ± 15.5
70.8 ± 12.6
85.2 ± 43.2

V
0.0165 ± 0.00765a
0.0124 ± 0.00374b
0.0171 ± 0.00606a

Mn
15.5 ± 3.29a
9.21 ± 2.00b
8.17 ± 2.05b

Fe
5.80 ± 4.22
6.74 ± 3.37
6.07 ± 2.30

Co
0.00437 ± 0.00214b
0.00541 ± 0.00179b
0.00854 ± 0.00287a

Cu
2.43 ± 0.679a
2.19 ± 0.726ab
1.90 ± 0.552b

Zn
13.2 ± 1.42b
13.9 ± 1.68ab
14.2 ± 1.75a

As
0.127 ± 0.0544ab
0.108 ± 0.0397b
0.140 ± 0.0270a

Rb
1.55 ± 0.764a
0.524 ± 0.185b
0.755 ± 0.346b

Sr
0.151 ± 0.165
0.113 ± 0.0185
0.143 ± 0.071

Cd
0.0223 ± 0.0219a
0.0632 ± 0.0295b
0.0712 ± 0.0148b

Pb
0.0308 ± 0.0442
0.0164 ± 0.0105
0.0248 ± 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.

Table 4: Elements for soil samples (0–20 cm and 20–40 cm) among three different regions (µg·g⁻¹).

Region

Wuchang
Qiqihar
Jiamusi

Soil (0–20 cm)

Na
1.08 ± 0.454E4
1.10 ± 0.364E4
1.30 ± 0.104E4

Mg
6.49 ± 2.62E3b
8.29 ± 1.79E3a
4.40E3 ± 0.538E3c

Al
5.60 ± 1.87E4
6.32 ± 1.47E4
5.70 ± 0.421E4

K
1.70 ± 0.609E4
1.90 ± 0.509E4
1.84 ± 0.00811E4

Ca
1.09 ± 0.716E4b
1.62 ± 1.08E4a
6.54 ± 1.01E3b

V
75.3 ± 29.2b
94.5 ± 23.7a
87.7 ± 4.87ab

Mn
542 ± 261
675 ± 176
660 ± 410

Fe
2.99 ± 1.17E4b
3.74 ± 0.938E4a
2.90 ± 0.282E4b

Co
11.3 ± 5.17b
14.9 ± 3.64a
13.0 ± 3.28ab

Cu
19.6 ± 7.80b
23.6 ± 5.19a
20.5 ± 1.66ab

Zn
64.7 ± 21.0b
76.3 ± 15.6a
50.3 ± 6.93c

As
9.05 ± 3.81b
11.5 ± 3.11a
10.6 ± 1.473b

Rb
72.1 ± 23.9b
80.9 ± 22.1ab
89.0 ± 20.1a

Sr
140 ± 51.1b
172 ± 45.7a
147 ± 9.05ab

Cd
108 ± 30.3a
118 ± 45.4a
71.0 ± 19.2b

Pb
20.2 ± 7.01b
23.7 ± 6.20ab
25.7 ± 1.41a

Soil (20–40 cm)

Na
1.09 ± 0.446E4b
1.63 ± 0.348E4a
1.47 ± 0.230E4a

Mg
6.70 ± 2.72E3a
8.19 ± 2.11E3a
5.03 ± 1.37E3b

Al
5.88 ± 1.72E4b
7.32 ± 1.76E4a
5.97 ± 0.819E4b

K
1.720 ± 0.541E4b
2.37 ± 0.515E4a
1.99 ± 0.185E4b

Ca
1.17 ± 0.780E4a
1.05 ± 0.26E4a
6.66 ± 1.45E3b

V
81.0 ± 27.9
94.4 ± 28.2
89.5 ± 5.85

Mn
620 ± 303
634 ± 194
633 ± 342

Fe
3.25 ± 1.13E4
3.63 ± 1.14E4
3.05 ± 0.367E4

Co
12.6 ± 5.12
13.2 ± 4.10
12.9 ± 3.07

Cu
20.2 ± 7.51b
26.9 ± 9.87a
19.5 ± 2.23b

Zn
68.5 ± 18.1a
79.2 ± 18.5a
54.1 ± 16.1b

As
10.5 ± 4.15
10.8 ± 3.52
10.5 ± 1.76

Rb
76.1 ± 22.1b
99.7 ± 23.8a
87.1 ± 7.25ab

Sr
147 ± 50.7b
184 ± 37.5a
147 ± 21.6b

Cd
95.4 ± 33.8b
162.0 ± 66.0a
71.6 ± 28.1b

Pb
21.1 ± 6.42b
26.6 ± 7.16a
25.3 ± 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.

Table 5: Elements for all rice samples among three different genotypes at Jiamusi from the year of 2014 (µg·g⁻¹).

Element
Genotype

Longjing 31
Longjing 39
Kongyu 131

Na
4.65 ± 1.78
6.22 ± 2.19
5.71 ± 1.62

Mg
341 ± 32.7
319 ± 25.9
294 ± 65.7

Al
12.6 ± 11.6
14.3 ± 6.79
32.5 ± 43.4

K
838 ± 63.1
877 ± 50.1
79.8 ± 130

Ca
66.6 ± 8.21
93.7 ± 43.0
70.0 ± 11.8

V
0.0162 ± 0.0385
0.0184 ± 0.00592
0.0191 ± 0.00741

Mn
7.62 ± 1.59
8.75 ± 2.53
8.43 ± 2.36

Fe
5.07 ± 1.40
6.98 ± 3.70
7.58 ± 2.52

Co
0.00717 ± 0.00182
0.00884 ± 0.00436
0.00898 ± 0.00173

Cu
1.84 ± 0.515ab
1.41 ± 0.553b
2.36 ± 0.604a

Zn
14.3 ± 1.71
13.7 ± 2.92
14.0 ± 1.27

As
0.133 ± 0.0256
0.159 ± 0.0293
0.134 ± 0.0311

Rb
0.654 ± 0.214
0.933 ± 0.514
0.746 ± 0.408

Sr
0.123 ± 0.014
0.135 ± 0.034
0.113 ± 0.012

Cd
0.00579 ± 0.00259
0.00987 ± 0.0115
0.00490 ± 0.00154

Pb
0.0132 ± 0.0131
0.0170 ± 0.0174
0.0191 ± 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 ().

Table 6: Elements for all rice samples between two different years (µg·g⁻¹).

2013
2014

Na
17.9 ± 5.84a
4.43 ± 1.89b

Mg
318 ± 105a
289 ± 64.3b

Al
12.4 ± 12.9b
24.2 ± 23.9a

K
897 ± 175a
770 ± 112b

Ca
100 ± 37a
80.1 ± 28.4b

V
0.0263 ± 0.00621
0.0154 ± 0.00633

Mn
10.6 ± 2.93b
10.8 ± 4.05a

Fe
11.3 ± 7.84a
6.00 ± 2.65b

Co
0.0104 ± 0.00604b
0.00616 ± 0.00292a

Cu
2.19 ± 1.44
2.17 ± 0.687

Zn
14.01 ± 2.94a
13.8 ± 1.67b

As
0.157 ± 0.0908a
0.126 ± 0.0430b

Rb
1.38 ± 0.904a
0.942 ± 0.656b

Sr
0.181 ± 0.080a
0.126 ± 0.0502b

Cd
0.00109 ± 0.00247
0.00116 ± 0.00148

Pb
0.0394 ± 0.0341
0.0234 ± 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).

Table 7: Linear discriminant analysis of elements in rice from three regions.

Region
Predicted group membership

Wuchang
Qiqihar
Jiamusi
Total

2013

Original count (%)
28
0
0
28

0
31
0
31

1
2
27
30

100
100
90
96.6

Cross-validated count (%)
27
0
1
28

0
30
0
31

1
2
27
30

96.4
100
90
95.5

2014

Original count (%)
31
0
0
31

0
28
4
32

0
6
27
33

100
87.5
81.8
89.6

Cross-validated count (%)
30
1
0
31

0
28
4
32

1
8
24
33

96.8
87.5
72.7
85.4

(a)

(b)

Figure 1: Scatter plot of discriminant scores from functions 1-2 of (a) 2013 and (b) 2014 samples.

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.

Disclosure

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

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

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).

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