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

Journal of Food Composition and Analysis 83 (2019) 103276

Contents lists available at ScienceDirect

Journal of Food Composition and Analysis journal homepage: www.elsevier.com/locate/jfca

Original Research Article

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

T

⁎

Lili Qiana,b, ,1, Caidong Zhanga, Feng Zuoa,1, Lina Zhenga, Dan Lia, Aiwu Zhanga, ⁎ Dongjie Zhanga,b, a b

College of Food Science, Heilongjiang Bayi Agricultural University, Daqing 163319, China Key Laboratory of Agro-Products Processing and Quality Safety of Heilongjiang province, Daqing 163319, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Rice Mineral element Fertilizer Pesticide Influence factors Traceability index

The application of fertilizers and pesticides significantly affects the contents of some mineral elements in rice. Excluding these mineral elements can improve the accuracy of traceability models of rice. With Longjing 31 (a rice variety) as the study object, we carried out field experiments with different dosage levels of fertilizers and pesticides in Jansanjiang, China. The mineral elements in rice were determined by inductively coupled plasma mass spectrometry (ICP-MS). The effects of different dosage levels of fertilizers and pesticides on the mineral elements in rice were compared through single factor analysis of variance. The elements significantly influenced by fertilizers were Fe, Co, Ni, Se, Rh, Eu, Pr, Tl and Pt. The elements significantly affected by pesticides were Al, Co, and Ni. These elements should be excluded in geographical origin tracing. The predictions of the geographic origin made by Fisher discrimination after excluding the above elements gave an overall correct classification rate of 98.9% and a cross-validation rate of 97.8%. Therefore, it is necessary to exclude mineral elements significantly affected by fertilizers and pesticides in the geographical origin traceability of rice.

1. Introduction Rice is an important food crop in global grain trade. Driven by economic interests, high-quality rice in trans-regional trade is often replaced by inferior rice (usually occurs in the origin), thus resulting in huge negative impacts on consumers and producers. A series of geographical origin traceability methods have been gradually developed. Traceability based on mineral elements is an important traceability method. The composition and content of mineral elements in plant-derived products are closely related to the plantation environment (Ghezzi et al., 2017). During the growth of agricultural crops, mineral elements significantly affected by soils and hydrological regimes are stored in plants, so mineral elements in agricultural products can reflect the regional specificity. The traceability technique based on mineral elements has been applied in cheese (Suhaj and Koreňovská, 2008; Moreno-Rojas et al., 2010), honey (Széles et al., 2006; Chudzinska et al., 2012), chili (Marion et al., 2010), tea (Ye et al., 2016; Zhao et al., 2017), wine (Koreňovská and Suhaj, 2005), wheat (Zhao et al., 2013; Liu et al., 2017), sesame (Choi et al., 2017) and other agricultural products. Previous studies on the traceability of cereal origin emphasized natural factors, such as genotype (Yang et al., 1998),

soil (Zhao et al., 2012), climate (Zhao et al., 2014) and processing precision (Wang et al., 2011). Chemical fertilizers and pesticides may change the contents of mineral elements in agricultural products. A foliar fertilizer containing 5.2% Mn, 0.65% Zn and 0.65% Cu provided sufficient nutrient ratios (P/Mn, P/Zn and Fe/Zn) in wheat, thus increasing the yield of wheat markedly (Shaaban, 2001). In the treatments with nitrogen fertilizer, the concentrations of Fe, Mn, Cu and Zn in most parts of rice increased, indicating that the transportation ability of microelements from root to shoot in rice was improved after the application of nitrogen fertilizer (Hao et al., 2007). Pesticides also affect the physiology and biochemistry of plants in a certain way. After spraying pesticides, the contents of P and Zn in cabbage were significantly decreased, whereas those of Fe, Ca and K were significantly increased. In different periods after the spraying of pesticides, the contents of Ca, P, Zn and K in cabbage decreased remarkably, whereas the content of iron increased significantly. Moreover, spraying endosulfan exerted a more significant effect on the contents of minerals than spraying malathion (Reddy and Dash, 1997). In Swedish studies, the use of mineral P fertilizer was correlated with the increase in Cd concentration in soils and cereal grain (Andersson and Bingefors, 1985; Andersson and Simon, 1991; Hedlund et al.,

⁎

Corresponding authors at: College of Food Science, Heilongjiang Bayi Agricultural University, Daqing 163319, China. E-mail addresses: [email protected] (L. Qian), [email protected] (D. Zhang). 1 Lili Qian and Feng Zuo contributed equally to this work. https://doi.org/10.1016/j.jfca.2019.103276 Received 29 August 2018; Received in revised form 25 July 2019; Accepted 31 July 2019 Available online 12 August 2019 0889-1575/ © 2019 Published by Elsevier Inc.

Journal of Food Composition and Analysis 83 (2019) 103276

L. Qian, et al.

1997). Similarly, the application of Cu fertilizers and fungicides was correlated with the increase in Cu content in both soils and food crops (Gundersen et al., 2000; Bengtsson et al., 2003; Rossi et al., 2006, 2008). Although the effects of pesticides and fertilizers on the content of elements in agricultural products has long been studied, previous studies mainly focused on their impacts on nutrient elements and their effects on traceability were seldom reported. Various rice origin identification models were established on the basis of unscreened mineral elements. For example, Li et al. (2016) distinguished Wuchang rice from Non-Wuchang rice by Fisher discriminant method with the selected data of 14 elements (Ca, Cu, Fe, K, Mg, Mn, Zn, Ni, As, Sr, Cd, Ba, Mo and Se) and realized an overall correct classification rate of 93.5%. Shen et al. (2013) established an origin discrimination model for rice from 9 provinces with the selected data of 9 elements (Mg, K, Ca, Na, Be, Mn, Ni, Cu, and Cd) and realized an overall correct classification rate of 100% and a cross-validation rate of 93.8%. Gonzálvez and Guardia (2011) traced the rice samples from different countries with the selected data of 32 elements (Al, As, Ba, Bi, Cd, Ca, Cr, Co, Cu, Fe, Pb, Li, Mg, Mn, Mo, Ni, K, Se, Na, Sr, Tl, Ti, Zn, La, Ce, Pr, Nd, Sm, Eu, Ho, Er and Yb) and gave an overall correct classification rate of 91.3%. In these studies, a discrimination model was firstly established with randomly collected samples and then mineral elements were determined by ICP-MS to discriminate the origins of samples through chemical metrology techniques. These studies proved that it was feasible to trace the origin of rice based on mineral elements. However, the effects of fertilizers and pesticides were not considered in the established models. Therefore, it is necessary to investigate the effects of fertilizers and pesticides on the contents of mineral elements in rice, for the purpose of improving the accuracy of the traceability model. In the study, rice field experiments were performed under different dosages of pesticides and fertilizers in Jiansanjiang, China to screen mineral elements in rice significantly affected by pesticides and fertilizers. Taking rice samples from Jiansanjiang, Chahayang, and Wuchang as the study object, we detected mineral elements by ICP-MS and excluded the affected mineral elements, in order to establish the discriminant model of rice origins by the analysis of variance and Fisher discriminant analysis. The study provides useful information for developing traceability methods of rice origin.

Table 1 Application dosage and time of fertilizers and pesticides used in the medium dose group. Fertilizers and pesticides

Amount Dosage and time of use Base fertilizer

Urea 250 g Triple superphosphate 140 g Potassium sulfate 100 g

75.0 g 140 g 60.0 g

Fertilizer for tillering Tillering stage

Elongation stage

Earing fertilizer Booting stage

80.0 g – –

20.0 g – –

75.0 g – 40.0 g

Herbicide

First dose

Second dose

20.0 mL

10.0 mL

10.0 mL

Fungicide

Booting stage

Heading stage

Full-heading stage

30.0 mL

10.0 mL

10.0 mL

10.0 mL

(–) means that no fertilizer was applied.

cereus; Yueda Pesticide Chemical Co., Dalian, China) were applied. The herbicide was applied twice. Oxadiazon (C15H18Cl2N2O3) was applied for the first time at 3–7 d before the transplantation and then for the second time at 15–20 d after the transplantation. The fungicide mixture of 25% prochloraz and 10% validamycin-B. cereus was sprayed respectively in the booting stage, heading stage and full-heading stage. Three levels (high, medium and low) of basic fertilizer, top dressing and pesticides were designed and blank experiments were arranged at the same time. The dosage in the high dose group and low dose group were respectively 10% higher and lower than that in the medium dose group. Table 1 shows the dosages and application time of fertilizers and pesticides used in the medium dose group in each plot (10 m2). 2.2. Sampling According to the representative sampling principle, 5 groups of rice samples were randomly collected with the checkerboard sampling method in every experimental field. The rice samples used in the model establishment were collected randomly in 2016. The samples came from Jiansanjiang Rice Protection Area, Chahayang Rice Protection Area and Wuchang Rice Protection Area in Heilongjiang Province. The rice samples were gathered at the ripening stage of rice. The five-point sampling method was adopted to select the five points in the field as the repeats. The sampling location, species, longitude, latitude, average temperature, annual precipitation and sunshine hours were recorded (Table 2).

2. Materials and methods 2.1. Design of field experiments In the experimental fields (132°35′E; 47°20′) in Jiansanjiang, Heilongjiang Province, China, the fields containing rich meadow albic soil were selected for the experimental treatments. We conducted the experiments of fertilizers and pesticides with different dosages during the cultivation period of rice. Longjing 31 widely planted in Heilongjiang Province was the tested rice variety. In the experimental design, three repetitive random plots were arranged and irrigated separately and the area of each plot was 10 m2. The application amount of fertilizers was determined based on the soil testing formula and sowing time was determined based on local climate conditions. In the blank test of the fertilizer test group, only a medium dose of pesticides was sprayed. Similarly, in the blank test of the pesticide test group, only a medium dose of fertilizers was sprayed. Firstly, the following nitrogen fertilizer, phosphate fertilizer and potash fertilizer were applied as basal fertilizers: urea (pure nitrogen ≧ 46%; Jiuhuang Chemical Co., Taian, China), triple superphosphate (P2O5 ≧ 43%, Jiuhuang Chemical Co., Taian, China) and potassium sulfate (K2O ≧ 50%, Jiuhuang Chemical Co., Taian, China). The top application was mainly carried out in three periods: tillering stage, elongation stage and booting stage. The conventional herbicides (12% oxadiazon and 50% pretilachlor; Yueda Pesticide Chemical Co., Dalian, China) and fungicides (25% prochloraz and 10% validamycin-Bacillus

2.3. Pretreatment of samples The collected rice samples were dried to a moisture content below 14%. Polished rice (ZW001; Standard Material Purchase Center, Beijing, China) was obtained after threshing, hulling (FC2K rice huller; Ohtake Works Ltd., Japan) and whitening (VP-32 rice mill; Yamamoto Electric Co., Japan) in the laboratory. Then the polished rice was ground (QM-3SP2 Planetary Ball Mill; Nanjing Nanda Instrument Plant, China). Rice flour was sifted by sieve (100-mesh) and then moisture content of each group of samples was measured. All the samples were processed according to the same procedure. 2.4. Digestion of samples and determination of mineral elements Rice flour samples were subjected to acid digestion in a closedvessel microwave reaction system (Mara 240/50 microwave digestion instrument; CEM Microwave Technology Ltd., Buckingham, UK). About 0.25 g (dry basis) of finely ground rice flour sample was weighed into 2

Journal of Food Composition and Analysis 83 (2019) 103276

L. Qian, et al.

Table 2 Data of rice samples. Origin

Chahayang rice protection area

Jiansanjiang rice protection area

Wuchang rice protection area

Sample number Variety

32 Longjing 25, Longjing 31, Longjing 39, and Longjing 21 Meadow chernozem 124°05′–124°14′ 48°07′–48°12′ 4.7 593 3015

25 Longjing 31, Kongyu 131, Longjing 26, Longjing 36, Longjing 39, Kendao18 Meadow albic soil 132°40′–134°25′ 47°01′–47°58′ 3.2 454 2477

35 Wuyoudao 4

Agrotype Longitude (E) Latitude (N) Average temperature (°C) Annual precipitation (mm) Sunshine hours (h)

Table 3 Quality control for determining the elements of rice.

Black soil 126°42′–127°45′ 44°25′–45°13′ 4.9 562 2149

Table 4 Mineral element contents in rice of different origins.

Mineral elements

LOD (μg/ kg)

LOQ (μg/ kg)

Mineral elements

LOD (μg/ kg)

LOQ (μg/ kg)

Mineral elements

Jansanjiang

Wuchang

Chahayang

Mg Al Ca Mn Fe Cu Zn K V Co Se Rh Ag Sb Cs Nd Eu Yb Te Ho

0.005 0.092 0.139 0.001 0.011 0.005 0.005 0.014 0.005 0.005 0.017 0.056 0.087 0.006 0.001 0.017 0.014 0.01 0.01 0.013

0.017 0.307 0.046 0.004 0.037 0.017 0.016 0.047 0.017 0.016 0.057 0.187 0.027 0.020 0.001 0.055 0.046 0.034 0.034 0.042

As Sr Mo Cd Ba Pb Na Cr Ni Rb Pd La Pr Sm Gd Dy Er Pt Tl U

0.008 0.146 0.742 0.006 0.009 0.004 0.009 0.007 0.005 0.001 0.007 0.022 0.018 0.016 0.014 0.012 0.012 0.001 0.007 0.001

0.026 0.485 0.247 0.018 0.031 0.015 0.029 0.024 0.017 0.001 0.023 0.073 0.058 0.054 0.048 0.041 0.038 0.001 0.023 0.003

Mg Ca Cr Mn Zn Rb Sr Co Ag V Sb As Se Rh Te Ba Nd Cs Gd Eu Dy Ho Er Yb Tl Pb Mo U

0.325 ± 0.0414a 85.3 ± 43.2a 0.0692 ± 0.112a 8.17 ± 2.05c 14.3 ± 1.75a 0.764 ± 0.353ab 0.137 ± 0.0724ab 17.6 ± 10.4a 4.27 ± 10.7a 24.9 ± 5.29b 2.94 ± 4.59a 2.68 ± 2.48a 20.1 ± 12.6a 0.301 ± 0.267ab 0.413 ± 0.445b 0.111 ± 0.0523ab 1.09 ± 0.784b 0.284 ± 0.110a 0.187 ± 0.164b 0.254 ± 0.231a 0.113 ± 0.154b 0.224 ± 0.0221a 0.0101 ± 0.0332c 0.0717 ± 0.0928b 0.324 ± 0.187a 0.0245 ± 0.0347a 1.04 ± 0.543a 0.775 ± 0.921b

0.217 ± 0.0396c 84.4 ± 15.5b 0.0627 ± 0.0275b 15.5 ± 3.29a 13.2 ± 1.42c 1.55 ± 0.764a 0.154 ± 0.160a 10.0 ± 7.77b 4.16 ± 5.64ab 24.4 ± 7.07b 3.11 ± 2.95a 2.69 ± 2.04a 17.6 ± 11.5ab 0.428 ± 0.21a 0.247 ± 0.369b 0.142 ± 0.0727a 1.45 ± 0.916a 0.332 ± 0.154a 0.209 ± 0.160ab 0.142 ± 0.0840b 0.164 ± 0.151ab 0.102 ± 0.0714b 0.0142 ± 0.0324b 0.0485 ± 0.0914c 0.0824 ± 0.0547b 0.0263 ± 0.0440a 0.394 ± 0.0872b 1.00 ± 1.27a

0.313 ± 0.0347b 70.8 ± 12.6c 0.0719 ± 0.0278ab 9.21 ± 2.00b 13.9 ± 1.68b 0.517 ± 0.191b 0.114 ± 0.0194b 8.18 ± 2.09b 3.24 ± 5.60b 30.0 ± 6.04a 1.64 ± 1.49a 0.747 ± 0.201a 9.04 ± 4.70b 0.211 ± 0.0927b 0.450 ± 0.642a 0.114 ± 0.0743b 1.39 ± 1.62b 0.411 ± 0.134b 0.244 ± 0.276a 0.194 ± 0.182b 0.172 ± 0.178a 0.142 ± 0.0341ab 0.0332 ± 0.114a 0.104 ± 0.100a 0.294 ± 0.202a 0.0242 ± 0.0124a 0.507 ± 0.153b 0.864 ± 1.04b

55-mL Teflon vessels containing 6 mL of 70% HNO3 (Beijing Institute of Chemical Reagents, Beijing, China) and 2 mL of 30% H2O2 (Beijing Institute of Chemical Reagents, Beijing, China). The 40-min digestion program consisted of the following 3 steps. In Step 1, the power ramped up from 0 W to 1600 W and the temperature was increased to 120 °C in 8 min and maintained for 2 min. In Step 2, the temperature was increased from 120 °C to 160 °C in 5 min and maintained for 5 min. In Step 3, the temperature was increased from 160 °C to 180 °C in 5 min and maintained for 15 min. At the end of the digestion, samples were cooled to room temperature and then the vessels were transferred to a fume cupboard to remove the remaining acid in a DV4000 electrothermal digestion instrument (Beijing Annan Technology Co., Beijing, China). The filtrates were diluted with 18.2 MΩ cm ultrapure water (Milli-Q; Millipore, Billerica, MA) until the total solution weight was approximately 100 g. Standard samples and blank samples of rice were digested according to the same procedure. Analytical quality was checked against the certified values of the quality control sample (GBW08502; Standard Material Purchase Center, Beijing, China). The operating conditions of ICP-MS equipment (Agilent 7700 Series; Agilent, Santa Clara, CA) were described as follows. RF power (1280 W), the temperature of spray chamber (2 °C), the flow rate of cooling water (1.47 L min−1), the flow rate of auxiliary gas (1.0 L min−1), and the flow rate of compensative gas (1.0 L min−1). The contents of 52 mineral elements (Na, Mg, Al, K, Ca, Sc, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Y, Mo, Ru, Rh, Pd, Ag, Cd, Sn, Sb, Te, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Hf, Ir, Pt, Au, Tl, Pb, Th, and U) in rice were measured with the ICP-MS. In the measurement process, the recovery rates of all elements in the rice

The units of the content of Mg, Ca, Cr, Mn, Zn, Rb, Sr, As, Rh, Ba, Pb and Mo are mg/kg and the units for other minerals are μg/kg. Numbers labeled with the same letter are not significantly different (p < 0.05).

standard should be greater than 90%. Table 3 lists limit of detection (LOD) and limit of quantification (LOQ). If a certain mineral element could not be detected in at least 2/3 of total samples, the data of the mineral element would not be utilized for further analysis. Three independent tests were carried out for each group of samples under repeatability conditions. Ge, In and Bi were selected as internal standard elements. Rice samples would be retested if the relative standard deviation (RSD) of internal standard elements was higher than 5%.

2.5. 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 samples from the fields treated with different dosage levels of fertilizers and pesticides. Fisher discriminant analysis was performed to establish a discriminant model for identifying the geographical origin of rice samples with SPSS Version 18.0 for Windows (SPSS Inc., Chicago, IL). 3

Journal of Food Composition and Analysis 83 (2019) 103276

L. Qian, et al.

Table 5 Effects of different dosage levels of fertilizers on mineral elements in rice. Mineral elements

High

Medium

Low

Blank

Na Mg Al Ca V Cr Mn Fe** Co** Ni** Se** Rh** Eu** Tl** Cu Zn As* Rb Sr Mo* Ag Cd Sb Ba La Pr** Nd Sm Dy Pt** Pb U

6.94 ± 0.164 245 ± 3.26 5.86 ± 0.198 73.5 ± 1.49 9.91 ± 0.757 130 ± 5.89 13.5 ± 0.356 16.2 ± 0.649 21.4 ± 1.09 676 ± 30.8 48.9 ± 3.38 545 ± 10.3 1.83 ± 0.0333 2.12 ± 0.119 3.38 ± 0.0919 16.0 ± 0.752 83.4 ± 4.14 1.02 ± 0.0591 180 ± 5.79 0.607 ± 0.0116 11.7 ± 0.361 26.7 ± 1.88 10.1 ± 0.566 78.1 ± 1.92 3.00 ± 0.285 5.64 ± 0.143 2.23 ± 0.171 1.05 ± 0.0873 0.327 ± 0.0319 16.6 ± 1.39 30.8 ± 1.78 1.21 ± 0.122

7.37 ± 0.214 239 ± 4.85 5.84 ± 0.236 76.1 ± 1.03 8.80 ± 0.322 151 ± 10.8 14.0 ± 0.276 10.8 ± 0.0728 17.0 ± 1.20 271 ± 9.16 10.3 ± 0.518 164 ± 1.06 0.993 ± 0.0191 1.74 ± 0.134 3.18 ± 0.0840 15.2 ± 0.419 133 ± 2.54 1.12 ± 0.0662 167 ± 9.99 0.497 ± 0.0188 11.2 ± 0.279 22.6 ± 0.551 12.7 ± 0.231 81.0 ± 1.52 3.23 ± 0.229 0.572 ± 0.0578 2.04 ± 0.173 0.965 ± 0.0992 0.329 ± 0.0338 3.89 ± 0.276 30.6 ± 2.73 1.04 ± 0.107

7.22 ± 0.254 241 ± 6.36 6.05 ± 0.123 72.5 ± 0.486 10.0 ± 0.828 133 ± 3.39 12.2 ± 0.414 7.09 ± 0.144 10.6 ± 0.472 673 ± 38.8 4.57 ± 0.0846 234 ± 8.95 0.214 ± 0.0124 0.328 ± 0.0290 3.25 ± 0.154 16.7 ± 0.566 71.6 ± 1.79 1.08 ± 0.0624 180 ± 12.9 0.256 ± 0.0261 10.5 ± 0.471 26.0 ± 0.950 9.93 ± 0.376 80.3 ± 2.94 3.08 ± 0.194 1.04 ± 0.0742 2.01 ± 0.190 0.943 ± 0.0727 0.311 ± 0.0265 8.06 ± 0.767 30.0 ± 2.85 1.44 ± 0.490

6.98 ± 0.117 246 ± 4.99 6.25 ± 0.250 79.0 ± 3.19 9.78 ± 0.310 142 ± 4.60 115 ± 2.38 10.0 ± 0.0686 7.79 ± 0.199 360 ± 2.33 2.10 ± 0.0397 99.8 ± 1.92 0.173 ± 0.0144 0.373 ± 0.0196 3.23 ± 0.107 17.1 ± 0.337 82.9 ± 2.06 1.01 ± 0.0467 171 ± 12.4 0.369 ± 0.0311 9.84 ± 0.567 21.3 ± 0.707 10.6 ± 0.343 73.9 ± 1.99 2.62 ± 0.224 1.48 ± 0.142 1.93 ± 0.213 0.916 ± 0.0656 0.331 ± 0.0231 5.13 ± 0.415 31.9 ± 2.46 1.09 ± 0.0984

The experimental values in this table are expressed as mean ± standard deviation. The units of the contents of Na, Mg, Al, Ca, Mn, Fe, Cu, Zn and Rb are mg/kg and units for the other minerals are μg/kg. * The element differs significantly between different groups (pY < 0.05). ** The element differs significantly between different groups (p < 0.01).

3. Results

different dosages of pesticides (pY < 0.01). With the increase of the dosages of pesticides, the content of Al rose, whereas the contents of Co and Ni increased firstly and then decreased. The content of Ni in the low dose group was 2.25 times that in the medium dose group and 2.04 times that in the high dose group.

3.1. Differences in the contents of mineral elements among the rice samples of different geographical origins The contents of mineral elements in rice samples from Jansanjiang, Wuchang and Chahayang were assayed and the mean and standard deviation were recorded (Table 4). There were significant differences in the contents of Mg, Ca, Cr, Mn, Zn, Rb, Sr, Co, Ag, V, Sb, As, Se, Rh, Te, Ba, Nd, Cs, Gd, Dy, Ho, Er, Yb, Tl, Pb, Mo and U among rice samples from different areas (pY < 0.05).

3.4. Canonical discriminant analysis In order to validate the geographical origin traceability, the relevant elements significantly affected by fertilizers and pesticides were excluded (Co, As, Se, Rh, Tl, and Mo). Fisher multivariate discriminant analysis model was established by stepwise discriminant analysis based on other elements and the cross-validation was adopted to verify the accuracy of the model. Finally, predictive models were established with the data of the seven elements of Mg, V, Cr, Rb, Sr, Cs, and Hf as follows:

3.2. Differences in the contents of mineral elements among the rice samples treated with different dosages of fertilizers The differences in the contents of mineral elements in rice samples from the fields treated with different dosages of fertilizers were analyzed (Table 5). There was extremely significant differences in the contents of Fe, Co, Ni, Se, Rh, Eu, Pr, Tl and Pt in rice samples from the fields treated with different dosages of fertilizers (pY < 0.01). The difference was also significant in the contents of As and Mo (pY < 0.05).

YWuchang = 0.185X1 (Mg) − 0.358X2 (V) − 0.008X3 (Cr) − 0.001X 4 (Rb) + 0.143X5 (Sr) + 3.016X6 (Cs) + 0.264X7 (Hf) − 34.749; YWuchang = 0.185X1 (Mg) − 0.358X2 (V) − 0.008X3 (Cr) − 0.001X 4 (Rb) + 0.143X5 (Sr) + 3.016X6 (Cs) + 0.264X7 (Hf) − 34.749;

3.3. Differences in the contents of mineral elements among the rice samples treated with different dosages of pesticides

YChahayang = 0.357X1 (Mg) − 0.074X2 (V) − 0.002X3 (Cr) − 0.003X 4 (Rb)

The differences in the contents of mineral elements in rice samples from the fields treated with different dosages of pesticides were analyzed (Table 6). There were extremely significant differences in the contents of Al, Co and Ni in rice samples from fields treated with

YJiansanjiang = 0.189X1 (Mg) − 0.107X2 (V) + 0.004X3 (Cr) + 0.0141X 4 (Rb)

− 0.002X5 (Sr) + 3.039X6 (Cs) + 0.039X7 (Hf) − 57.349;

+ 0.052X5 (Sr) − 6.441X6 (Cs) + 0.01X7 (Hf) − 28.664. The 4

cross

discrimination

rate

is

directly

related

to

the

Journal of Food Composition and Analysis 83 (2019) 103276

L. Qian, et al.

Table 6 Effects of different dosages levels of pesticides on mineral elements in rice. Mineral elements

High

Medium

Low

Blank

Na Mg Al** K Ca V Cr Mn Fe Co** Ni** Cu Zn As Rb Sr Mo Ag Cd Sb Ba La Pr Nd Gd Pt Pb U

8.34 ± 0.205 205 ± 8.26 45.3 ± 1.58 732 ± 41.9 83.3 ± 3.36 14.9 ± 1.23 66.3 ± 6.37 14 ± 0.604 4.46 ± 0.172 3.05 ± 0.208 260 ± 16.3 2.66 ± 0.0791 12.9 ± 0.547 103 ± 8.25 1.40 ± 0.0562 157 ± 11.0 515 ± 42.4 4.99 ± 0.433 13.2 ± 1.16 10.0 ± 0.699 101 ± 8.75 1.14 ± 0.0893 0.529 ± 0.0483 0.916 ± 0.0659 0.379 ± 0.0281 3.08 ± 0.262 25.3 ± 2.23 1.14 ± 0.101

9.58 ± 0.414 216 ± 7.97 10.9 ± 0.438 776 ± 47.6 88.8 ± 3.02 17.4 ± 1.23 64.0 ± 5.95 12.7 ± 0.467 5.96 ± 0.209 16.4 ± 1.31 286 ± 19.4 2.56 ± 0.0977 14.0 ± 0.539 106 ± 7.92 1.84 ± 0.0751 168 ± 10.6 549 ± 38.3 5.42 ± 0.436 15.1 ± 1.02 11.8 ± 0.849 105 ± 9.69 2.222 ± 0.166 0.572 ± 0.0524 0.985 ± 0.0857 0.345 ± 0.0256 3.30 ± 0.259 26.7 ± 1.94 1.00 ± 0.0659

9.09 ± 0.414 224 ± 7.60 4.76 ± 0.161 748 ± 42.7 89.0 ± 1.82 12.9 ± 1.07 60.6 ± 4.86 16.0 ± 0.688 3.38 ± 0.132 2.66 ± 0.190 586 ± 35.7 3.25 ± 0.0934 14.5 ± 0.575 94.3 ± 7.30 1.54 ± 0.0682 163 ± 11.1 536 ± 35.8 5.18 ± 0.448 12.9 ± 0.973 10.9 ± 0.931 94.2 ± 6.18 1.72 ± 0.148 0.550 ± 0.0485 0.919 ± 0.0871 0.304 ± 0.0244 3.18 ± 0.282 28.1 ± 2.21 0.914 ± 0.0771

9.30 ± 0.315 202 ± 8.71 4.75 ± 0.123 739 ± 51.5 78.2 ± 2.74 15.7 ± 1.45 61.5 ± 4.40 14.6 ± 0.457 4.75 ± 0.205 2.76 ± 0.208 509 ± 37.2 2.97 ± 0.0890 14.6 ± 0.527 101 ± 8.76 1.63 ± 0.0703 156 ± 9.46 507 ± 30.2 5.51 ± 0.389 14.2 ± 1.21 11.9 ± 1.09 103 ± 8.43 2.16 ± 0.169 0.611 ± 0.0515 0.967 ± 0.0814 0.316 ± 0.0237 3.01 ± 0.258 28.1 ± 1.87 0.962 ± 0.0549

The experimental values in this table are expressed as mean ± standard deviation. The units of the contents of Na, Mg, Al, Ca, Mn, Fe, Cu, Zn and Rb are mg/kg and the units of the other minerals are μg/kg. ** The element differs significantly between different groups (p < 0.01). Table 7 Results of stepwise discriminant analysis and cross validation with the common data of common mineral elements. Origin

Members of forecast group Wuchang

Initial Count Wuchang Chahayang Jiansanjiang % Wuchang Chahayang Jiansanjiang Total Cross-validation Count Wuchang Chahayang Jiansanjiang % Wuchang Chahayang Jiansanjiang Total

Chahayang

Table 8 Results of stepwise discriminant analysis and cross validation with the screened data of partial mineral elements.

Sum

Origin

Jiansanjiang

35 0 0

0 32 0

0 0 25

35 32 25

100 0 0 100

0 100 0 100

0 0 100 100

100 100 100 100

28 0 3

1 32 0

6 0 22

35 32 25

80 0 12 80

2.9 100 0 100

17.1 0 88 88

100 100 100 89.3

Count Initial Wuchang Chahayang Jiansanjiang % Wuchang Chahayang Jiansanjiang Total Cross-validation Count Wuchang Chahayang Jiansanjiang % Wuchang Chahayang Jiansanjiang Total

Members of forecast group

Sum

Wuchang

Chahayang

Jiansanjiang

34 0 0

0 32 0

1 0 25

35 32 25

97.1 0 0 97.1

0 100 0 100

2.9 0 100 100

100 100 100 98.9

34 0 0

0 31 0

1 1 25

35 32 25

97.1 0 0 97.1

0 96.8 0 96.8

2.9 3.2 100.0 100.0

100 100 100 97.8

variety (Table 8). Wang et al. (2017) once reported that the identification accuracy of different rice varieties was 10% lower than that of the same rice variety. The overall correct classification rate was 98.9% and the cross-validation rate was 97.8%. The classification chart was plotted according to the discriminant analysis results (Fig. 1). Rice samples from three areas were distributed in their respective regions. Although several overlapping points occurred, the overall distribution was regular.

discrimination effect. The cross discrimination rate of the model established with the common data of common mineral elements was 89.3% (Table 7), which was lower than that of the model established with the screened data of partial mineral elements (97.8%, Table 8). The difference showed that fertilizers and pesticides had an impact on the traceability. Therefore, excluding the elements affected by fertilizers and pesticides could improve the accuracy of traceability. Among 35 Wuchang rice samples, only 2 samples were misclassified and the misclassified results might be caused by the difference in rice 5

Journal of Food Composition and Analysis 83 (2019) 103276

L. Qian, et al.

Fig. 1. Canonical discriminant function analysis of rice samples.

4. Discussion

other elements in rice in this experiment. The application of mineral fertilizers (such as Zn fertilizer and Fe fertilizer) could increase the contents of corresponding mineral elements in plants (Kutman et al., 2010; Shivay et al., 2016). The pesticides used in this experiment were fungicides and herbicides. Fungicides can cause the reduction of chlorophyll in rice leaves. Moreover, fungicides may affect the microbial population, thus reducing the dissolution of minerals in the soil (Scheepmaker and Kassteele, 2011). Although oxadiazon and pretilachlor belong to conventional herbicides, they have a slight toxic effect on rice in practice. Both of them can damage the cellular tissue of plant roots (Reeves and Hess, 1980) and pretilachlor can inhibit the synthesis of protein and the activity of alpha-amylase (Devlin and Cunningham, 1970; Wilkinson, 1988). All these factors can affect the absorption of mineral elements in rice. However, the influences of pesticides on mineral elements in rice have been seldom reported because the toxicity of pesticide residues on human health was more significant in previous studies. Watanabe et al. (2015) analyzed the contents of water-soluble metabolites and mineral nutrients in tomatoes sprayed with pesticides by principal component analysis and indicated the significant effect of the application of pesticides on Fe in tomatoes. The differences in the contents of mineral nutrients and water-soluble metabolites caused by different fertilizers and pesticides also affected the quality of tomatoes. There are differences in the contents and types of mineral elements in different areas of the soil. The application of fertilizers and pesticides affects the absorption of certain mineral elements in plants. In order to ensure that the differences in the contents of mineral elements in rice was only related to the geological nature, the influences of fertilizers and pesticides on mineral elements should be excluded for tracing the origins of rice. The mineral elements significantly influenced by fertilizers were Fe, Co, Ni, Se, Rh, Eu, Pr, Tl, and Pt. The mineral elements significantly influenced by pesticides were Al, Co, and Ni. The prediction of the geographic origin by Fisher discrimination analysis after excluding the above elements gave an overall correct classification rate of 98.9% and a cross-validation rate of 97.8%. The results demonstrated that it was feasible to improve the geographical origin traceability of

The elements introduced by fertilizers (P, K) have positive effects on the contents of P and K in rice (Šrek et al., 2010; Hejcman et al., 2013). Different dosages of potassium fertilizer had a significant effect on the K content in rice (Amin et al., 2015). Traditional fertilizers have positive effects on the development and metabolism of plants. Ma et al. (2014) indicated that the application of P plus ammonium as ammonium sulfate (NH4-N + P) might be an efficient approach to improve maize growth and increase Zn and Fe accumulation because NH4-N + P could modify root traits (such as root length, lateral root proliferation and root length density) and intensify rhizosphere acidification by ammonium uptake. Hao et al. (2007) found that nitrogen fertilizer could promote the transportation capacity of mineral elements in rice roots. Li et al. (2015) found that the content of Se in vegetables decreased gradually with the long-term use of nitrogen fertilizer because the nitrogen fertilizer in the soil was converted into nitrate, which had a competitive and antagonistic effect on the absorption of Se in vegetables. A range of studies based on surveys and field experiments proved that fertilizers had an effect on the mineral elements in rice. Nutrient elements or harmful elements were usually used as the study object. Gimeno-García et al. (1996) found that traditional nitrogen and phosphate fertilizers (urea and superphosphate) contained Fe, Co, Ni, Mn and other mineral elements, which might be the most important factor in rice enriched by Fe, Co, Ni and other mineral elements. Yuan et al. (2006) found that the contents of Fe, Zn, Cu, Mn, Mg, and Ca in rice increased firstly and then decreased with the increase in the application amount of N fertilizer. The rational application of K fertilizer was positively correlated with the contents of Fe, Se, Cu and Mn, whereas the application of P fertilizer was negatively correlated with the contents of Fe, Cu, Mn, and Ca. Fertilizers had an impact on the discrimination of wheat varieties based on mineral elements (Zen, 2012). When the fertilizer was not applied, the contents of Mg, Fe, Zn, Cu, and Ni in wheat grains reflected the differences among varieties more completely. After the fertilizer was applied, the contents of Ca, Mn, Cr, and Pb in grain could more completely reflect the differences among varieties. Their findings were consistent with the effects of fertilizers on Fe, Co, Ni and 6

Journal of Food Composition and Analysis 83 (2019) 103276

L. Qian, et al.

rice based on mineral elements by excluding the influences of fertilizers and pesticides. In the study, rice samples were from a small geographical area. The categories of used fertilizers and pesticides were similar in the small geographical area, but their application dosages were different. Investigating mineral elements affected by fertilizers and pesticides provides the discrimination basis for screening traceable indices. When the traceable geographical area is large, various fertilizers and pesticides are applied due to the differences in soils and insect pests. Therefore, the differences in the mineral elements caused by the application of fertilizers and pesticides in a large area should be considered in the screening of traceable indices. Data collected from longterm field experiments will contribute to the establishment of more accurate discriminant models.

Kutman, U.B., Yildiz, B., Ozturk, L., Cakmak, I., 2010. Biofortification of durum wheat with zinc through soil and foliar applications of nitrogen. Cereal Chem. 87, 1–9. Li, S., Bañuelos, G.S., Min, J., Shi, W., 2015. Effect of continuous application of inorganic nitrogen fertilizer on selenium concentration in vegetables grown in the Taihu lake region of China. Plant Soil 393, 351–360. Li, Y.L., Zheng, Y.J., Tang, L., Su, Z.Y., Xiong, Q., 2016. Study on the Identification of geographical indication Wuchang rice based on the content of inorganic elements. Spectrosc. Spect. Anal. 36, 834–837 (in Chinese). Liu, H., Wei, Y., Zhang, Y., Wei, S., Zhang, S., Guo, B., 2017. The effectiveness of multielement fingerprints for identifying the geographical origin of wheat. Int. J. Food Sci. Technol. 52, 1018–1025. Ma, Q., Wang, X., Li, H., Li, H., Cheng, L., Zhang, F., 2014. Localized application of NH4+N plus P enhances zinc and iron accumulation in maize via modifying root traits and rhizosphere processes. Field Crops Res. 164, 107–116. Marion, B., Róbert, K., Zsolt, S., Thomas, P., 2010. Determination of the geographical origin of processed spice using multielement and isotopic pattern on the example of Szegedi paprika. Eur. Food Res. Technol. 231, 623–634. Moreno-Rojas, R., Sánchez-Segarra, P.J., Cámara-Martos, F., Amaro-López, M.A., 2010. Multivariate analysis techniques as tools for categorization of southern Spanish cheeses: nutritional composition and mineral content. Eur. Food Res. Technol. 231, 841–851. Reddy, N.S., Dash, S., Sontakke, 1997. Effect of spraying selected pesticides on the contents of specified minerals in cabbage. Plant Foods Hum. Nutr. 51, 357–363. Reeves, J.T., Hess, F.D., 1980. Use of an in vitro protein synthesizing system to test the mode of action of chloracetamides. Weed Sci. 28, 334–340. Rossi, F., Bertuzzi, T., Comizzoli, S., Turconi, G., Roggi, C., Pagani, M., et al., 2006. Preliminary survey on composition and quality of conventional and organic wheat. Ital. J. Food Sci. 18, 355–366. Rossi, F., Godani, F., Bertuzzi, T., Trevisan, M., Ferrari, F., Gatti, S., 2008. Health-promoting substances and heavy metal content in tomatoes grown with different farming techniques. Eur. J. Nutr. 47, 266. Shaaban, M.M., 2001. Effect of trace-nutrient foliar fertilizer on nutrient balance, growth, yield and yield components of two cereal crops. Pak. J. Biol. Sci. 4, 361–372. Shen, S., Xia, L., Xiong, N., Liu, Z., Sun, H., 2013. Determination of the geographic origin of rice by element fingerprints and correlation analyses with the soil of origin. Anal. Methods 5, 6177–6185. Shivay, Y.S., Prasad, R., Singh, U., 2016. Micronutrient fertilizers for zinc and iron enrichment in major food crops: a practicable strategy. Biofortification of Food Crops. Springer, India. Šrek, P., Michal, H., Eva, K., 2010. Multivariate analysis of relationship between potato (Solanum tuberosum L.) yield, amount of applied elements, their concentrations in tubers and uptake in a long-term fertilizer experiment. Field Crops Res. 118, 183–193. Suhaj, M., Koreňovská, M., 2008. Study of some European cheeses geographical traceability by pattern recognition analysis of multielemental data. Eur. Food Res. Technol. 227, 1419–1427. Széles, E., Borbély, M., Prokisch, J., Kovács, B., Hovánszki, D., Harizanis, P.C., 2006. Studying of quality parameters and organic contaminants in honey samples. Acta Geochim. 25, 68. Wang, C.H., Zhang, Y.T., Min, W.H., Wang, Y.H., Liu, J.S., 2017. Effects of rice varieties on accuracy of rice origin discrimination. J. Jilin Agric. Univ. 39, 113–119 (in Chinese). Wang, I.C., Hsu, J.C., Lu, S., 2011. Rice traceability system in Taiwan. Qual. Assur. Saf. Crops Foods 3, 74–81. Watanabe, M., Ohta, Y., Licang, S., Motoyama, N., Kikuchi, J., 2015. Profiling contents of water-soluble metabolites and mineral nutrients to evaluate the effects of pesticides and organic and chemical fertilizers on tomato fruit quality. Food Chem. 169, 387–395. Wilkinson, R.E., 1988. Consequences of metolachlor induced inhibition of gibberellin biosynthesis in sorghum seedlings. Pestic. Biochem. Physiol. 32, 25–37. Yang, X., Ye, Z.Q., Shi, C.H., Zhu, M.L., Graham, R.D., 1998. Genotypic differences in concentrations of iron, manganese, copper, and zinc in polished rice grains. J. Plant Nutr. 21, 1453–1462. Ye, X., Jin, S., Wang, D., Zhao, F., Yu, Y., Zheng, D., 2016. Identification of the origin of white tea based on mineral element content. Food Anal. Methods 10, 1–9. Yuan, J.C., Liu, C.J., Zhe, E.S., Yang, S.M., Zhu, Q.S., Yang, J.C., 2006. Effect of nitrogen application rate and fertilizer ratio on nutrition quality and trace-elements contents of rice grain. Plant Nutr. Fertil. Sci. 12, 182–183. Zen, Y.L., 2012. Effect of Nitrogen and Phosphorus Fertilizer Rate on Yield, Nutrient Utilization and Grain Mineral Nutrient Quality of Wheat in Dryland. Northwest A&F University. Zhao, H., Guo, B., Wei, Y., Zhang, B., 2012. Effects of wheat origin, genotype, and their interaction on multielement fingerprints for geographical traceability. J. Agric. Food Chem. 60, 10957. Zhao, H., Guo, B., Wei, Y., Zhang, B., 2013. Multi-element composition of wheat grain and provenance soil and their potentialities as fingerprints of geographical origin. J. Cereal Sci. 57, 391–397 2013. Zhao, H., Zhang, S., Zhang, Z., 2017. Relationship between multi-element composition in tea leaves and in provenance soils for geographical traceability. Food Control 76, 82–87. Zhao, Y., Zhang, B., Chen, G., Chen, A., Yang, S., Ye, Z., 2014. Recent developments in application of stable isotope analysis on agro-product authenticity and traceability. Food Chem. 145, 300.

Acknowledgments This work was funded by Heilongjiang Province Applied Technology Research and Development Project (GA14B104), Thirteenth Five-Year Key Scientific and Technological Projects of the Reclamation Bureau of Heilongjiang Province (HNK135-06-06), Heilongjiang Natural Science Foundation Joint Guidance Project (LH2019C075) and National key research and development program “Grain transportation from north to south” integrated research and development of key technologies and equipment for anti-condensation and anti-mildew in container transportation of bulk grain (2018YFD0401403-04). References Amin, M.S., Hossain, M.Z., Rabbi, S.M.F., Islam, S., Khanom, S., Rahman, A., 2015. Effect of salinity and potassium fertilizer application on yield contributing characteristics of rice. Khulna Univ. Stud. 10, 145–152. Andersson, A., Bingefors, S., 1985. Trends and annual variation in Cd concentrations in Swedish wheat and barley. Arch. Environ. Health 30, 321–328. Andersson, A., Simon, G., 1991. Levels of Cd and some other trace elements in soils and crops as influenced by lime and fertilizer level. Acta Agric. Scand. 41, 3–11. Bengtsson, H., Öborn, I., Jonsson, S., Nilsson, I., Andersson, A., 2003. Field balances of some mineral nutrients and trace elements in organic and conventional dairy farming-a case study at Öjebyn, Sweden. Eur. J. Agron. 20, 101–116. Choi, Y.H., Hong, C.K., Kim, M., Sun, O.J., Park, J., Oh, Y.H., 2017. Multivariate analysis to discriminate the origin of sesame seeds by multi-element analysis inductively coupled plasma-mass spectrometry. Food Sci. Biotechnol. 26, 375–379. Chudzinska, M., Debska, A., Baralkiewicz, D., 2012. Method validation for determination of 13 elements in honey samples by ICP-MS. Accred. Qual. Assur. 17, 65–73. Devlin, R.M., Cunningham, R.P., 1970. The inhibition of gibberellic add induction of aamylase activity in barley endosperm by certain herbicides. Weed Res. 10, 316–320. Ghezzi, L., Arienzo, I., Buccianti, A., Demarchi, G., Petrini, R., 2017. Highly radiogenic Srisotopic signature and trace element content of grape musts from northern Piedmont vineyards (Italy). Eur. Food Res. Technol. 244, 1–9. Gimeno-García, E., Andreu, V., Boluda, R., 1996. Heavy metals incidence in the application of inorganic fertilizers and pesticides to rice farming soils. Environ. Pollut. 92, 19–25. Gonzálvez, A., Guardia, S.A.D.L., 2011. Geographical traceability of “arròs de valencia” rice grain based on mineral element composition. Food Chem. 126, 1254–1260. Gundersen, V., Bechmann, I.E., Annette Behrens, A., Stürup, S., 2000. Comparative investigation of concentrations of major and trace elements in organic and conventional Danish agricultural crops. 1. Onions (Allium cepa Hysam) and peas (Pisum sativum ping pong). J. Agric. Food Chem. 48, 6094–6102. Hao, H., Wei, Y., Yang, X., Feng, Y., Wu, C., 2007. Effects of different nitrogen fertilizer levels on concentrations of Fe, Mn, Cu and Zn and grain quality in rice Oryza sativa. Chin. J. Rice Sci. 14, 289–294. Hedlund, B., Eriksson, J., Peterson-Grawe, K.P., Oborn, I., 1997. Kadmium-Tillstand och Trender (Cadmium-Levels and Trends); no. 4759. Swedish Environmental Protection Agency, Research and Development Department, Stockholm, Sweden. Hejcman, M., Berková, M., Kunzová, E., 2013. Effect of long-term fertilizer application on yieldand concentrations of elements (N, P, K, Ca, Mg, As, Cd, Cu, Cr, Fe, Mn, Ni, Pb, Zn) in grain of spring barley. Plant Soil Environ. 59, 329–334. Scheepmaker, J.W.A., Kassteele, J.V.D., 2011. Effects of chemical control agents and microbial biocontrol agents on numbers of non-target microbial soil organisms: a meta-analysis. Biocontrol Sci. Technol. 21, 1225–1242. Koreňovská, M., Suhaj, M., 2005. Identification of some slovakian and European wines origin by the use of factor analysis of elemental data. Eur. Food Res. Technol. 221, 550–558.

7