Journal of Food Quality

Journal of Food Quality / 2021 / Article
Special Issue

Applications of Mass Spectrometry in the Analysis of Food Composition and Contaminants

View this Special Issue

Research Article | Open Access

Volume 2021 |Article ID 5536241 | https://doi.org/10.1155/2021/5536241

Yan Wang, Xiaoxuan Yuan, Linan Liu, Junmei Ma, Sufang Fan, Yan Zhang, Qiang Li, "Multielement Principal Component Analysis and Origin Traceability of Rice Based on ICP-MS/MS", Journal of Food Quality, vol. 2021, Article ID 5536241, 12 pages, 2021. https://doi.org/10.1155/2021/5536241

Multielement Principal Component Analysis and Origin Traceability of Rice Based on ICP-MS/MS

Academic Editor: Antonio J. Signes-Pastor
Received02 Mar 2021
Revised03 Sep 2021
Accepted06 Sep 2021
Published24 Sep 2021

Abstract

In this experiment, inductively coupled plasma tandem mass spectrometry (ICP-MS/MS) was used to determine the content of 30 elements in rice from six places of production and to explore the relationship between the multielement content in rice and the producing area. The contents of Ca, P, S, Zn, Cu, Fe, Mn, K, Mg, Na, Ge, Sb, Ba, Ti, V, Se, As, Sr, Mo, Ni, Co, Cr, Al, Li, Cs, Pb, Cd, B, In, and Sn in rice were determined by ICP-MS/MS in the SQ and MS/MS mode. By passing H2, O2, He, and NH3/He reaction gas into the ICP-MS/MS, respectively, the interference was eliminated by means of in situ mass spectrometry and mass transfer. The detection limit of each element was 0.0000662–0.144 mg/kg, and the limit of quantification was in the range of 0.000221–0.479 mg/kg, the linear correlation coefficient was greater or equal to 0.9987 (R2 ≥ 0.9987), and the detection results had low detection limit and great linear regression. Recovery of the method was in the range of 80.6% to 110.5% with spike levels of 0.10–100.00 mg/kg, and relative standard deviations were lower than 10%. For the multielement content of rice from different producing areas, the principal component factor analysis can get six principal component factors, 87.878% cumulative contribution rate, and the distribution of the principal component scores of each element and different producing areas. Based on the multielement content and cluster analysis, the samples were accurately divided into two major categories and six subcategories according to the places of production, which proved that there was a significant correlation between the multielement content in rice and the place of production, so that the place of rice origin can be traced.

1. Introduction

Rice is the main staple food of our country, which contains sugar, protein, fat and dietary fiber, and other main nutrition elements and also contains a lot of necessary trace elements, such as Ca, Fe, Zn, Se, and other mineral elements [1]. Heavy and toxic metals, especially As and Cd, present due to environmental pollution are taken up by the rice plant [25]. In China, rice varieties are rich and diverse, with large planting area span and large quality difference. China is a vast country with diverse climatic and geographical conditions, and the crops have different biological characteristics and physical and chemical indexes. Therefore, it is valuable to analyze and compare the differences of multielement contents in rice from south to north China and to provide theoretical basis and technical support for distinguishing rice from different places of origin.

At present, the origin traceability indexes in food mainly include stable isotope [611], multielement composition [12, 13], characteristic content of organic component [14, 15], DNA fingerprint [16], and near-infrared spectrum [1721]. There are some common problems in multielement analysis, such as few element types, high detection limit of low content elements, and unquantifiable trace elements. The determination methods of poly element contents in rice include an electrochemical method, atomic absorption spectroscopy (AAS) [22], atomic fluorescence spectrometry (AFS) [23], inductively coupled plasma optical emission spectrometer (ICP-OES) [24], and inductively coupled plasma mass spectrometry (ICP-MS) [25, 26]. However, there are some common problems in these methods, which hamper the rapid determination of trace elements in rice, thus causing the reduction of the accuracy of traceability of multielement composition in rice. The advantage of the ICP-MS/MS method is to reduce elemental interference [27]. Alexander Simpson et al. [28]. found that by using ICP-MS/MS and NH3 reagent gas, isotope interference can be reduced and the sensitivity of 176Lu and 176Yb can be improved. In terms of multielement determination, ICP-MS/MS has higher accuracy and greater diversity of elements than ICP-MS [29]. Among them, when measuring P, S, Sr, and other specific elements, O2 and other reaction gases were used to accurately determine the element content by mass transfer [30, 31]. The advantage of the ICP-MS/MS method lies in the determination of ultratrace elements [32]. It can reduce the detection limit, which cannot be achieved by general ICP-MS and other detection methods.

In this paper, ICP-MS/MS was first used to determine the 30 elements’ contents in rice from six rice-production areas in Anhui, Guangxi, Guangdong, Jilin, Heilongjiang, and Inner Mongolia. Under SQ and MS/MS models [33], He, NH3/He [34], O2 [35], and H2 [36] were selected as reactive gases for different elements to eliminate mass spectrum interference and reduce detection limit, and the relationship between the element content and the origin was studied by principal component analysis and cluster analysis, which provides technical support for quality control and origin traceability of rice.

2. Materials and Methods

2.1. Reagents and Solutions

Li, Na, Mg, Al, B, P, S, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ge, As, Se, Sr, Mo, Cd, In, Sn, Sb, Cs, Ba, Pb, Sc, Bi, Rh, and Y element standard solution (1000 μg/mL, Guobiao Testing & Certification Co., Ltd., Beijing, China), GBW0043, GBW10044, and GBW10045 Rice reference materials (Institute of Geophysical and Geochemical Exploration IGGE, Langfang, China), 65% BV-III grade of HNO3 (Beijing Institute of Chemical Reagents, Beijing, China), 30% H2O2 (Sinopharm Chemical Reagent Co., Ltd., Beijing, China), and deionized water (18.2 MΩ/cm), prepared by using the Milli-Q system (Millipore, Bedford, MA), were used.

2.2. Sample Collection and Preparation

The samples were collected from six rice-producing areas in Anhui Province, Guangxi Province, Guangdong Province, Jilin Province, Heilongjiang Province, and Inner Mongolia. We purchased common local rice samples with large planting areas in each rice market, a total of 18 batches. Three independent packages were purchased for each batch, and mixed samples were taken to ensure uniformity. The rice samples of each batch were hulled, ground, crushed, and stored in a sealed, low-temperature, and dark place.

In a PTFE digestion tank, each rice sample which weighs 0.3–0.5 g (accurate to 0.001 g) was added to 4 mL HNO3 and 1 mL 30% H2O2 and soaked for 3–4 h or overnight, the upper cap was screwed, and it was digested with the microwave digestion instrument (CEM MARS6, CEM, Matthews, USA). The conditions of the microwave digestion instrument are shown in Table 1. Then, they were placed on the temperature-controlled electric heating plate (BHW-09C, Shanghai Botong Chemical Technology Co., Ltd., Shanghai, China) and heated at for 20–30 min for degassing. After cooling, the samples were diluted to 50 mL with deionized water and shook well for later use. For each group of samples, blanks (deionized water and reagents) and reference materials were included throughout the entire sample preparation and analytical process.


StepClimbing time (min)Hold time (min)Temperature (°C)Power (W)

106 : 0003 : 001201500
208 : 0006 : 001551500
308 : 0015 : 001801500

2.3. Inductively Coupled Plasma Tandem Mass Spectrometry Analysis

This experiment was carried out by tandem mass spectrometry. The concentration of 30 isotopes (7Li, 23Na, 24Mg, 27Al, 11B, 31P, 32S, 39K, 44Ca, 47Ti, 51V, 52Cr, 55Mn, 56Fe, 59Co, 60Ni, 63Cu, 66Zn, 72Ge, 75As, 78Se, 88Sr, 95Mo, 111Cd, 115In, 118Sn, 121Sb, 133Cs, 137Ba, and 208Pb) in rice was determined by inductively coupled plasma tandem mass spectrometry (Agilent 8900 Series, Agilent, USA). 1 μg/mL mixed solution of Li, Y, Co, Tl, Ce, and Mg was used as the tuning solution, and 0.10 rps speed of the peristaltic pump was used to continuously feed the solution. Through the tuning program, the conditions of no gas, H2, O2, He, and NH3/He multimode analysis methods were optimized. In the no-gas mode, the monitored ions were 7, 89, and 205. In the He mode, the monitored ions were 59, 89, and 205. In the H2 mode, the monitored ions were Q1 = Q2 = 59, 89, and 205. In the O2 mode, the monitored ions were Q1 = Q2 = 59, Q1 = 89/Q2 = 105, and Q1 = Q2 = 205. In the NH3/He mode, the monitored ions were Q1 = Q2 = 59, Q1 = 89/Q2 = 191, and Q1 = Q2 = 205. Under different modes, RF power was 1550 W, auxiliary gas was 0.90 L/min, plasma gas was 15.0 L/min, sampling depth was 8.0 mm, and extraction lens was −7.6 V. The instrument’s other conditions of ICP-MS/MS are shown in Table 2.


Instrument conditionsNo-gas modeH2 modeHe modeO2 modeNH3/He mode

Q1 deflection voltage(V)−3.01.0−3.01.01.0
Q2 deflection voltage(V)−3.0−18.0−15.0−10.0−12.0
Collision pool gasH2HeO2NH3/He
Gas flow rate of collision pool (L·min−1)7.05.04.54.5/1.0
Deflection voltage of eight-stage pole (V)−8.0−18.0−18.0−3.0−5.0

For the selection of element determination mode and reagent gas, this method involves two modes: the SQ (single quadrupole) standard mode and MS/MS tandem mode. There are He and no-gas reagent gas modes in the SQ mode and He, NH3/He, H2, O2, and no-gas reagent gas modes in the MS/MS mode. The elements are measured in all modes, and the mode with the lowest detection limit of each element is determined as the best measurement mode.

Through the measured experimental conditions and methods, the elements Sc, Y, Rh, and Bi were used as the internal standard elements. Analyzing the experimental data can get a linear fitting standard curve with the X-axis as the concentration point and the Y-axis as the response value. Through this standard curve, the detection limit and background equivalent concentration of the analysis element can be obtained by calculating the element standard deviation. The linear correlation coefficient and range, internal standard elements, limit of detection (LOD), and limit of quantification (LOQ) are shown in Table 3.


Calibration (μg/L)R2Internal standardLOD (mg/kg)LOQ (mg/kg)

7Li0∼1000.9998Sc0.0006590.0220
23Na0∼10000.9999Sc0.1440.479
24Mg0∼10000.9999Sc0.003720.0124
27Al0∼1000.9987Sc0.09070.302
11B0∼1000.9995Sc0.03750.125
31P0∼10000.9994Sc0.02040.0679
32S0∼10000.9997ScO0.06140.205
39K0∼10001.0000Sc0.02910.0970
44Ca0∼10001.0000Sc0.1050.351
47Ti0∼1000.9999ScO0.002190.00731
51V0∼1000.9999ScO0.0009460.00315
52Cr0∼1000.9997ScO0.005210.0174
55Mn0∼10001.0000Sc0.001480.00495
56Fe0∼10001.0000Sc(NH3)20.01700.0567
59Co0∼1001.0000Sc(NH3)20.0005100.00170
60Ni0∼1001.0000Sc(NH3)20.001330.00444
63Cu0∼10001.0000Sc0.004740.0158
66Zn0∼10001.0000Sc0.01150.0383
72Ge0∼1001.0000Y0.0002060.000686
75As0∼1001.0000YO0.002500.00832
78Se0∼1001.0000Y0.001520.00507
88Sr0∼1001.0000Y0.0009290.00310
95Mo0∼1000.9999YNH30.0006050.00202
111Cd0∼1001.0000Rh0.00006620.000221
115In0∼1000.9998Rh0.001230.00410
118Sn0∼1001.0000Rh0.001760.000588
121Sb0∼1001.0000Rh0.0001960.000653
133Cs0∼1001.0000Rh0.0002270.000756
137Ba0∼1001.0000Rh0.001580.00525
208Pb0∼1001.0000Bi0.0009490.00317

At the same time, the content of each element in rice reference materials (GBW10043, GBW10044, and GBW10045) was determined, the standard value was compared, and the recovery rate was calculated to prove the accuracy and reliability of the method, and the recovery experiment was conducted.

2.4. Statistical Analysis

All analyses were conducted in triplicate. The results reported were the average of these three replicates. Each sample was considered as an assembly of 30 variables represented by the data of chemical information. The analysis data and the fitted linear regression curve were analyzed by Agilent Mass Hunter software (Agilent Inc., USA). A normal distribution test of multielements, principal component analysis, and clustering analysis were performed with SPSS 25.0 software (SPSS, IBM Corp., USA).

3. Results and Discussion

3.1. Mass Spectrometry Mode Selection and Interference Elimination

In this experiment, the SQ (single quadrupole) standard mode and MS/MS tandem mode were used to simultaneously determine the concentration of multielement. The elements were measured in different modes and different reaction gas modes, and the element detection limit was used as the criterion to determine the best measurement mode for each element. The results are shown in Table 4.


ModeReaction gasMass numberEliminate interference

7LiMS/MSNH3/HeQ1 = Q2 = 7In situ mass spectrometry
23NaMS/MSH2Q1 = Q2 = 23In situ mass spectrometry
24MgMS/MSNH3/HeQ1 = Q2 = 24In situ mass spectrometry
27AlMS/MSH2Q1 = Q2 = 27In situ mass spectrometry
11BMS/MSNH3/HeQ1 = 11, Q2 = 60Mass transfer
31PMS/MSO2Q1 = 31, Q2 = 47Mass transfer
32SMS/MSO2Q1 = 32, Q2 = 48Mass transfer
39KMS/MSO2Q1 = Q2 = 39In situ mass spectrometry
44CaMS/MSNH3/HeQ1 = Q2 = 44In situ mass spectrometry
47TiMS/MSO2Q1 = 47, Q2 = 63Mass transfer
51VMS/MSO2Q1 = 51, Q2 = 67Mass transfer
52CrMS/MSO2Q1 = 52, Q2 = 68Mass transfer
55MnMS/MSH2Q1 = Q2 = 55In situ mass spectrometry
56FeMS/MSNH3/HeQ1 = 56, Q2 = 90Mass transfer
59CoMS/MSNH3/HeQ1 = 59, Q2 = 93Mass transfer
60NiMS/MSNH3/HeQ1 = Q2 = 60In situ mass spectrometry
63CuSQHeQ2 = 63
66ZnMS/MSH2Q1 = Q2 = 66In situ mass spectrometry
72GeMS/MSH2Q1 = Q2 = 72In situ mass spectrometry
75AsMS/MSO2Q1 = 75, Q2 = 91Mass transfer
78SeMS/MSH2Q1 = Q2 = 78In situ mass spectrometry
88SrMS/MSH2Q1 = Q2 = 88In situ mass spectrometry
95MoMS/MSNH3/HeQ1 = Q2 = 95In situ mass spectrometry
111CdSQNo gasQ2 = 111
115InMS/MSH2Q1 = Q2 = 115In situ mass spectrometry
118SnSQHeQ2 = 118
121SbSQNo gasQ2 = 121
133CsMS/MSO2Q1 = Q2 = 133In situ mass spectrometry
137BaMS/MSNH3/HeQ1 = Q2 = 137In situ mass spectrometry
208PbSQNo gasQ2 = 208

The interference was eliminated by making full use of the collision mode between the element and the reaction gas. In the SQ mode, the mass ions of 63Cu, 111Cd, 118Sn, 121Sb, and 208Pb had the characteristics of high abundance value and less interference. The corresponding Q2 mass number was the only one that needs to be set during the determination.

In the MS/MS mode, the NH3/He mixture gas collided with 7Li, 24Mg, 44Ca, 60Ni, 95Mo, and 137Ba ions in the reaction cell, H2 collided with 23Na, 27Al, 55Mn, 66Zn, 72Ge, 78Se, 88Sr, and 115In ions, and O2 collided with 39K and 133Cs ions, respectively. The interference was eliminated by in situ mass spectrometry, which means the elements only collide with the reaction gas and do not combine with each other. Therefore, the mass number of the front and after tetrodes to be set remains unchanged (Q1 = Q2). However, the system will still have the same amount of heterotopic number signal superposition interference and double charge ion interference; for example, ions Ni++, SiH, CO, and NO may interfere with 31P; ions Zn++, NO, and OO may interfere with 32S; ions CAr and ArO interfere with 52Cr; ions ArCl, CaCl, and CoO interfere with 75As; ions ArO and MnH interfere with 56Fe; and ions Sn++, NiH, and MgCl interfere with 59Co. Therefore, in the determination of some specific elements, if the reactant gas and the element collide with each other to generate ions with a new mass number, the abovementioned interferences can be better avoided. In addition, when the gas collided with the analysis element, new mass ions were formed in the reaction, that is, mass transfer (Figure 1). In the experiment, NH3/He mixture gas can react with 11B+ and 56Fe+ to form 11B49NH(NH3)2+ and 56Fe(14NH3)2+ cluster ions. Also, the O2 mode was more widely used, which can undergo mass transfer with 31P+, 32S+, 47Ti+, 51V+, 52Cr+, 75As+, and generated 31P16O+, 32S16O+, 47Ti16O+, 51V16O+, 52Cr16O+, and 75As16O+ cluster ions, respectively.

3.2. Standard Material Determination and Precision

Multielement determination was performed on the standard materials GBW10043, GBW10044, and GBW10045, and the results are shown in Table 5. The average recoveries of element content of reference materials were in the range between 82.9% and 115%.


Recovery (%)AsBBaCaCdCoCrCsCuFeGeKLiMgMnMoNaNiPPbSSbSeSrSnTiVAlZnIn

GBW1004392.691.692.695.210989.491.710392.210410696.592.810596.588.695.390.610289.595.310610595.6ND94.810592.695.6ND
GBW1004495.193.810410510890.293.510785.610810994.810710391.585.792.110510611590.710810996.1ND93.810990.294.1ND
GBW1004596.310310593.810296.510787.593.895.611295.911196.590.382.994.892.810310693.111010392.8ND91.111393.5103ND

ND means not detected.

The recoveries of analytes were evaluated by adding the standard solutions with three different concentration levels to the known amounts of samples. The data of recovery and precision are given in Table 6, and the average recoveries of element content in rice were in the range between 80.6% and 110.5%. The RSDs were in the range of 0.4%–8.9%. The measurement results show that this method has high accuracy and meets the requirements of analysis and measurement.


ElementBackground (mg/kg)Added (mg/kg)Recovery (%)RSD (%)

As0.12710105.33.5
1108.13.2
0.1104.61.1

B0.5061098.60.6
186.12.8
0.1105.81.6

Ba0.3511094.32.8
195.22.6
0.182.88.9

Ca41.610093.60.9
5095.42.2
10102.51.6

Cd0.027210107.65.8
192.50.9
0.1108.63.5

Co0.006751091.64.5
195.34.1
0.1106.80.9

Cr0.01381084.35.0
193.51.5
0.189.61.1

Cs0.0015210110.50.6
192.32.8
0.194.22.3

Cu2.061095.10.5
196.74.1
0.197.64.8

Fe2.4510105.62.3
1103.96.8
0.1107.50.5

Ge0.002611085.32.4
196.42.9
0.195.86.3

K51010084.32.5
5082.22.7
1093.64.1

Li0.0036510109.50.9
1106.42.2
0.193.65.4

Mg105100104.33.6
50106.41.1
1083.21.8

Mn7.461092.62.0
191.11.5
0.193.72.9

Mo0.4961094.50.4
1105.67.1
0.193.23.8

Na1.9710108.94.5
1103.74.8
0.182.61.5

Ni0.1681091.60.5
1106.30.8
0.187.51.6

P448100101.21.3
50104.52.9
1093.43.5

Pb0.001521089.96.6
1104.21.7
0.1106.60.9

S55210080.61.1
5085.90.7
1093.82.5

Sb0.00015410106.43.9
195.33.3
0.192.80.6

Se0.02741094.72.5
195.92.1
0.184.60.8

Sr0.16310106.40.7
1108.50.9
0.184.95.3

SnND10104.31.4
196.15.6
0.195.62.0

Ti0.08651085.31.7
191.80.8
0.185.97.6

V0.005941092.71.6
1108.51.5
0.1106.90.8

AlND1094.52.6
193.72.1
0.1105.21.0

Zn10.910092.36.8
5082.84.6
1094.63.7

InND1091.52.5
185.48.9
0.196.31.9

ND means not detected.
3.3. Multielement Analysis of Samples

There are obvious differences in the content of Ba, Ge, Co, Cu, Cr, Ti, S, Ca, Mg, Na, Li, and other elements in rice from different producing areas in north and south China. In southern China, there are differences in the content of Na, Mg, K, Ca, V, Ge, Cs, Ba, and other elements in rice produced in Anhui Province, Guangxi Province, and Guangdong Province. However, in northern China, there are obvious differences in the content of B, Na, Ca, P, Cr, Mn, Ni, Co, Zn, Sr, Mo, Cs, and other elements in batches of rice in Jilin Province, Heilongjiang Province, and Inner Mongolia (Table 7). The contents of Al, In, and Sn were not detected.


LiBNaMoAlAsCaPSTiVCrMnNiCuFeSbPbGeInSrKSnCdSeMgCoCsBaZn

A-13.4701.910.54600.12441.543656977.65.1713.67.050.1222.112.370.13502.6200.148513026.821.596.66.681.570.31610.9
A-2001.350.48400.10640.546658175.33.6819.77.600.1142.551.950.022702.6400.120517052.034.398.27.531.130.29712.4
A-31.9001.730.54600.11740.249460572.64.8511.89.940.1302.392.150.06250.3682.7500.139524020128.289.97.491.200.30412.3
X-19.560.5064.800.50200.13448.354063045.68.335.228.430.1302.312.790.35016.61.9100.12170105.5938.91725.941.790.18711.8
X-24.840.3334.440.48500.11249.352862451.77.8109.540.1293.152.320.2452.762.4400.132739028.927.92006.291.910.19011.9
X-31.3903.630.47400.11548.149961182.27.987.999.450.1173.042.530.1161.152.3100.128716015026.81676.351.240.17811.6
D-11.5201.040.45900.12637.145659942.65.1711.58.630.1362.061.770.15901.1300.137593039.759.452.66.949.940.19411.6
D-21.5701.030.31700.061837.845058143.35.3513.710.90.1132.961.810.17701.7100.180578017523.054.16.9510.90.20511.7
D-31.4600.5630.51400.11238.646360679.44.159.159.100.1162.101.730.1250.2161.5900.116685016961.369.66.9412.60.22411.0
J-15.460.28321.30.27000.093050.040944821.87.3434.510.10.06111.261.740.1505.970.52900.13753709.1724.51202.780.7340.047910.4
J-26.450.23319.50.20100.085852.441343638.46.7434.810.10.05081.122.910.13600.63000.15651403.4914.31242.840.6950.04869.27
J-37.100.24516.60.25400.11154.145446736.69.4741.910.60.05971.152.220.98400.52000.13152603.1034.11282.640.6240.050810.5
H-12.640.02195.660.47000.10647.944851436.27.6855.210.40.1261.722.3408.650.96900.10354905.7324.71244.841.050.035713.6
H-22.6306.630.40600.16647.145048136.36.9559.410.40.1061.602.050.10500.91100.0802516015.553.01144.951.560.037512.6
H-33.100.03114.050.41400.064645.842849556.56.0361.99.830.1061.322.440.20900.94900.10247204.2616.01054.391.190.034312.8
N-15.97013.00.36300.11041.049952519.95.2265.87.120.05151.432.990.08900.97200.18261604.6925.81273.900.8330.024711.2
N-25.190.037710.90.37600.090045.151953723.15.7261.47.250.05151.512.930.1041.200.94800.19158203.8721.41254.450.7410.029210.4
N-34.65011.90.34000.090241.551052921.75.6259.57.030.07911.602.520.1000.3020.80800.18959103.0026.61194.500.6370.020210.8

The data unit is mg(kg), among which the element data unit of Li, Ti, V, Cr, Co, Ge, Se, Cd, Sb, Cs, and Pb is μg(kg). A- Anhui Province, X- Guangxi Province, D- Guangdong Province, J- Jilin Province, H- Heilongjiang Province, N- Inner Mongolia.

We conducted further statistical analysis on the abovementioned experimental data, by calculating the standard deviation of each element and judging the difference of each element in different regions according to the degree of dispersion of the value of each element. As shown in Figure 2, the standard deviations of S, P, K, Cd, Mg, and other elements were large, and the degree of dispersion was relatively higher than that of other elements, which can be initially used as indicative elements for traceability.

3.4. Multielement Normal Distribution Test

The Kolmogorov–Smirnov test was conducted on the content of 30 elements in rice from different origins. The asymptotic significance (bilateral) value was calculated. The content data of 24 elements obeyed normal distribution.

3.5. Principal Component Analysis

Principal component analysis (PCA) is a multivariate statistical analysis method that analyses a few variables which can reveal the internal structure sufficiently by studying the relationship between multiple original variables.

According to the rule that the characteristic value is greater than 1 and the cumulative variance contribution rate is greater than 80%, six principal component factors were obtained through rotation and extraction factors, and the total contribution rate was 87.878%, indicating that the experimental data can fully reflect the original information (Table 8).


ComponentInitial eigenvalueRotate the sum of squares loading
TotalVariance (%)Accumulate (%)TotalVariance (%)Accumulate (%)

110.4138.5638.567.54727.9527.95
24.80017.7856.344.87018.0445.99
33.14211.6467.973.22511.9457.94
42.2438.30676.283.22311.9469.87
51.8506.85283.132.80910.4180.28
61.2824.74987.882.0527.59987.88

The first principal component is mainly composed of S, Ti, Ni, Cu, Co, Ge, Mo, Cd, Cs, Ba, Zn, and Se elements. The second principal component is mainly composed of Li, B, Mg, K, Ca, P, V, Pb, Fe, and As elements. The third principal component is mainly composed of Mn and Sb elements (Table 9). The first principal component, the second principal component, and the third principal component were used to analyze the contribution of the principal components of samples from different origins (Figure 3). The contribution scores of the principal components of samples from the same origin were concentrated, while the distribution of different origins is scattered. On the whole, rice samples from the north and south of China have a large difference in the contribution scores of the principal components which can be clearly distinguished. This result has certain guiding significance for the distinction of rice from different production places.


ElementComponent
123456

Li−0.6970.551−0.1640.1670.154−0.115
B−0.3670.7530.1620.3240.003−0.068
Na−0.927−0.009−0.0380.184−0.016−0.114
Mg−0.3180.846−0.124−0.167−0.1850.045
K0.3240.644−0.2630.2910.2890.282
Ca−0.6840.5150.3600.011−0.278−0.083
P0.2360.617−0.559−0.0790.2530.182
S0.8550.381−0.2770.1020.1080.094
Ti0.7720.0700.1410.016−0.375−0.333
V−0.5120.6600.4050.087−0.0920.132
Cr−0.693−0.380−0.096−0.5080.1400.175
Mn−0.091−0.0020.7940.261−0.2810.353
Ni0.8470.2480.297−0.177−0.0100.113
Cu0.8090.346−0.1120.180−0.2130.212
Fe−0.4760.361−0.587−0.277−0.133−0.014
Co0.9840.033−0.093−0.020−0.0070.000
Zn0.4720.0920.364−0.633−0.0520.401
Ge0.8180.301−0.192−0.039−0.363−0.216
Se0.432−0.0030.3680.0810.778−0.154
Sr−0.221−0.212−0.8220.384−0.0080.107
As0.2440.3830.273−0.3160.480−0.448
Mo0.8220.277−0.040−0.3950.056−0.117
Cd0.715−0.1720.0340.380−0.2090.165
Sb−0.3680.3080.3290.4380.062−0.237
Cs0.547−0.3320.1520.5210.3990.247
Ba0.8600.097−0.0440.198−0.203−0.350
Pb−0.0870.7060.132−0.1140.1480.236

3.6. Cluster Analysis

The contents of multielements in rice from different areas were analyzed by cluster analysis. The samples were successfully divided into two categories (the north and south of China) and six subcategories (six rice-producing areas) by the method of intergroup connection (Figure 4). The results show that there were obvious differences in the contents of multielements in rice from different producing areas, and they had certain regional characteristics. Therefore, by measuring the multielement content of rice, it is possible to accurately classify the samples according to the place of origin and finally realize the traceability of the production place of the rice.

4. Conclusions

In this experiment, the ICP-MS/MS method was developed to determine the content of 30 elements in rice from different production areas. The determination mode and reaction gas conditions were optimized, and the optimal determination conditions were selected for each element in five determination modes of no gas, H2, O2, He, and NH3/He. In addition, in situ mass spectrometry and mass transfer technology were used to eliminate the interference and reduce the detection limit. To achieve the determination of ultratrace elements, we established a complete detection method, which provided a method basis for rice origin traceability. Through the principal component analysis of the multielement content of 18 batches of samples from different origins, the distribution of the six principal components of the samples and the characteristic elements of each principal component were determined. Through cluster analysis, the samples were accurately classified according to the place of production based on the multielement content, which proved that there was a significant correlation between the content of multielement in rice and the place of production, providing technical support and research direction for the traceability of the origin of rice.

Data Availability

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

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this article.

Authors’ Contributions

YZ, QL, and YW conceived and designed the study. YW and LNL performed the experiments. YW and XXY wrote the paper. YZ, QL, JMM, and SFF reviewed and edited the manuscript. All authors read and approved the manuscript.

Acknowledgments

This work was supported by the National Key Research and Development Program of China (Project no. 2018YFC1603400), State Administration for Market Regulation Special Technical Support Program (Project no. 2019YJ009), and Scientific Research Projects of Hebei Market Supervision and Administration Bureau (Project no. 2021ZC07).

References

  1. V. Taleon, S. Gallego, J. C. Orozco, and C. Grenier, “Retention of Zn, Fe and phytic acid in parboiled bio fortified and non-biofortified rice,” Food Chemistry X, vol. 8, Article ID 100105, 2020. View at: Publisher Site | Google Scholar
  2. M. Jaafar, A. Shrivastava, S. Rai Bose, M. Felipe-Sotelo, and N. I. Ward, “Transfer of arsenic, manganese and iron from water to soil and rice plants: an evaluation of changes in dietary intake caused by washing and cooking rice with groundwater from the Bengal Delta, India,” Journal of Food Composition and Analysis, vol. 96, Article ID 103748, 2021. View at: Publisher Site | Google Scholar
  3. M. Tatahmentan, S. Nyachoti, L. Scott et al., “Toxic and essential elements in rice and other grains from the United States and other countries,” International Journal of Environmental Research and Public Health, vol. 17, no. 21, p. 8128, 2020. View at: Publisher Site | Google Scholar
  4. Z. Y. Min, L. S. Si, L. B. Yu et al., “Enrichment of cadmium in rice (Oryza sativa L.) grown under different exogenous pollution sources,” Environmental Science and Pollution Research International, vol. 27, pp. 44249–44256, 2020. View at: Publisher Site | Google Scholar
  5. F. Zhang, F. Gu, H. Yan et al., “Effects of soaking process on arsenic and other mineral elements in brown rice,” Food Science and Human Wellness, vol. 9, no. 2, pp. 168–175, 2020. View at: Publisher Site | Google Scholar
  6. L. Wang and Y. Jin, “Possible application of stable isotope compositions for the identification of metal sources in soil,” Journal of Hazardous Materials, vol. 407, p. 5, 2021. View at: Publisher Site | Google Scholar
  7. W. Jie, Z. Tengteng, and G. Yongbin, “C/N/H/O stable isotope analysis for determining the geographical origin of American ginseng (Panax quinquefolius),” Journal of Food Composition and Analysis, vol. 96, Article ID 103756, 2021. View at: Publisher Site | Google Scholar
  8. S. Yaeko, M. Shotaro, T. Tomoki et al., “Preliminary study for tracing the geographical origin of wheat flour in breads using stable isotope analysis of wheat proteins,” Food Analytical Methods, vol. 14, p. 1, 2020. View at: Publisher Site | Google Scholar
  9. J. Wang, T. Chen, W. Zhang, Y. Zhao, S. Yang, and A. Chen, “Tracing the geographical origin of rice by stable isotopic analyses combined with chemometrics,” Food Chemistry, vol. 313, Article ID 126093, 2020. View at: Publisher Site | Google Scholar
  10. P. Vera, G. Raquel, C. B. Dias, and M. Cabrita, “Combination of stable isotope analysis and chemo metrics to discriminate geoclimatically and temporally the virgin olive oils from three mediterranean countries,” Foods, vol. 9, p. 12, 2020. View at: Publisher Site | Google Scholar
  11. Y.-Y. SU, J. Gao, Y. F. Zhao et al., “Geographical origin classification of Chinese wines based on carbon and oxygen stable isotopes and elemental profiles,” Journal of Food Protection, vol. 83, p. 8, 2020. View at: Publisher Site | Google Scholar
  12. N. W. Ling, B. L. Jie, G. Gary et al., “Multivariate statistical analysis of stable isotope signatures and element concentrations to differentiate the geographical origin of retail milk sold in Singapore,” Food Control, vol. 123, Article ID 107736, 2020. View at: Publisher Site | Google Scholar
  13. L. Qian, F. Zuo, H. Liu, C. Zhang, X. Chi, and D. Zhang, “Determination of geographical origin of wuchang rice with the geographical indicator by multielement analysis,” Journal of Food Quality, vol. 2, pp. 1–7, 2019. View at: Publisher Site | Google Scholar
  14. J. Zhang, Z. Tian, Y. Ma et al., “Origin identification of the sauce-flavor Chinese baijiu by organic acids, trace elements, and the stable carbon isotope ratio,” Journal of Food Quality, vol. 2019, Article ID 7525201, 7 pages, 2019. View at: Publisher Site | Google Scholar
  15. 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 & Technology, vol. 53, p. 9, 2018. View at: Publisher Site | Google Scholar
  16. 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, p. 4, 2013. View at: Publisher Site | Google Scholar
  17. P. Wang, X. R. Ma, W. P. WANG et al., “Characterization of flavor fingerprinting of red sufu during fermentation and the comparison of volatiles of typical products,” Food Science and Human Wellness, vol. 8, pp. 375–384, 2019. View at: Publisher Site | Google Scholar
  18. A. Maike, R. Marc, D. Alissa et al., “Food authentication: determination of the geographical origin of almonds (Prunus dulcis Mill.) via near-infrared spectroscopy,” Micro Chemical Journal, vol. 160, 2021. View at: Publisher Site | Google Scholar
  19. C. Hui, T. Chao, and L. Hongjin, “Discrimination between wild-grown and cultivated Gastrodia elata by near-infrared spectroscopy and chemometrics,” Vibrational Spectroscopy, vol. 113, Article ID 103203, 2021. View at: Publisher Site | Google Scholar
  20. G. L. Truong, T. P. Quoc, and Y. D. Hai, “Identification of rice varieties specialties in Vietnam using Raman spectroscopy,” Vietnam Journal of Chemistry, vol. 58, p. 6, 2020. View at: Publisher Site | Google Scholar
  21. A. Maike, D. Alissa, A. Christian, and M. Fischer, “Determination of the geographical origin of walnuts (juglans regia L.) using near-infrared spectroscopy and chemometrics,” Foods, vol. 9, p. 12, 2020. View at: Publisher Site | Google Scholar
  22. A. A. Wasim, S. Naz, M. N. Khan, and S. Fazalurrehman, “Assessment of heavy metals in rice using atomic absorption spectrophotometry–a study of different rice varieties in Pakistan,” Pakistan Journal of Analytical & Environmental Chemistry, vol. 20, p. 1, 2019. View at: Publisher Site | Google Scholar
  23. X. Deng, R. Li, and S. Deng, “Determination of the total content of arsenic, antimony, selenium and mercury in Chinese herbal food by chemical vapor generation-four-channel non-dispersive atomic fluorescence spectrometry,” Journal of Fluorescence, vol. 30, pp. 949–954, 2020. View at: Publisher Site | Google Scholar
  24. L. HongLin, Z. YiTao, Z. Xin, and H. Tong, “Improved geographical origin discrimination for tea using ICP-MS and ICP-OES techniques in combination with chemo metric approach,” Journal of the Science of Food and Agriculture, vol. 100, p. 8, 2020. View at: Publisher Site | Google Scholar
  25. S. O. Yeon, I. M. Atikul, S. J. Hyeon et al., “Elemental composition of pork meat from conventional and animal welfare farms by inductively coupled plasma-optical emission spectrometry (ICP-OES) and ICP-mass spectrometry (ICP-MS) and their authentication via multivariate chemo metric analysis,” Meat Science, vol. 172, Article ID 108344, 2021. View at: Publisher Site | Google Scholar
  26. B. M. Freire, V. D. S. Santos, P. D. C. F. Neves et al., “Elemental chemical composition and as speciation in rice varieties selected for bio fortification,” Analytical Methods, vol. 12, p. 16, 2020. View at: Publisher Site | Google Scholar
  27. L. Emily, F. Juraj, G. Sarah, J. Hutson, and L. Mosley, “A simple and rapid ICP-MS/MS determination of sulfur isotope ratios (34S/32S) in complex natural waters: a new tool for tracing seawater intrusion in coastal systems,” Talanta, vol. 235, Article ID 122708, 2021. View at: Publisher Site | Google Scholar
  28. S. Alexander, G. Sarah, T. Renee et al., “In-situ Lusingle bondHf geochronology of garnet, apatite and xenotime by LA ICP MS/MS,” Chemical Geology, vol. 577, Article ID 120299, 2021. View at: Publisher Site | Google Scholar
  29. S. Yoshinari, M. Kirara, and Y. Yukiya, “Assignment of PM2.5 sources in western Japan by non-negative matrix factorization of concentration-weighted trajectories of GED-ICP-MS/MS element concentrations,” Environmental Pollution, vol. 207, Article ID 116054, 2021. View at: Publisher Site | Google Scholar
  30. L. Xiaoming, D. Shuofei, Y. Yahu et al., “87Sr/86Sr isotope ratios in rocks determined using inductively coupled plasma tandem mass spectrometry in O2 mode without prior Sr purification,” Rapid Communications in Mass Spectrometry, vol. 34, Article ID e8690, 2020. View at: Publisher Site | Google Scholar
  31. A. Akif, E. A. Pelin, and O. G. Eftade, “Chemical characterization of size-segregated particulate matter (PM) by inductively coupled plasma – tandem mass spectrometry (ICP-MS/MS)”,” Talanta, vol. 208, Article ID 120350, 2020. View at: Publisher Site | Google Scholar
  32. J. Hirata, D. Itabashi, and M. Aimoto, “Determination of ultra-trace tellurium in steel by ID-ICP-MS/MS with liquid-liquid extraction,” Analytical Sciences, 2021. View at: Publisher Site | Google Scholar
  33. T. M. A. M. Tamer, I. H. E. Dalia, and S. S. Abdelsalam, “Determination of some common heavy metals and radionuclides in some medicinal herbs using ICP-MS/MS,” Journal of AOAC International, vol. 102, p. 5, 2020. View at: Publisher Site | Google Scholar
  34. L. Fu and S. Shi, “A novel strategy to determine the compositions of inorganic elements in fruit wines using ICP-MS/MS,” Food Chemistry, vol. 299, Article ID 125172, 2019. View at: Publisher Site | Google Scholar
  35. O. Pérez-Arvizu and J.-P. Bernal, “Measurement of sulfur in environmental samples using the Interference Standard Method with a O2-pressurized reaction cell and a single quadrupole inductively coupled plasma mass spectrometer,” Rapid Communications in Mass Spectrometry : Rapid Communications in Mass Spectrometry, vol. 35, p. 9034, 2020. View at: Publisher Site | Google Scholar
  36. F. Liang, X. Hualin, H. Jianhua, and L. Chen, “Determination of the non-metallic elements in herbal tea by inductively coupled plasma tandem mass spectrometry,” Biological Trace Element Research, vol. 199, pp. 769–778, 2021. View at: Publisher Site | Google Scholar

Copyright © 2021 Yan Wang 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.


More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views165
Downloads122
Citations

Related articles

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.