Journal of Analytical Methods in Chemistry
Volume 2021 (2021), Article ID 8860852, 11 pages
https://doi.org/10.1155/2021/8860852

Research Article

Multielement Analysis of Pakchoi (Brassica rapa L. ssp. chinensis) by ICP-MS and Their Classification according to Different Small Geographical Origins

Trung Nguyen-Quang,¹ Giang Do-Hoang,¹ and Minh Truong-Ngoc1,2

¹Vietnam Academy of Science and Technology (VAST), Center for Research and Technology Transfer (CRETECH), 18 Hoang Quoc Viet Road, 100000 Hanoi, Vietnam
²Vietnam Academy of Science and Technology (VAST), Institute of Applied Mechanics and Informatics, 18 Hoang Quoc Viet Road, 100000 Hanoi, Vietnam

Correspondence should be addressed to Trung Nguyen-Quang; nqt79@yahoo.com

Received 6 July 2020; Revised 17 January 2021; Accepted 28 January 2021; Published 9 February 2021

Academic Editor: Sofia Gama

Copyright © 2021 Trung Nguyen-Quang 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.

Statistical interpretation of the concentrations of 42 elements, determined using solution-based inductively coupled plasma mass spectrometry (ICP-MS) analysis and multivariate statistical methods, such as principal components analysis (PCA), was used to establish the provenance of pakchoi (Brassica rapa L. ssp. chinensis) from 6 areas in Ha Noi, Vietnam. Although pakchoi is widely cultivated and manufactured, no universal method is used to discriminate the origin of this vegetable. Our study introduced for the first time a method to classify pakchoi in small geographical areas. 42 metallic elements of pakchoi were detected by ICP-MS, which were further analyzed using multivariate statistical analysis to perform clusters based on the geographical locations. Eleven elements, i.e., ²⁸Si; ⁵⁶Fe; ⁵⁹Co; ⁶³Cu; ⁶⁹Ga; ⁷⁵As; ⁸⁵Rb; ⁹³Nb; ¹⁰⁷Ag; ¹¹⁸Sn, and ¹³⁷Ba, were identified as discriminators to distinguish pakchoi from those areas. Results from this study presented a new method to discriminant the geographical origins of pakchoi, which could apply to other types of vegetables on the food market.

1. Introduction

Discriminating the geographical origins of various food and drink products is growing considerably in recent years. In general, inductively coupled plasma mass spectrometry (ICP-MS) is one of the most useful approaches for multielement analysis of foods and drinks. It has been shown that ICP-MS data could be used successfully for discriminating wines from different geographical areas in Slovenia, Romania, and Croatia [1–3]. Chuzinska and Baralkiewicz determined 13 mineral elements for the classification of 55 honey samples in Poland [4]. Whereas, Chung et al. distinguished verified cultivation regions of potato by the stable isotope composition analysis of bioelements [5]. The geographical origin of vegetables, such as cabbages, was also discriminated by 21 inorganic elements through ICP-MS analysis [6]. The data of elements compositions are often combined with multivariate statistical analysis, such as principal component analysis (PCA), to reduce the dimension of the data matrix, visualize the similarities and differences among samples, and identify the key discriminants distinguishing the samples [7].

Pakchoi (Brassica rapa L.) is a major vegetable in Vietnamese diets. The consumption of pakchoi may provide many health benefits. Previous studies have shown that pakchoi potentially exerts inhibitory activity against cancer [8]. Phytochemical investigations have shown that flavonoids, hydroxycinnamic acids, and glucosinolate are the major bioactive ingredients of pakchoi, which possess high antioxidant activity [9, 10].

The chemical compositions of pakchoi are varied with cultivating conditions, such as soils, weather, and water resources [11, 12]. The growth and quality of pakchoi are significantly affected by inorganic elements, for example, selenate at low concentrations (0.5–1.0 mg/kg) could promote the growth of pakchoi and reduce cadmium contents in this vegetable [13]. The use of organic fertilizers and soil improvers, such as leonardite, might enrich the contents of macronutrients (Mg, Ca, K, and S) and micronutrients (Fe, Cu, Mn, and Zn).

The classification of different pakchoi cultivars has been conducted by different methods. Wiesner reported that pakchoi origin can be classified through their glucosinolate profile using HPLC-DA-MS analysis. In the glucosinolate profile, levels of 3-butenyl glucosinolate and 2-hydroxy-3-butenyl glucosinolate were considered important discriminators. By contrast, 5-methylsulfinylpentyl glucosinolate was found to be a characteristic ontogenetic variation of mature leaves of pakchoi by using PCA as the statistical model [14]. Another research also assessed the morphological and genetic diversity of pakchoi based on PCA and clustering analysis [15]. On the other hand, no previous approach categorized the geographical origins based on the characterization of elemental compositions of pakchoi. Thus, this study presents a promising method to discriminate against the geographical origins of pakchoi by using ICP-MS elemental analysis coupled with statistical principal component analysis. The proposed method may be utilized for discriminating against the origins of other vegetables.

2. Materials and Methods

2.1. Samples

Totally 60 pakchoi (Brassica rapa L. ssp. chinensis) samples were obtained from 6 different locations in Hanoi, Vietnam (Figure 1), which are Linh Nam (Hoang Mai district) 20°58′33.7″N 105°53′21.9″E, Tien Phong (Me Linh district) 21°09′27.2″N 105°44′19.4″E, Thanh Da (Phuc Tho district) 21°06′32.6″N 105°36′43.0″E, Thanh Xuan (Soc Son district) 21°14′04.6″N 105°45′59.3″E, Van Duc (Gia Lam district) 20°56′23.0″N 105°53′17.7″E, and Van Noi (Dong Anh district) 21°08′49.0″N 105°48′50.2″E. The samples were preliminarily sampled and prepared followed by Vietnam Standard TCVN 9016 : 2011 and TCVN 8551 : 2010 and then washed several times with distilled water.

Figure 1: The geographical locations of the pakchoi cultivation regions.

2.2. Chemicals and Reagents

Nitric acid 65% (HNO₃) and hydrogen peroxide 30% (H₂O₂) solutions were purchased from Merck, USA. Ultrapure deionized water with the resistivity of 18.2 MΩ cm was obtained from a Milli-Q Plus water puriﬁcation system (Millipore, Bedford, MA, USA). Twenty-six multielement standard solutions including ¹¹B, ²³Na, ²⁴Mg, ²⁷Al, ²⁸Si, ³⁹K, ⁴³Ca, ⁵¹V, ⁵²Cr, ⁵⁵Mn, ⁵⁶Fe, ⁵⁹Co, ⁶⁰Ni, ⁶³Cu, ⁶⁶Zn, ⁶⁹Ga, ⁷⁵As, ⁸⁵Rb, ⁸⁸Sr, ¹⁰⁷Ag, ¹¹¹Cd, ¹²¹Sb, ¹³³Cs, ¹³⁷Ba, ²⁰²Hg, and ²⁰⁸Pb (TraceCERT, periodic table mix 1 for ICP, product no. 92091, lot: BCBW5563) and nine rare earth elements (⁴⁵Sc, ⁸⁹Y, ¹³⁹La, ¹⁴⁰Ce, ¹⁴¹Pr, ¹⁴⁶Nd, ¹⁴⁷Sm, ¹⁵³Eu, and ¹⁵⁷Gd) 10 mg/L each element were provided by Sigma-Aldrich Company. A standard solution containing 50 µg/L of ⁴⁷Ti, ⁹⁰Zr, ⁹³Nb, ⁹⁵Mo, ¹⁰⁵Pd, ¹¹⁸Sn, and ¹⁸²W in 6% ethanol/0.14 M HNO₃ was used to determine the sensitivity factors for all elements across the entire mass range for the measurement of diluted samples made in the semiquantitative mode. In case of measuring digested samples, ethanol was omitted in the calibration solution. High-purity ethanol was used for preparing matrix-matched standards. Internal standards for quantitative analysis were prepared in 6% ethanol/0.14 M HNO₃ for diluted samples and in 0.14 M HNO₃ for digested samples. Blanks for the measurement of diluted and digested samples were a 6% ethanol/0.14 M HNO₃ solution containing 50 µg/L of the internal standard and a 0.14 M HNO₃ procedure blank submitted to the microwave treatment and including the internal standard, respectively. Standards and the internal standard were prepared by appropriate dilution from 1000 mg/L standard stock solutions.

2.3. Sample Preparation and ICP-MS Measurements

All pakchoi samples were lyophilized at −45°C for 3 days and then ground with a pulverizer to obtain find powder (<400 μm particle size) before storing in clean plastic bags. Samples (0.25 g) were weighed into a polytetrafluoroethylene (PTFE) vessel, and then, 4 mL H₂O, 2 mL HNO₃, and 2 mL H₂O₂ were added. After 30 min, the sample was digested in a Mars X-press plus microwave digestion system (CEM, NC, USA). The digestion program was as follows: the sample was heated to 12°C for 15 min, held for 10 min, before heating to 160°C for 10 min, held for 10 min, and finally heated to 180°C within 10 min and held for 30 min. After cooling, the solution was diluted to 25 mL in a volumetric flask with ultrapure water. The contents were then diluted with deionized water and analyzed [16]. An Agilent 7900 ICP-MS instrument (Agilent Technologies, Tokyo, Japan) was utilized for the measurement of 42 elements in the pakchoi samples, which were ¹¹B, ²³Na, ²⁴Mg, ²⁷Al, ²⁸Si, ³⁹K, ⁴³Ca, ⁴⁵Sc, ⁴⁷Ti, ⁵¹V, ⁵²Cr, ⁵⁵Mn, ⁵⁶Fe, ⁵⁹Co, ⁶⁰Ni, ⁶³Cu, ⁶⁶Zn, ⁶⁹Ga, ⁷⁵As, ⁸⁵Rb, ⁸⁸Sr, ⁸⁹Y, ⁹⁰Zr, ⁹³Nb, ⁹⁵Mo, ¹⁰⁵Pd, ¹⁰⁷Ag, ¹¹¹Cd, ¹¹⁸Sn, ¹²¹Sb, ¹³³Cs, ¹³⁷Ba, ¹³⁹La, ¹⁴⁰Ce, ¹⁴¹Pr, ¹⁴⁶Nd, ¹⁴⁷Sm, ¹⁵³Eu, ¹⁵⁷Gd, ¹⁸²W, ²⁰²Hg, and ²⁰⁸Pb. The analytical parameters of the ICP-MS were RF power at 1550 W, RF matching at 2.0 V, cell entrance at −40 V, cell exit of −60 V, cell energy discrimination at 5.0 V, spray chamber temperature at 2°C, argon was used as carrier gas at flowrate 1.09 L/min, and helium was used as auxiliary gas at 4.3 L/min. Data quantitation was achieved regarding matrix-matched multielement standards that had been prepared in 1% HNO₃.

In this study, instrument detection limits were calculated using the raw intensity data from the standard and the blank (using ultrapure 2% nitric acid matrix) as per the following equation: IDL = 3SDblankx Cx/(Sx–Sblank), where SDblank is the standard deviation of the intensities of the multiple blank measurements, Cx is the mean signal for the standard, Sx is the signal for Cx, and Sblank is the signal for blank. Method detection limits (MDLs) were calculated as follows: MDL = IDL x constant volume/sample weight.

Continuing calibration verification standards were prepared from single element ICP standards (Merck) consisting of 20 mg/L Ca and Mg for the high standard series, and 250 µg/L Al, B, Cu, Rb, Sr, and Zn and 25 µg/L Cd, Co, Cs, Ni, Tl, and V for the low standard series. The calibration verifications were measured after every 10 samples.

Duplicates of two pakchoi samples were prepared. Possible matrix effects were checked by running an interference check sample consisting of Ca (50 mg/L), Na (100 mg/L), Mg (150 mg/L), Fe (200 mg/L), Cu (250 mg/L), and Zn (500mg/L). In addition, spike recovery tests and serial dilutions were performed on pakchoi samples. The spiked samples were prepared at concentration levels of 20 and 100 µg/L for the elements Al, Cu, and Sr and of 100 and 500 µg/L for the elements B, Mn, Rb, and Zn. A serial dilution check (1 : 10 followed by 1 : 3, thus 1 : 30 final dilution) was performed on one pakchoi.

2.4. Statistical Analysis

The statistical analysis of the data was performed using STATISTICA 12 (Dell Software, United States). The principal component analysis (PCA) was applied to the data acquired from the ICP-MS analysis to evaluate the discriminants among 6 groups of pakchoi samples from 6 geographical locations. The outputs of multivariate statistical analysis included the scree plot to show contributions of principal components to the PCA model, the score scatterplot illustrates the separation of 6 different groups, the loading scatterplot explains the influences of the elements to the clustering, and the moving range charts illustrate the means and ranges of common distributions of variables on the cases.

3. Results and Discussion

3.1. Elemental Analysis of Pakchoi by ICP-MS

Table 1 shows the contents of 42 elements in pakchoi obtained from 6 areas in Hanoi, including Tien Phong, Thanh Da, Linh Nam, Thanh Xuan, Van Duc, and Van Noi. K was the most abundant element in pakchoi samples which accounted for 52–62% of the total elements. Furthermore, Ca was the second major element present in pakchoi which accounted for 30–40% of the total elements. The highest total element contents (about 21 mg/g of fresh material) were found in the samples from Van Duc and Thanh Xuan, whereas the samples from Linh Nam and Van Noi showed the lowest contents of elements at about 16 mg/g of fresh material. The concentration of each element in the samples varied significantly due to the differences in geographical locations.

Table 1: Contents (µg/g, fresh weight base) of the 42 elements in pakchoi from 6 locations in Hanoi.

Element
LOD
LOQ
Cultivation region

Tien Phong
Thanh Da
Linh Nam
Thanh Xuan
Van Duc
Van Noi

(n = 10)
(n = 10)
(n = 10)
(n = 10)
(n = 10)
(n = 10)

¹¹B
0.031 ± 0.002
0.103 ± 0.006
<MQL
2.225 ± 0.001
2.296 ± 0.149
1.679 ± 0.254
1.103 ± 0.028
1.367 ± 0.129

²³Na
2.220 ± 0.054
7.399 ± 0.179
740.7 ± 3.2
194.33 ± 3.43
343 ± 27
1056 ± 7
352.6 ± 3.6
401 ± 8

²⁴Mg
9.090 ± 0.081
30.29 ± 0.269
528.8 ± 5.1
543.4 ± 5.9
536.5 ± 49.1
543.1 ± 42
629.6 ± 10.5
518.6 ± 6.7

²⁷Al
1.100 ± 0.007
3.666 ± 0.023
58.85 ± 5.6
242.1 ± 7.7
130.5 ± 9.6
102.8 ± 1.2
305.8 ± 20.2
72.63 ± 4.16

²⁸Si
28.07 ± 0.103
93.55 ± 0.342
45.77 ± 3.2
37.53 ± 1.83
212.4 ± 10.8
302.5 ± 33.1
126.7 ± 1.8
163.9 ± 9.9

³⁹K
12.01 ± 0.051
40.02 ± 0.169
11369 ± 44
10464 ± 15
8683 ± 85
11915 ± 20
12113 ± 133
9220 ± 36

⁴³Ca
4.003 ± 0.011
13.34 ± 0.036
5422 ± 41
6425 ± 62
6626 ± 61
7517 ± 56
8268 ± 52
5983 ± 24

⁴⁵Sc
0.667 ± 0.004
2.223 ± 0.013
0.135 ± 0.011
0.272 ± 0.026
0.215 ± 0.121
0.213 ± 0.076
0.241 ± 0.012
0.207 ± 0.011

⁴⁷Ti
0.429 ± 0.003
1.429 ± 0.009
1.383 ± 0.012
2.726 ± 0.121
1.246 ± 0.122
2.062 ± 0.169
1.608 ± 0.073
1.255 ± 0.191

⁵¹V
0.056 ± 0.006
0.186 ± 0.019
0.028 ± 0.002
0.065 ± 0.06
0.042 ± 0.003
0.034 ± 0.003
0.054 ± 0.002
0.031 ± 0.002

⁵²Cr
0.072 ± 0.001
0.239 ± 0.003
0.123 ± 0.003
0.121 ± 0.007
0.111 ± 0.004
0.192 ± 0.001
0.157 ± 0.006
0.214 ± 0.006

⁵⁵Mn
0.114 ± 0.002
0.379 ± 0.006
20.34 ± 1.07
9.976 ± 0.311
10.98 ± 0.11
22.9 ± 0.13
20.44 ± 0.18
14.11 ± 0.37

⁵⁶Fe
2.030 ± 0.007
6.765 ± 0.023
20.39 ± 1.05
50.89 ± 0.18
31.59 ± 2.35
26.17 ± 2.21
21.12 ± 1.94
26.97 ± 0.43

⁵⁹Co
0.005 ± 0.001
0.016 ± 0.003
0.018 ± 0.001
0.027 ± 0.002
0.041 ± 0.044
0.023 ± 0.002
0.026 ± 0.002
0.017 ± 0.001

⁶⁰Ni
0.023 ± 0.003
0.076 ± 0.009
0.117 ± 0.001
0.065 ± 0.003
0.061 ± 0.005
0.279 ± 0.002
0.274 ± 0.092
0.364 ± 0.015

⁶³Cu
0.027 ± 0.002
0.089 ± 0.006
0.35 ± 0.02
0.219 ± 0.011
0.441 ± 0.003
0.979 ± 0.019
0.702 ± 0.074
0.786 ± 0.036

⁶⁶Zn
0.703 ± 0.026
2.343 ± 0.086
7.148 ± 0.09
9.204 ± 0.811
8.674 ± 0.572
15.73 ± 1.21
16.74 ± 0.302
12.73 ± 0.156

⁶⁹Ga
0.070 ± 0.001
0.233 ± 0.003
0.399 ± 0.017
0.561 ± 0.005
0.636 ± 0.003
1.079 ± 0.025
1.835 ± 0.037
0.525 ± 0.035

⁷⁵As
0.005 ± 0.001
0.016 ± 0.003
<MQL
0.022 ± 0.002
0.041 ± 0.002
0.073 ± 0.19
0.044 ± 0.004
0.032 ± 0.003

⁸⁵Rb
0.004 ± 0.001
0.013 ± 0.003
3.111 ± 0.044
2.474 ± 0.218
4.783 ± 0.321
8.548 ± 0.581
6.638 ± 0.058
4.587 ± 0.139

⁸⁸Sr
0.091 ± 0.016
0.303 ± 0.053
17.77 ± 0.32
15.57 ± 0.57
13.6 ± 1.2
18.5 ± 0.8
17.18 ± 0.01
15.81 ± 0.02

⁸⁹Y
0.018 ± 0.001
0.059 ± 0.006
0.028 ± 0.004
0.046 ± 0.004
0.032 ± 0.001
0.044 ± 0.003
0.047 ± 0.002
0.0302 ± 0.0022

⁹⁰Zr
0.0021 ± 0.0001
0.006 ± 0.0003
0.006 ± 0.001
0.004 ± 0.001
0.017 ± 0.001
0.0048 ± 0.0003
0.021 ± 0.001
0.0062 ± 0.0004

⁹³Nb
0.0014 ± 0.0001
0.004 ± 0.0003
0.004 ± 0.001
0.004 ± 0.002
0.003 ± 0.003
0.0058 ± 0.004
0.016 ± 0.001
0.045 ± 0.002

⁹⁵Mo
0.032 ± 0.002
0.106 ± 0.006
0.075 ± 0.003
0.032 ± 0.003
0.026 ± 0.002
0.155 ± 0.002
0.070 ± 0.031
0.093 ± 0.003

¹⁰⁵Pd
0.0091 ± 0.0002
0.030 ± 0.0006
0.034 ± 0.002
0.056 ± 0.002
0.038 ± 0.003
0.039 ± 0.007
0.045 ± 0.003
0.035 ± 0.002

¹⁰⁷Ag
0.0041 ± 0.0001
0.013 ± 0.0003
0.106 ± 0.007
0.005 ± 0.001
0.005 ± 0.001
0.042 ± 0.003
2.687 ± 0.449
0.018 ± 0.001

¹¹¹Cd
0.0032 ± 0.0001
0.010 ± 0.0003
0.011 ± 0.001
0.023 ± 0.002
0.036 ± 0.003
0.027 ± 0.002
0.040 ± 0.003
0.033 ± 0.003

¹¹⁸Sn
0.061 ± 0.002
0.203 ± 0.006
0.093 ± 0.004
0.015 ± 0.001
0.255 ± 0.013
0.121 ± 0.001
0.155 ± 0.009
0.123 ± 0.007

¹²¹Sb
0.0035 ± 0.0001
0.011 ± 0.0003
0.042 ± 0.003
0.059 ± 0.004
0.041 ± 0.031
0.122 ± 0.005
0.131 ± 0.027
0.075 ± 0.005

¹³³Cs
0.0027 ± 0.0002
0.008 ± 0.0006
0.012 ± 0.001
0.039 ± 0.003
0.041 ± 0.002
0.035 ± 0.003
0.044 ± 0.001
0.020 ± 0.001

¹³⁷Ba
0.523 ± 0.048
1.743 ± 0.159
1.318 ± 0.094
1.31 ± 0.09
2.616 ± 0.111
2.763 ± 0.547
5.953 ± 0.479
1.64 ± 0.11

¹³⁹La
0.009 ± 0.001
0.029 ± 0.003
0.016 ± 0.002
0.042 ± 0.002
0.044 ± 0.002
0.036 ± 0.002
0.023 ± 0.002
0.020 ± 0.002

¹⁴⁰Ce
0.0052 ± 0.0001
0.017 ± 0.0003
0.018 ± 0.001
0.055 ± 0.002
0.031 ± 0.002
0.045 ± 0.002
0.057 ± 0.003
0.028 ± 0.007

¹⁴¹Pr
0.0017 ± 0.0001
0.005 ± 0.0003
0.003 ± 0.000
0.011 ± 0.001
0.0043 ± 0.0003
0.006 ± 0.001
0.0057 ± 0.002
0.0028 ± 0.0001

¹⁴⁶Nd
0.010 ± 0.001
0.033 ± 0.003
0.012 ± 0.001
0.033 ± 0.002
0.015 ± 0.001
0.032 ± 0.013
0.024 ± 0.002
0.014 ± 0.001

¹⁴⁷Sm
0.0025 ± 0.0001
0.008 ± 0.0003
<MQL
0.008 ± 0.001
0.008 ± 0.001
0.014 ± 0.001
0.0032 ± 0.0002
0.0053 ± 0.0003

¹⁵³Eu
0.0028 ± 0.0002
0.009 ± 0.0006
0.003 ± 0.001
0.004 ± 0.001
0.006 ± 0.001
0.007 ± 0.498
0.014 ± 0.001
0.0038 ± 0.0001

¹⁵⁷Gd
0.0014 ± 0.0002
0.004 ± 0.0006
0.003 ± 0.001
0.011 ± 0.001
0.006 ± 0.001
0.013 ± 0.001
0.015 ± 0.001
0.0038 ± 0.0002

¹⁸²W
0.0011 ± 0.0001
0.003 ± 0.006
0.003 ± 0.001
0.002 ± 0.001
0.023 ± 0.002
0.014 ± 0.001
0.368 ± 0.007
0.012 ± 0.001

²⁰²Hg
0.0005 ± 0.0000
0.001 ± 0.0001
0.003 ± 0.001
0.0011 ± 0.0001
0.005 ± 0.001
0.0058 ± 0.0003
0.0045 ± 0.003
0.0072 ± 0.0001

²⁰⁸Pb
0.014 ± 0.001
0.046 ± 0.003
0.023 ± 0.002
0.0010 ± 0.0001
0.301 ± 0.002
0.489 ± 0.009
0.354 ± 0.002
0.512 ± 0.034

MQL, method quantification limit.

3.2. Principal Components Analysis (PCA)

An unsupervised principal component analysis (PCA) was conducted to visualize the effects of geographical locations on elements and the discriminant of 60 pakchoi samples. Figure 2 shows the scree plot which is used to illustrate the contributions of principal components (PCs) in a PCA model. As can be seen that the first three PCs accounted for more than 60% of the total variation of samples (Table 2), we can suppose that these PCs carried major information of variables. The quality of the PCA models was represented by R² and Q² values. The R²X(cum) value at 0.499 and the Q²(cum) value at 0.617 obtained from the PCA model were established by PC1 and PC2. The result indicated that 49.9% and 61.7% of the total variation could be explained and predicted, respectively, by the first two PCs.

Figure 2: Eigenvalues scree plot of the principal components.

Table 2: Principal component analysis summary.

PC
R²X(Cum)
Q²X(Cum)
Eigenvalues

1
0.2883
0.2582
12.1088

2
0.4993
0.6172
8.8615

3
0.6122
0.8371
4.7413

4
0.7137
0.9498
4.2656

5
0.7726
0.9875
2.4729

6
0.8161
0.9975
1.8260

7
0.8486
0.9996
1.3642

8
0.8754
0.9999
1.1253

9
0.8978
1.0000
0.9402

10
0.9112
1.0000
0.5660

In the two-dimensional score scatterplot based on PC1 and PC2 (Figure 3), pakchoi samples from 6 areas were sharply separated, which highlighted the possibility to distinguish the origins of this vegetable merely by the metal elements distribution. In addition, the contribution of variables to classify was determined by the loading scatterplot (Figure 4). The contents of  ⁶⁹Ga, ¹⁵⁷Gd, and ¹⁵³Eu had the highest weight on the first PC, while ⁵⁶Fe and ⁶⁴Cu showed the largest contribution to the separation on PC2.

Figure 3: PCA scores scatterplot of principal components 1 and 2.

Figure 4: PCA loading scatterplots.

Moving range charts illustrated the means and ranges of common distributions of variables on the cases. As can be seen, Thanh Da samples had significantly higher contents of ⁵⁶Fe than the samples from other areas that could be used as a marker for distinguishing (Figure 5(b)). Meanwhile, in the samples from Linh Nam, the elemental isotopes ⁵⁹Co and ¹¹⁸Sn were dramatically increased as compared to samples from other sites that could be found as two important discriminating elements for this area (Figures 5(c) and 5(j)). Whereas, a unique combination of three elements (²⁸Si, ⁶³Cu, and ⁸⁵Rb) was identified as the main discriminator of pakchoi from Thanh Xuan (Figure 5(a), 5(d), and 5(g)). Similarly, a group of three elements, namely, ⁶⁹Ga, ¹⁰⁷Ag, and ¹³⁷Ba, contributed strongly to the discrimination of samples from Van Duc (Figure 5(e), 5(i), and 5(k)). The levels of ¹⁰⁷Ag and ¹³⁷Ba increased sharply in Van Duc samples, compared to significantly low contents in the samples of other areas. For the Van Noi samples, the content of ⁹³Nb was three-fold to tenth-fold higher than the levels of this element in the other samples (Figure 5(h)). Thus, ⁹³Nb was responsible for the unique classification of samples from Van Noi. Interestingly, no elemental isotope was identified that contained at a dramatically high level in Tien Phong samples; however, it was possible to discriminate this area’s samples based on the significantly low content of ⁶⁹Ga, which was also identified as a discriminator for Van Duc (Figure 5(a)). This contrast was also seen on scores scatterplots where samples from Tien Phong and Van Duc distributed on two contrary ranges of PC1.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

(k)

Figure 5: Moving range chart of (a) ²⁸Si, (b) ⁵⁶Fe, (c) ⁵⁹Co, (d) ⁶³Cu, (e) ⁶⁹Ga, (f) ⁷⁵As, (g) ⁸⁵Rb, (h) ⁹³Nb, (i) ¹⁰⁷Ag, (j) ¹¹⁸Sn, and (k) ¹³⁷Ba. The vertical axis illustrates the content (µg/g, fresh weight base) of each element in the sample. The horizontal axis shows names of the areas.

To the best of our knowledge, this is the first time the mineral elements are used for discrimination of pakchoi geographical origins. The chemometric-based ICP-MS approach was applied to distinguish many types of foods and plants, such as cabbage [6], chili [17], and tea [18]. PCA was used widely in those reports that could find clearly and preciously the most important elements for the classification of the analytes.

4. Conclusions

This study is the first report of the method to determine the geographical origin of various pakchoi samples based on ICP-MS analysis and multivariate statistical analysis (PCA). The results illustrated that eleven elemental discriminators for vegetables were identified, namely, ²⁸Si, ⁵⁶Fe, ⁵⁹Co, ⁶³Cu, ⁶⁹Ga, ⁷⁵As, ⁸⁵Rb, ⁹³Nb, ¹⁰⁷Ag, ¹¹⁸Sn, and ¹³⁷Ba. By using the PCA model, 60 samples of 6 areas in Hanoi were classified clearly with one-half of the variation that could be explained. The findings of this study imply that the discrimination of pakchoi based on their geographical locations could be well achieved by the combination of elemental profiling and multivariate statistical analysis. The reported method is convenient, fast, and environmentally friendly, with potential applications in distinguishing origins of a larger amount, ranges, and types of vegetables.

Data Availability

The majority of the data used to support the findings of this study are included within the article. Other data are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This research was funded by the Vietnam Academy of Science and Technology (TĐNDTP.01/19-21 and QTCZ01.01/20-21). Center for Research and Technology Transfer, Vietnam, is appreciated for partly supporting to this research. The authors thank Le Quang Huong, Tran Hai Anh, Do Van Chi, and Nguyen Tien Dat for helpful advice on this research.

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