Improved Discrimination for Brassica Vegetables Treated with

Article pubs.acs.org/JAFC

Improved Discrimination for Brassica Vegetables Treated with Agricultural Fertilizers Using a Combined Chemometric Approach Yuwei Yuan,*,†,‡ Guixian Hu,†,‡ Tianjin Chen,§ Ming Zhao,∥ Yongzhi Zhang,†,‡ Yong Li,⊥ Xiahong Xu,†,‡ Shengzhi Shao,†,‡ Jiahong Zhu,†,‡ Qiang Wang,†,‡ and Karyne M. Rogers*,# †

Institute of Quality and Standards for Agricultural Products, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, P.R. China ‡ Key Lab for Pesticide Residue Detection, Ministry of Agriculture, Hangzhou 310021, P.R. China § Institute of Quality Standards and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R. China ∥ Qingdao Academy of Agricultural Sciences, Qingdao 266100, P.R. China ⊥ College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China # National Isotope Centre, GNS Science, 30 Gracefield Road, Lower Hutt 5040, New Zealand ABSTRACT: Multielement and stable isotope (δ13C, δ15N, δ2H, δ18O, 207Pb/206Pb, and 208Pb/206Pb) analyses were combined to provide a new chemometric approach to improve the discrimination between organic and conventional Brassica vegetable production. Different combinations of organic and conventional fertilizer treatments were used to demonstrate this authentication approach using Brassica chinensis planted in experimental test pots. Stable isotope analyses (δ15N and δ13C) of B. chinensis using elemental analyzer−isotope ratio mass spectrometry easily distinguished organic and chemical fertilizer treatments. However, for low-level application fertilizer treatments, this dual isotope approach became indistinguishable over time. Using a chemometric approach (combined isotope and elemental approach), organic and chemical fertilizer mixes and low-level applications of synthetic and organic fertilizers were detectable in B. chinensis and their associated soils, improving the detection limit beyond the capacity of individual isotopes or elemental characterization. LDA shows strong promise as an improved method to discriminate genuine organic Brassica vegetables from produce treated with chemical fertilizers and could be used as a robust test for organic produce authentication. KEYWORDS: stable isotope, multielement, Brassica chinensis, chemometric, linear discriminant analysis (LDA)

■

δ15N value of crops is also affected by other factors such as fertilizer type,10 application amount and time,10 growth stages,11 and type of manure.12,13 Over the last ten years, many studies have used δ15N analysis to discriminate organically and conventionally grown vegetables such as carrots, tomatoes, lettuce, maize, Chinese cabbage, cabbage, onion, paprika, broccoli, and zucchini.12−17 However, overlapping δ15N values are sometimes seen in some organic and conventional produce,15,17,18 especially for slower-growing produce and produce fertilizer with mixed chemical−compost applications.16 This overlap in δ15N values was observed in a recent growth study of Brassica chinensis,19 where it was difficult to discriminate between organic and conventional production methods using nitrogen isotopes alone. Further limitations of this δ15N isotope discrimination method are that it does not easily discriminate synthetic nitrogen fertilizers from green manures (containing nitrogen-fixing legumes where δ15Nair = 0‰).20 Other isotopes such as hydrogen (δ2H), carbon (δ13C), oxygen (δ18O), magnesium (δ25Mg, δ26Mg), and sulfur (δ34S)

INTRODUCTION Organic food grown without chemical fertilizers and pesticides is thought to be healthier by consumers. Increasing demand for organic produce has pushed organic food sales to USD $72 billion globally in 2013, with around USD $2.6 billion spent on organic food in China alone.1 Organic fruit and vegetables retail between 50 and 200% higher than the conventional produce.2 However, with increased consumer demand, there is a higher risk that conventional foods may be mislabeled as organic and that the consumer will be subjected to paying more for fraudulently labeled food.3 It is apparent that consumer confidence in organic food has already been seriously damaged in China, with many shoppers showing a strong preference for imported organic produce which is perceived as more trustworthy than domestically grown organic produce.4 There is now an urgent need to enhance the inspection and certification of organic food beyond paper audits and introduce discriminatory scientific techniques to regulate, verify, and restore consumer confidence.5,6 The chemical composition of agricultural produce is affected by several factors such as crop species, cultivar, growth stages, soil fertility, climate, cultivation, rotation, and fertilization application.7,8 It is now established that synthetic N fertilizer application results in δ15N values in the plants that are lower than those resulting form organic manures.9−19 However, the © XXXX American Chemical Society

Received: January 27, 2016 Revised: June 7, 2016 Accepted: June 29, 2016

A

DOI: 10.1021/acs.jafc.6b00453 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

Article

Journal of Agricultural and Food Chemistry Table 1. Fertilizer Treatments and Application Rates for B. chinensis Pot Experiments application rate treatment (n = 3) organic chemical organic− chemical(1:1) control

low dose (75 kg Nha−1)

high dose (150 kg Nha−1)

chicken manure 18.7 g/pot (coded L−M) chicken manure 37.4 g/pot (coded H−M) urea 0.72 g, Ca(H2PO4)2·H2O 1.39 g, K2SO4 0.33g/pot (coded L− urea 1.45 g, Ca(H2PO4)2·H2O 2.78 g, K2SO4 0.67 g/pot (coded H−C) C) chicken manure 9.35 g + urea 0.36 g, Ca(H2PO4)2·H2O 0.70 g, chicken manure 18.7 g + urea 0.72 g, Ca(H2PO4)2·H2O 1.39 g, K2SO4 0.33 K2SO4 0.16 g/pot (coded L−M+C) g/pot (coded H−M+C) no fertilizer application (coded CK)

been used widely in agriculture and food analysis. For example, Gundersen et al. used the third and fourth principal components to separate two pea cultivation methods when PCA was applied to 55 elements.37 Liu et al. used PCA, PLSDA, and LDA methods to classify Riesling wines from different countries.30 We propose that a chemometric approach will provide a more robust discrimination method to authenticate organic produce, especially in agricultural systems where farmers may be using multiple cultivation methods, including mixing organic and synthetic fertilizers, even at lower application levels. We investigate B. chinensis uptake from soil treated with different fertilizer types and application rates. A multicompound chemometric approach is developed to explore the variation of δ15N, δ13C, δ2H, and δ18O and multielement composition in B. chinensis using experimental pot trials. We compare traditional δ15N isotope organic and conventional produce discrimination with PCA and LDA chemometric methods.

have been used in the past to discriminate between organic and conventional cultivation with mixed results.20 Issues arose where multielement and isotopic compositions of plants were affected by agricultural practice, such as fertilizer application with organic manures where animals had been fed mineral supplements. Elemental studies of organic lettuce had significantly higher levels (p < 0.05) of Cr, Cu, Fe, K, and Mg, whereas conventional samples had higher levels of Mn and Zn.21 Multielement concentrations in tomatoes were also shown to be influenced by the fruit maturation stage, with higher concentrations in the final stage, and more micronutrients in organic tomatoes than in conventional tomatoes.22 The recent introduction of chemometric analyses (using multiple components) has broadened the efficiency and accuracy of screening methods. No systematic difference was found between organic and conventional crops, such as winter wheat, spring barley, fava bean, and potato, when individual essential plant nutrients were analyzed. However, they were differentiated by chemometric analysis using a combination of 14 elements.23 A further study of coffee was able to discriminate and classify conventional and organic production with a 96.3% accuracy level for multilayer perceptron (MLP) and support vector machine (SVM), and 98.2% for naive Bayes (NB).24 Chemometric methods can be roughly divided into two categories, unsupervised methods and supervised methods,25 with the former known as principal component analysis (PCA)26,27 and is simply a descriptive output of the underlying data structure. The latter is known as linear discriminant analysis (LDA) and is used to sort samples into predefined classes.28,29 PCA is a multivariate technique that analyzes a data table in which observations are described by several intercorrelated quantitative dependent variables. Its goal is to extract important information from a table, represent it as a set of new orthogonal variables called principal components, and to map observed patterns. Mathematically, PCA depends upon the eigen-decomposition of positive semidefinite matrices and upon the singular value decomposition (SVD) of rectangular matrices. PCA is an unsupervised method of pattern recognition in the sense that no data grouping is required before the analysis.26−30 The LDA method is a well-known technique for dimension reduction and feature extraction. It was used here to find an optimal transformation that maps the data into a lower dimensional space that minimizes the within-class distance and simultaneously maximizes the between-class distance, thus achieving maximum discrimination. The basic idea underlying discriminant function analysis is to establish whether groups differ with regard to the mean of a variable and then to use that variable to predict group membership.31−36 Chemometric techniques make it possible to investigate the dependence and connections among variables, helping to reduce and simplify data, and also to classify objects into groups. It has

■

MATERIALS AND METHODS

Experimental Pot Trials and Plant Growth. Experimental trials were conducted in plastic pots (200 mm bottom diameter × 300 mm top diameter × 220 mm high) in an open, experimental field site at Qingdao Academy of Agricultural Sciences, Shandong Provence, China. Three different fertilizer treatments, each with two application rates, were studied, along with a control (no treatment) (Table 1). Fertilizer treatments consisted of an organic manure (chicken manure with total N of 20.4 g kg−1, P2O5 of 40.0 g kg−1, K2O of 24 g kg−1, δ15N of +13.0‰), a synthetic fertilizer (urea with total N of 460 g kg−1, δ15N of −1.2‰), and a mixed organic manure−synthetic fertilizer (δ15N of +6.1‰). To investigate effects from different fertilizer application rates, a lower dose (equivalent to 75 kg Nha−1) and a higher dose (150 kg Nha−1) of the three fertilizer treatments were applied to pots containing soil, replicating standard farming fertilizer practices to meet vegetable growth requirements. A control pot was included, and the soil was given no additional fertilizer. The soil used in this study was treated with fertilizers in spring 2011 and had no further treatment until this study started in 2012, as described in our previous publication.19 A total of 63 pots (9 control pots and 18 pots for organic, chemical, and organic−chemical fertilizer treatments, respectively) containing a total of 10 kg of soil and fertilizer were prepared over 5 days, then 20 B. chinensis seeds were planted in each pot. Sample Collection and Preparation. B. chinensis plant tissue (leaf and stem) from each fertilizer treatment were sampled from three pots at the seedling stage (approximately 20 plants), an intermediate growth stage (approximately 10 plants), and at maturity (approximately 6 plants) on the 4th, 15th, and 27th of June 2012, respectively. Because of the limited soil content of each pot, a portion of soil was collected only at the seedling stage. The fertilizers were dried at 60 °C in an oven, and soil samples were air-dried. B. chinensis samples were dried at 105 °C in an oven for 30 min to deactivate all enzymes and then finished at 70 °C. All the samples (including urea) were homogenized, passed through a 100 mesh sieve, and stored in desiccators before analysis. B


Article

Journal of Agricultural and Food Chemistry Table 2. Elemental Concentrations and Isotope Values of Soils with Different Treatments at Seedling Stagea treatment analyte

CK

L−M

H−M

L−C

H−C

L−M+C

H−M+C

δ C (‰) C (%) δ15N (‰) N (%) 206 Pb/207Pb 208 Pb/206Pb 7 Li (mg/kg) 9 Be (mg/kg) 23 Na (g/kg) 24 Mg (g/kg) 27 Al (g/kg) 39 K (g/kg) 44 Ca (g/kg) 51 V (mg/kg) 52 Cr (mg/kg) 55 Mn (mg/kg) 56 Fe (g/kg) 59 Co (mg/kg) 60 Ni (mg/kg) 65 Cu (mg/kg) 66 Zn (mg/kg) 69 Ga (mg/kg) 85 Rb (mg/kg) 88 Sr (mg/kg) 95 Mo (mg/kg) 107 Ag (mg/kg) 111 Cd (mg/kg) 115 In (mg/kg) 133 Cs (mg/kg) 138 Ba (mg/kg) 205 Tl (mg/kg) 208 Pb (mg/kg) 209 Bi (mg/kg)

−23.1 ± 0.3cd 0.59 ± 0.02de 3.1 ± 0.1d 0.06 ± 0.00c 1.13 ± 0.01a 2.20 ± 0.03a 34.5 ± 0.8a 3.1 ± 0.1a 20.0 ± 0.3bc 4.5 ± 0.1a 41.4 ± 0.7c 36.6 ± 0.8b 2.0 ± 0.1e 45.6 ± 4.2a 38.7 ± 3.1a 405.1 ± 25.2b 29.8 ± 852.7a 11.2 ± 0.1ab 25.4 ± 0.5ab 23.0 ± 0.6c 50.7 ± 2.0b 29.9 ± 3.3a 91.3 ± 0.8c 102.4 ± 3.0c 2.0 ± 1.6a 0.41 ± 0.02ab 0.20 ± 0.01bc 0.06 ± 0.00a 5.1 ± 0.2a 435.6 ± 16.9bc 0.94 ± 0.01a 28.2 ± 0.2a 0.26 ± 0.00a

−21.9 ± 0.4b 0.74 ± 0.09bc 6.1 ± 0.6b 0.08 ± 0.01b 1.13 ± 0.01a 2.20 ± 0.00a 34.7 ± 1.5a 3.0 ± 0.2a 19.5 ± 1.0bc 4.6 ± 0.3a 43.0 ± 1.4bc 36.7 ± 1.3b 2.8 ± 0.2b 45.7 ± 5.0a 37.9 ± 2.9a 413.3 ± 32.6ab 30.4 ± 1.3a 11.2 ± 0.9ab 26.1 ± 1.4a 24.2 ± 0.2b 58.1 ± 2.4a 31.0 ± 2.2a 93.4 ± 3.0bc 110.9 ± 1.2b 1.0 ± 0.1a 0.46 ± 0.05a 0.22 ± 0.01a 0.06 ± 0.00a 5.1 ± 0.1a 426.5 ± 19.6c 0.92 ± 0.02ab 28.1 ± 1.9a 0.23 ± 0.01b

−18.9 ± 1.3a 0.99 ± 0.08a 7.7 ± 0.2a 0.11 ± 0.01a 1.13 ± 0.00a 2.19 ± 0.01a 33.6 ± 0.9a 2.8 ± 0.1a 19.2 ± 0.4c 4.6 ± 0.2a 44.0 ± 1.5b 36.7 ± 1.4b 3.8 ± 0.3a 43.6 ± 2.4a 38.3 ± 2.1a 445.8 ± 6.1a 30.5 ± 1.1a 11.4 ± 0.5a 24.7 ± 0.6abc 25.3 ± 1.1a 61.7 ± 3.8a 31.2 ± 1.3a 90.2 ± 3.3c 119.1 ± 4.9a 1.0 ± 0.1a 0.42 ± 0.062ab 0.20 ± 0.02ab 0.05 ± 0.00b 5.0 ± 0.2a 426.3 ± 4.7c 0.87 ± 0.02c 25.9 ± 1.0ab 0.21 ± 0.00cd

−22.7 ± 0.3bcd 0.53 ± 0.04e 2.8 ± 0.3de 0.06 ± 0.00c 1.13 ± 0.01a 2.20 ± 0.02a 34.3 ± 0.6a 3.0 ± 0.1a 20.3 ± 0.7ab 4.6 ± 0.1a 46.0 ± 0.5a 38.2 ± 0.7ab 2.1 ± 0.1de 45.5 ± 0.7a 36.2 ± 0.8a 413.1 ± 15.1ab 3.2 ± 0.2a 10.6 ± 0.1abc 24.1 ± 0.6bc 21.1 ± 0.4d 48.4 ± 1.7b 31.1 ± 1.6a 97.5 ± 2.4ab 110.9 ± 1.4b 1.1 ± 0.1a 0.41 ± 0.01ab 0.20 ± 0.01b 0.05 ± 0.00b 5.0 ± 0.1ab 450.9 ± 3.9ab 0.90 ± 0.02bc 27.2 ± 0.8ab 0.21 ± 0.00c

−23.1 ± 0.1d 0.58 ± 0.07e 2.4 ± 0.3e 0.07 ± 0.00bc 1.13 ± 0.01a 2.20 ± 0.01a 33.2 ± 1.1a 3.1 ± 0.3a 20.0 ± 0.7bc 4.5 ± 0.2a 46.2 ± 1.4a 38.0 ± 1.5ab 2.1 ± 0.1de 45.4 ± 7.0a 36.3 ± 2.5a 412.5 ± 25.3ab 31.2 ± 0.9a 10.5 ± 0.5bc 23.3 ± 0.5c 20.3 ± 0.3d 47.7 ± 1.3b 32.1 ± 1.2a 97.1 ± 4.6ab 110.4 ± 2.2b 1.2 ± 0.2a 0.38 ± 0.01b 0.19 ± 0.00bc 0.05 ± 0.00b 4.7 ± 0.1bc 456.2 ± 7.0a 0.86 ± 0.03cd 25.8 ± 0.7ab 0.19 ± 0.01de

−22.0 ± 0.3bc 0.72 ± 0.06c 4.1 ± 0.5c 0.08 ± 0.01b 1.13 ± 0.01a 2.20 ± 0.02a 33.6 ± 0.2a 2.9 ± 0.1a 21.0 ± 0.2a 4.5 ± 0.1a 47.6 ± 1.0a 39.7 ± 0.7a 2.4 ± 0.1cd 45.5 ± 1.3a 37.7 ± 2.9a 402.1 ± 9.7b 30.5 ± 0.7a 10.2 ± 0.1c 22.8 ± 0.2c 20.9 ± 0.4d 50.0 ± 2.0b 31.5 ± 1.4a 99.3 ± 0.9a 114.0 ± 1.6ab 1.0 ± 0.1a 0.41 ± 0.04ab 0.18 ± 0.01c 0.05 ± 0.00b 4.7 ± 0.1c 460.9 ± 11.8a 0.86 ± 0.02cd 27.9 ± 4.6ab 0.19 ± 0.01de

−21.9 ± 0.3b 0.74 ± 0.09b 4.7 ± 0.2c 0.08 ± 0.01b 1.13 ± 0.01a 2.21 ± 0.01a 34.5 ± 1.2a 2.9 ± 0.0a 20.4 ± 0.2ab 4.6 ± 0.2a 47.7 ± 2.1a 39.0 ± 1.1a 2.5 ± 0.2bc 47.5 ± 7.2a 37.3 ± 3.1a 405.9 ± 25.0b 31.6 ± 1.3a 10.2 ± 0.5c 23.4 ± 1.1c 21.2 ± 0.5d 49.3 ± 1.3b 30.3 ± 1.8a 97.0 ± 1.5ab 113.2 ± 6.1ab 1.1 ± 0.2a 0.44 ± 0.04ab 0.20 ± 0.01ab 0.05 ± 0.00b 4.6 ± 0.1c 441.6 ± 2.5abc 0.83 ± 0.02d 24.4 ± 0.6b 0.18 ± 0.01e

13

a

Values are means with standard errors (n = 3), and lower case letters indicate differences at α = 0.05 between different treatments at the same stage.

Stable Isotope Analytical Procedures. Around 0.2−0.5 mg of fertilizer, soils, and plant tissue were weighed into 6 × 4 mm tin capsules for stable isotope (δ13C and δ15N) analysis. Samples were also weighed into silver capsules (0.3 mg for δ2H and δ18O) and equilibrated for 3 days in a desiccator. All stable isotope samples were analyzed using an elemental analyzer−isotope ratio mass spectrometer (EA-IRMS, Elementar Vario PYRO cube equipped with Isoprime100, Isoprime Ltd., England). Samples were measured in duplicate with results calculated according to

Xsample(‰) = (R sample/R standard) − 1

Pb Isotopes and Multielemental Analysis. Prior to Pb isotopes (207Pb/206Pb, 208Pb/206Pb) and multielemental analysis, duplicate 500 mg samples were digested overnight in acid-washed 100 mL microwave oven vessels containing 4 mL of reagent grade 65% HNO3. The digestion fluid was spiked with 2 mL of 30% w/v H2O2 (reagent grade) and heated in a microwave oven (CEM Mars 5, CEM Co. Ltd., U.S.) under temperature control mode. After cooling, the digested samples were subsequently diluted to 25 mL with ultrapure water in a plastic tube. All samples were then measured with an ICPMS instrument (Thermo Fisher X-series II, Thermo Co. Ltd., U.S.). Multielemental analysis of B. chinensis samples resulted in the detection of 24 elements, which were further divided into three groups based on their relative concentrations: Group 1 elements (concentrations ranged from 0.1 to 10 mg/kg) included 7Li, 9Be, 51V, 59 Co, 69Ga, 107Ag, 111Cd, 133Cs, 205Tl, 208Pb,52Cr, 60Ni, 64Cu, and 95Mo. Group 2 elements (concentrations ranging from 10 to 500 mg/kg) included 27Al, 55Mn, 56Fe, 65Zn, 85Rb, and 138Ba. Group 3 elements (concentrations ranging from 1.0 to 50.0 g/kg) were 23Na, 24Mg, 39K, and 44Ca. Lead isotope ratios (206Pb/207Pb, 208Pb/206Pb) were measured using the ratio mode, and a semiquantitative method, (which had a lower background interference and quickly determined more elements than a quantitative method) was used to analyze the 24 elements with a precision of −19 to 13% and an accuracy of 84.3−

(1)

where X is either δ13C, δ15N, δ2H, or δ18O, and R values are the corresponding isotope ratio of 13C/12C, 15N/14N, 2H/1H, or 18O/16O. Reference gases we used to compare the unknown samples were CO2 (δ13C = −27.92 ± 0.15‰), N2 (δ15N = −1.3 ± 0.2‰), H2 (δ2H = −222.6 ± 1.5‰), and CO (δ18O = 14.6 ± 0.3‰). Standard reference materials were used as internal standards and quality control; IAEA-N1 (ammonium sulfate, δ15N = 0.4 ± 0.2‰), USGS24 (graphite, δ13C = −16.0 ± 0.1‰), VSMOW (δ2H = 0‰), SLAP (δ2H = −428.0‰), IAEA601 (δ18Ovsmow = 23.3 ± 0.3‰), and IAEA602 (δ18Ovsmow = 71.4 ± 0.5‰). The analytical precision and reproducibility were better than 0.15‰, 0.2‰, 1.5‰, and 0.3‰ for δ13C, δ15N, δ2H, and δ18O, respectively. C


Pb/207Pb

Pb/206Pb

206

208

δ18O (‰)

δ2H (‰)

L−M −28.8 ± 0.6cdA −30.1 ± 0.1eA −29.6 ± 0.3bA 10.4 ± 0.9aA 11.2 ± 1.1bA 9.9 ± 0.6abcA −109.2 ± 32.7aA −127.0 ± 3.7bA −129.5 ± 2.8abA 21.8 ± 1.9aA 16.4 ± 0.2cB 16.3 ± 0.3abB 1.14 ± 0.01aA 1.14 ± 0.01bA 1.13 ± 0.01dA 2.17 ± 0.04aA 2.18 ± 0.04aA 2.15 ± 0.04aA

CK

−29.4 ± 0.4dA −30.1 ± 0.1eB −29.9 ± 0.3bAB 10.0 ± 1.3aA 11.0 ± 0.2bA 10.3 ± 1.6abA −128.4 ± 3.5aB −119.6 ± 5.9aA −130.6 ± 2.5abB 19.7 ± 0.3bA 16.6 ± 0.2bcB 16.9 ± 0.4aB 1.14 ± 0.01aB 1.14 ± 0.01bB 1.16 ± 0.01aA 2.15 ± 0.03aA 2.16 ± 0.04abA 2.12 ± 0.05aA

H−M −28.9 ± 0.6cdA −29.7 ± 0.2deB −30.0 ± 0.1bB 10.3 ± 0.7aB 12.8 ± 0.7aA 9.6 ± 0.6abcB −127.9 ± 0.3aB −122.6 ± 1.3abA −131.8 ± 1.9bC 20.0 ± 1.4abA 17.0 ± 0.3abB 15.8 ± 0.2bB 1.14 ± 0.02aA 1.14 ± 0.01bA 1.16 ± 0.00abA 2.12 ± 0.04aA 2.16 ± 0.01abA 2.13 ± 0.00aA

−28.3 ± 0.3bcA −28.6 ± 0.2abAB −29.1 ± 0.5aB 3.2 ± 0.6cC 6.2 ± 1.3cB 8.5 ± 0.5bcA −123.2 ± 3.3aA −122.2 ± 3.9abA −127.7 ± 5.8abA 20.6 ± 0.8abA 17.2 ± 0.1aB 16.7 ± 0.2aB 1.15 ± 0.02aA 1.15 ± 0.01abA 1.14 ± 0.01cA 2.15 ± 0.03aA 2.14 ± 0.03abcA 2.15 ± 0.03aA

L−C

treatment H−C −27.4 ± 0.3aA −28.3 ± 0.1aB −29.0 ± 0.2aC 2.8 ± 1.4cC 5.3 ± 0.5cB 8.4 ± 1.3cA −119.2 ± 2.9aA −122.1 ± 2.3abA −127.5 ± 2.3abB 20.3 ± 0.5abA 17.0 ± 0.2abB 16.6 ± 0.5aB 1.15 ± 0.02aA 1.14 ± 0.01bA 1.15 ± 0.01abcA 2.12 ± 0.03aB 2.16 ± 0.00abA 2.12 ± 0.01aB

L−M+C −27.9 ± 0.3abA −29.0 ± 0.6bB −29.6 ± 0.2bB 7.4 ± 1.1bB 10.4 ± 0.9bA 10.9 ± 1.1aA −123.3 ± 0.6aA −123.8 ± 1.6abA −125.5 ± 3.1aA 20.2 ± 0.5abA 17.0 ± 0.6abB 16.4 ± 0.2abB 1.14 ± 0.01aA 1.14 ± 0.02bA 1.15 ± 0.00bA 2.17 ± 0.03aA 2.13 ± 0.02bcAB 2.13 ± 0.01aB

H−M+C −28.1 ± 0.4abA −29.5 ± 0.1cdB −29.6 ± 0.2bB 7.4 ± 1.1bB 10.0 ± 0.6bA 10.2 ± 1.3abcA −120.8 ± 1.2aA −122.4 ± 1.3abA −126.9 ± 3.1abB 20.3 ± 1.3abA 16.6 ± 0.2bcB 17.0 ± 0.6aB 1.15 ± 0.01aA 1.16 ± 0.01aA 1.15 ± 0.00bA 2.14 ± 0.02aA 2.11 ± 0.01cB 2.13 ± 0.01aAB

The labels aS, M, and T represent the seedling, middle, and terminal stages, respectively. Values are means with standard errors (n = 3). Lower case letters indicate differences at α = 0.05 between different treatments at the same stage, and capital letters indicate differences in the same treatment between different stages.

a

S M T S M T S M T S M T S M T S M T

δ15N (‰)

stage

isotope

δ13C (‰)

a

Table 3. Stable Isotope Values of B. chinensis for Different Treatments and Growth Stages

Journal of Agricultural and Food Chemistry Article

D


Article

Journal of Agricultural and Food Chemistry 108.3%. Rh and Re were used as an internal standard solution (1 ng/ mL) to monitor and check instrument drift. Statistical Analysis. The data was first tested for homogeneity of variance and normality of distribution. Analysis of variance (ANOVA) was performed on all experimental variables using a general linear model procedure in SPSS 10.0 software package (SPSS Inc., Chicago, IL, U.S.) to assess treatment effects. When the treatment effects were significant, the mean values were separated by Duncan’s multiple range test. Chemometric methods such as PCA and LDA were then applied to discriminate B. chinensis samples into homogeneous groups according to different fertilizer treatments. Before PCA, the data were normalized to a unit vector and mean-centered, and the first three principal components were considered. In LDA analysis, two functions were built for classification. A cross-validation procedure was applied to assess the LDA model. In this procedure, each individual case was in turn omitted from estimating the model constants, then its group membership was determined from the resulting model and compared to its known group identity to calculate a classification success rate.

organic treatments (H−M, L−M) rather than chemical treatments (H−C, L−C) or organic−chemical treatments (L−M+C, H−M+C), although all treatments become more negative over time with growth and maturity (middle and terminal stage, Table 3). Schmidt et al. also found that plants which underwent organic treatments had more negative δ13C values.14 While the δ13C values of the organic treatments applied to B. chinensis were different from the chemical treatments during midgrowth and at maturity (up to 1.5‰), there was a smaller difference between the organic and chemical−organic treatments (up to 0.9‰). There were much larger differences between the δ15N values of B. chinensis between chemical treatments (δ15N values ranging from 2.8 to 8.5‰) and other treatments (δ15N values ranging from 7.4 to 12.8‰), but there was no significant isotopic difference between the two different application rates for each treatment. The isotope range for the chemical treatment (Δδ15N) is 5.6‰, while the control (CK) and organic and organic−chemical treatments were much lower (Δδ15N between 1.0 and 3.5‰). At the seedling stage, δ15N values of B. chinensis undergoing organic treatments (L−M, H− M) were significantly different from those undergoing chemical treatments (L−C, H−C) and organic−chemical treatments (L−M+C, H−M+C). Although these δ15 N differences decreased as the plants matured, differences between chemical treatments and other treatments were still discernible, confirming the usefulness of δ15N values to discriminate between chemical and organic manure fertilizer application.10,11,15−19,42 No significant difference in δ2H values of B. chinensis were found between organic and chemical treatments. In general, the δ2H values were more positive at the seedling stage than at plant maturity for each treatment. The lowest δ2H value was observed in the control sample seedling (−128.4‰). Although the average δ2H value generally decreased with B. chinensis growth from seedling to terminal stage, there was no significant difference among different treatments or stages. A previous study reported δ2H values could not discriminate organic vegetables such as fava beans and potatoes (no significant difference, p > 0.05) but could be used to discriminate other organic crops such as wheat or barley (with significant difference, p < 0.05);20 this may be due to crop water usage efficiency. The average δ18O values of B. chinensis also decreased from seedling to maturity across all treatments (Table 3), although there was no significant difference between δ18O values of organic and chemical treatments. In general, these results confirm that bulk δ2H and δ18O values are not useful isotopes to authenticate organic produce. Table 3 shows the 206Pb/207Pb and 208Pb/206Pb ratio range of B. chinensis for all treatments and stages, which range from 1.13 to 1.16 and 2.12 to 2.18, respectively. Our results had Pb ratios comparable to that of soils sampled from different regions of Eastern Canada and balsamic vinegars.43,44 This suggests the Pb ratios in our experimental soils were not affected by our fertilizer treatments and there was limited fractionation (uptake) of Pb by B. chinensis. On this basis, individual Pb isotope ratios are not useful for authenticating organic vegetables. Elemental Composition of B. chinensis with Different Fertilizer Treatments. Elemental uptake by specific crops is determined by their growing environment and genetics. With the exception of soil type and climatic effects, cultivation practice such as conventional or organic farming has an

■

RESULTS AND DISCUSSION Influence of Different Fertilizer Treatments on Soil Composition. Major, minor, and trace element concentrations in soil may be affected by fertilizer type38−40 and elemental uptake by plants. In this study, three different fertilizers (chicken manure, urea, and a 50:50 chicken manure−urea mix) were applied at two different application rates (Table 1) to investigate the elemental and isotopic transfer from fertilizer to soil. Higher soil carbon content (%C) and more positive δ13C soil values were found in fertilizers which contained organic chicken manure (H−M, L−M, H−M+C, L−M+C) than in the control sample (CK) and high and low applications of chemical (urea) fertilizers (H−C, L−C). The H−M treated soil had the highest soil nitrogen content (%N 0.11%) of all the soil fertilizer treatments, which ranged between 0.06 and 0.08%, respectively (Table 2). Chicken manure treated soils also had more positive soil δ15N values (L−M 6.1‰ and H−M 7.7‰) compared to other soil treatments including the control soil (3.1−4.7‰). There were no noticeable difference for 206 Pb/207Pb and 208Pb/206Pb ratios among different soil treatments, confirming these ratios were not related to any fertilizers used in this study. Elements such as 23Na, 27Al, and 138Ba had significantly higher concentrations in chicken manure−chemical (L−M+C and H−M+C) and chemical (L−C and H−C) treatments than in organic (L−M and H−M) treatments. Conversely, 44Ca, 65 Cu, and 66Zn contents were higher in organic treatments than in chemical treatments. These elements are commonly used constituents of animal feed supplements for livestock41 and have previously been reported to be responsible for more than 50% of the total Cu and Zn content in some soils.40 For the other elements monitored in this study, there was no significant difference in soils between the different treatments, and they are assumed to be absent in the fertilizers. Isotopic Composition of B. chinensis with Different Fertilizer Treatments. Five key isotopes (δ2H, δ13C, δ15N, δ18O, 206Pb/207Pb, and 208Pb/206Pb) were used to investigate the effects of various fertilizers in B. chinensis plants. Some physical growth variability and δ15N and δ13C isotopic differences were observed between organic (L−M, H−M) and chemical (L−C, H−C) treatments, although individually there was no significant statistical difference for δ2H, δ18O, 206 Pb/207Pb, and 208Pb/206Pb values. B. chinensis seedlings showed more negative δ13C values for plants treated with E


Article

Journal of Agricultural and Food Chemistry

Table 4. Elemental Concentrations of B. chinensis with a Range of 0.1−10 mg/kg for Different Treatments and Growing Stages treatment element 7

Li

9

Be (μg/kg)

51

V

59

Co

69

Ga

107

Ag (μg/kg)

111

Cd

133

Cs

205

Tl

208

Pb

52

Cr

60

Ni

64

Cu

95

Mo

stagea a

S M T S M T S M T S M T S M T S M T S M T S M T S M T S M T S M T S M T S M T S M T

CK

L−M

H−M

L−C

H−C

L−M+C

H−M+C

0.65 ± 0.02aA 0.65 ± 0.16aA 0.43 ± 0.20abA 18 ± 2.1aA 18 ± 6.1aA 5.1 ± 2.6abB 0.92 ± 0.07aA 0.78 ± 0.27aA 0.16 ± 0.19abB 0.36 ± 0.02aA 0.44 ± 0.08aA 0.21 ± 0.03abB 0.51 ± 0.07bA 0.52 ± 0.07aA 0.33 ± 0.10aB 14 ± 4.3aA 7.9 ± 1.7abA 7.7 ± 8.4aA 0.27 ± 0.03abA 0.24 ± 0.01cA 0.15 ± 0.04abcB 0.39 ± 0.04aA 0.38 ± 0.05aA 0.25 ± 0.09abB 0.11 ± 0.01bA 0.11 ± 0.03abA 0.09 ± 0.03aA 0.45 ± 0.05aB 0.61 ± 0.10aA 0.20 ± 0.04aC 1.59 ± 0.00aA 3.15 ± 2.07aA 1.28 ± 0.78abA 1.71 ± 0.11bB 3.34 ± 1.18aA 1.43 ± 0.30abB 5.00 ± 0.41abAB 5.75 ± 0.45aA 4.82 ± 0.30aB 1.77 ± 0.19cB 2.18 ± 0.09bAB 2.22 ± 0.30aA

0.59 ± 0.05abcA 0.55 ± 0.09abA 0.33 ± 0.08bB 18 ± 2.3aA 13 ± 4.2abA 5.4 ± 2.2abB 0.91 ± 0.05aA 0.58 ± 0.19abB 0.15 ± 0.10abC 0.32 ± 0.01aA 0.33 ± 0.04bA 0.21 ± 0.02abB 0.60 ± 0.03aA 0.46 ± 0.07abB 0.28 ± 0.05aC 5.7 ± 2.3cdA 5.2 ± 0.4bA 2.8 ± 1.6aA 0.17 ± 0.03dA 0.19 ± 0.01dA 0.12 ± 0.02bcB 0.20 ± 0.04bcA 0.20 ± 0.03cdA 0.20 ± 0.05bA 0.05 ± 0.01deB 0.08 ± 0.01cdA 0.07 ± 0.01abA 0.49 ± 0.11aA 0.61 ± 0.13aA 0.20 ± 0.06aB 1.90 ± 0.37aAB 2.05 ± 0.41abA 1.12 ± 0.54abB 1.60 ± 0.13bB 2.05 ± 0.10bcA 1.27 ± 0.22bC 4.42 ± 0.13abB 5.70 ± 0.28abA 4.38 ± 0.30abB 2.16 ± 0.39abB 2.66 ± 0.13aA 1.99 ± 0.07abcB

0.55 ± 0.12acA 0.44 ± 0.04bAB 0.36 ± 0.06abB 18 ± 4.2aA 8.8 ± 1.6bA 3.8 ± 0.1bB 0.79 ± 0.12abA 0.40 ± 0.076bB 0.060 ± 0.02bC 0.35 ± 0.08aA 0.30 ± 0.02bA 0.18 ± 0.01bB 0.49 ± 0.08bA 0.49 ± 0.07aA 0.25 ± 0.01aB 3.0 ± 0.8dceA 7.5 ± 6.1abA 2.0 ± 0.3aA 0.15 ± 0.01dB 0.17 ± 0.01dA 0.11 ± 0.00cB 0.15 ± 0.03cdB 0.14 ± 0.036dB 0.23 ± 0.041abA 0.04 ± 0.01deB 0.05 ± 0.01eB 0.08 ± 0.01abA 0.39 ± 0.10aA 0.46 ± 0.04bA 0.21 ± 0.02aB 1.88 ± 0.59aA 1.42 ± 0.11bAB 0.95 ± 0.31bB 1.60 ± 0.26bA 1.67 ± 0.07cA 1.20 ± 0.04bB 4.45 ± 0.23abB 5.31 ± 0.22acA 4.41 ± 0.13abB 2.47 ± 0.03aB 2.88 ± 0.17aA 2.10 ± 0.23abC

0.66 ± 0.02aA 0.65 ± 0.11aA 0.54 ± 0.14aA 20 ± 3.5aA 18 ± 4.0aA 8.2 ± 2.1aB 0.73 ± 0.11abA 0.68 ± 0.24abA 0.25 ± 0.05aB 0.33 ± 0.03aA 0.36 ± 0.02bA 0.25 ± 0.05aB 0.40 ± 0.03cA 0.42 ± 0.06abA 0.28 ± 0.07aB 9.8 ± 1.6bA 9.3 ± 3.3abA 4.1 ± 1.5aB 0.31 ± 0.03abAB 0.38 ± 0.07aA 0.20 ± 0.09aB 0.42 ± 0.03aA 0.44 ± 0.031aA 0.31 ± 0.074aB 0.14 ± 0.00aA 0.12 ± 0.02aA 0.09 ± 0.02aB 0.42 ± 0.02aA 0.53 ± 0.11abA 0.24 ± 0.03aB 1.26 ± 0.16aA 1.94 ± 0.68abA 1.48 ± 0.51abA 1.83 ± 0.10bB 2.54 ± 0.21abcA 1.60 ± 0.29abB 4.78 ± 0.49abAB 5.17 ± 0.55acA 4.13 ± 0.20bB 0.98 ± 0.05dB 1.70 ± 0.21dA 1.75 ± 0.19bcA

0.64 ± 0.14abA 0.64 ± 0.04aA 0.41 ± 0.06abB 16 ± 3.5aA 17 ± 3.6aA 7.4 ± 1.9abB 0.67 ± 0.27bA 0.66 ± 0.13abA 0.27 ± 0.12aB 0.33 ± 0.06aA 0.35 ± 0.03bA 0.23 ± 0.03abB 0.37 ± 0.10cAB 0.43 ± 0.03abA 0.27 ± 0.01aB 6.6 ± 1.0bcA 7.0 ± 1.4abA 2.7 ± 1.5aB 0.34 ± 0.08aA 0.29 ± 0.03bA 0.18 ± 0.03abB 0.22 ± 0.03bB 0.26 ± 0.029cAB 0.31 ± 0.027aA 0.08 ± 0.02cA 0.09 ± 0.01bcA 0.09 ± 0.02aA 0.38 ± 0.03aB 0.49 ± 0.03abA 0.26 ± 0.02aC 2.76 ± 2.22aA 1.82 ± 0.40abA 1.64 ± 0.69abA 3.01 ± 1.25aA 2.76 ± 0.47abA 1.76 ± 0.32aA 5.41 ± 1.52aA 4.77 ± 0.03cdA 4.15 ± 0.27bA 1.04 ± 0.21dB 1.57 ± 0.15dA 1.61 ± 0.016cA

0.50 ± 0.08bcA 0.57 ± 0.08abA 0.47 ± 0.09abA 15 ± 2.6aA 15 ± 3.4abA 7.6 ± 3.4aB 0.68 ± 0.10abA 0.70 ± 0.15abA 0.26 ± 0.11aB 0.30 ± 0.05aAB 0.32 ± 0.05bA 0.23 ± 0.04abB 0.41 ± 0.05cAB 0.48 ± 0.05aA 0.34 ± 0.07aB 3.7 ± 2.0ceA 18 ± 13aB 3.4 ± 2.0aA 0.20 ± 0.02cdA 0.19 ± 0.01cdA 0.15 ± 0.02abcB 0.21 ± 0.01bA 0.24 ± 0.058cA 0.23 ± 0.041abA 0.06 ± 0.00dA 0.07 ± 0.01cdeA 0.08 ± 0.01abA 0.37 ± 0.08aAB 0.49 ± 0.04abA 0.27 ± 0.06aB 1.70 ± 0.39aA 1.96 ± 0.50abA 1.20 ± 0.38abA 1.66 ± 0.23bA 1.89 ± 0.16bcA 1.54 ± 0.17abA 4.61 ± 0.23abA 4.89 ± 0.47cA 4.49 ± 0.44abA 1.93 ± 0.21bcA 2.10 ± 0.16bcA 2.19 ± 0.39aA

0.45 ± 0.07cA 0.55 ± 0.07abA 0.41 ± 0.12abA 16 ± 4.6aA 13 ± 2.1abA 5.6 ± 1.1abB 0.65 ± 0.15bA 0.65 ± 0.13abA 0.16 ± 0.04abB 0.31 ± 0.03aA 0.32 ± 0.05bA 0.20 ± 0.02bB 0.41 ± 0.09cA 0.38 ± 0.03bAB 0.28 ± 0.02aB 2.0 ± 0.3eA 12 ± 13abA 6.0 ± 7.7aA 0.17 ± 0.03dA 0.19 ± 0.01dA 0.13 ± 0.02bcB 0.13 ± 0.04dB 0.15 ± 0.0083dAB 0.20 ± 0.038bA 0.03 ± 0.01eB 0.05 ± 0.00eA 0.06 ± 0.01bA 0.38 ± 0.075aA 0.45 ± 0.042bA 0.25 ± 0.019aB 1.25 ± 0.24aA 2.43 ± 0.84abA 1.95 ± 0.65aA 1.44 ± 0.10bB 2.12 ± 0.45bcA 1.45 ± 0.13abB 4.22 ± 0.19bB 5.11 ± 0.20bcA 4.42 ± 0.29abB 2.06 ± 0.15bcB 2.64 ± 0.24aA 2.03 ± 0.16abB

a

S, M, and T represent seedling, middle, and terminal stages, respectively. Values are means with standard errors (n = 3). Lower case letters indicate differences at α = 0.05 between different treatments at the same stage, and capital letters indicate differences in the same treatment between different stages.

Chinensis leaves became withered at the terminal stage, the elemental leaf concentrations decreased. Other group 1 elements, such as 52Cr, 60Ni, and 64Cu, were found to have higher concentrations for chemical (H−C) treatment at the seedling stage. Our results differed from previous research which reported that the concentrations of copper and zinc were lower in conventional tomatoes than in organic tomatoes.45 Other research has reported significant differences between 64Cu, 52Cr, and 65Zn concentrations in organic and traditional coffee,46 which was explained by crop uptake ability. B. chinensis was found to have higher 95Mo contents from manure treatments than from other treatments.

important impact on elemental uptake. Past research had suggested it may be possible to separate genetically uniform crops that are cultivated both organically and conventionally using multivariate analysis from elemental concentration profiles.37 Group 1 elements (including 7Li, 9Be, 51V, 59Co, 69Ga, 107Ag, 111 Cd, 133Cs, 205Tl, 208Pb, Table 4), varied slightly with different treatments and stages, but most showed no significant elemental difference between organic and chemical treatments. Elemental concentrations were similar in the seedling and middle stages regardless of treatment type, although when B. F


Article

Journal of Agricultural and Food Chemistry Table 5. Concentrations of27Al, 55Mn, 56Fe, 65Zn, 85Rb, and

Ba in B. chinensis for Different Treatments and Growing Stages

138

treatment element (mg/kg) 27

Al

55

Mn

56

Fe

65

Zn

85

Rb

138

stagea

Ba

CK

S M T S M T S M T S M T S M T S M T

331.6 341.1 111.2 45.48 47.4 28.7 537.9 611.4 238.2 32.7 30.2 22.0 43.9 46.3 32.2 14.9 11.4 9.2

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

L−M

16.6aA 120.7aA 71.9abB 0.79cdA 3.0bA 5.6abB 29.2aA 171.6aA 59.7abB 4.9aA 0.95bA 4.3aB 7.4aA 9.3aA 7.4abA 0.8abA 0.9abB 2.7aB

379.7 248.7 105.0 41.9 41.8 27.7 545.5 448.3 232.8 22.0 23.5 17.8 22.9 26.8 26.3 15.9 11.6 7.5

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

H−M

48.3aA 87.4abB 48.8abC 2.5cdeA 0.5cA 3.6abB 64.7aA 97.0abA 60.1abB 1.0dA 1.1cA 1.5aB 0.7bA 4.1bA 4.5bA 1.7aA 1.1abB 1.1aC

344.0 170.9 66.2 38.9 45.4 24.5 541.1 354.0 192.7 19.7 22.6 17.6 20.2 21.2 28.3 12.8 12.7 6.9

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

L−C

58.1aA 25.9bB 3.9bC 0.9eB 1.5bcA 0.9bC 91.3aA 42.6bB 17.1bC 0.9dB 0.8cA 1.5aB 2.6bcB 3.8bAB 4.9abA 1.2bcA 1.6aA 0.3aB

371.2 310.4 157.1 50.8 56.3 38.6 566.8 580.7 302.2 31.2 33.2 22.2 42.9 46.2 37.0 10.6 11.1 6.9

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

H−C

61.8aA 103.0aA 36.4aB 3.8bA 3.8aA 16.1aA 25.4aA 130.3aA 59.2aB 1.8abA 1.2aA 4.9aB 1.5aA 6.6aA 4.8aA 0.5cdA 1.3abA 2.0aB

354.5 305.3 143.3 61.0 52.8 33.4 528.2 569.1 290.9 27.6 29.7 20.3 24.1 28.3 37.4 9.6 10.8 6.8

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

131.5aA 56.8abAB 46.9abB 3.4aA 0.9aB 5.3abC 126.7aA 83.3aA 68.7abB 3.8bcA 3.3bA 1.9aB 4.5bB 1.9bB 5.5aA 2.3dAB 0.7bcA 0.5aB

L−M+C 300.0 309.6 143.0 44.2 42.4 33.2 491.5 519.7 284.0 23.8 24.5 20.7 23.7 28.1 29.9 9.9 12.0 8.8

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

24.5aA 68.5abA 61.0abB 4.6cA 1.6cA 3.0abB 70.4aA 75.8abA 77.9abB 1.34cdA 1.6cA 0.6aB 1.0bA 5.5bA 5.7abA 1.1dAB 0.8abA 1.6aB

H−M+C 292.0 292.0 93.8 46.5 42.0 28.6 493.2 477.0 243.7 20.2 22.2 18.9 16.3 19.5 27.0 10.1 9.2 7.0

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

72.6aA 56.2abA 15.5abB 2.7bcdA 2.7cA 3.2abB 99.0aA 64.8abA 38.6abB 0.8dB 0.4cA 1.2aB 2.9cB 0.5bB 4.0bA 1.4dA 0.8cA 0.6aB

a

S, M, and T represent seedling, middle, and terminal stages, respectively. Values are means with standard errors (n = 3). Lower case letters indicate differences at α = 0.05 between different treatments at the same stage, and capital letters indicate differences in the same treatment between different stages.

Table 6. Concentrations of 23Na, 24Mg, 39K, 44Ca, 52Cr, 60Ni, 64Cu, and 95Mo in B. chinensis for Different Treatments and Growing Stages treatment element (g/kg) 23

Na

24

Mg

39

K

44

Ca

stagea S M T S M T S M T S M T

CK 23.8 22.3 24.0 5.2 4.6 2.9 32.9 37.1 35.2 10.7 12.7 10.4

± ± ± ± ± ± ± ± ± ± ± ±

0.1aA 2.8bcA 1.7aA 0.1aA 0.2bB 0.3aC 2.6cA 4.5abA 5.7aA 0.2aB 0.2bA 0.7aB

L−M 19.1 21.1 23.8 4.5 4.2 3.0 40.0 38.1 30.1 9.1 13.9 10.2

± ± ± ± ± ± ± ± ± ± ± ±

0.4bcC 0.4cB 1.1aA 0.3cA 0.1cA 0.2aB 3.7abA 1.3abA 1.1aB 0.2bB 0.9aA 0.9aB

H−M 17.3 20.4 24.0 4.3 4.4 2.9 42.8 39.8 34.4 9.5 12.8 10.6

± ± ± ± ± ± ± ± ± ± ± ±

L−C

3.3cB 0.5cB 0.8aA 0.3bcA 0.1bcA 0.2aB 2.9aA 3.5abA 0.7aB 1.2aB 0.2abA 0.5aB

23.4 25.0 25.8 5.2 5.1 3.3 32.1 35.2 29.5 9.3 11.0 9.4

± ± ± ± ± ± ± ± ± ± ± ±

0.3aA 2.1aA 2.8aA 0.1aA 0.3aA 0.8aB 3.3cA 3.0bA 2.1aA 0.5bA 0.5cA 1.7aA

H−C 23.4 24.7 24.7 4.9 4.4 3.1 35.9 37.1 32.9 9.6 10.8 9.3

± ± ± ± ± ± ± ± ± ± ± ±

1.2aA 1.4abA 1.6aA 0.2aA 0.2bcA 0.2aB 4.7bcA 4.5abA 3.8aA 0.8aAB 0.9cA 0.5aB

L−M+C 22.0 23.8 23.5 4.9 4.1 3.3 35.5 37.3 35.0 9.5 11.0 10.6

± ± ± ± ± ± ± ± ± ± ± ±

0.8aB 0.6abA 1.2aAB 0.1aA 0.3cB 0.4aC 3.6bcA 2.9abA 5.3aA 0.6aA 1.1cA 1.1aA

H−M+C 21.5 22.6 23.0 4.5 4.0 3.1 40.6 42.2 35.3 9.6 12.5 10.3

± ± ± ± ± ± ± ± ± ± ± ±

1.2abA 0.3abcA 1.4aA 0.1bcA 0.2cA 0.4aB 1.8abA 1.2aA 5.7aA 0.8aB 0.6bA 1.6aAB

a

S, M, and T indicate seedling, middle, and terminal stage, respectively. Values are means with standard errors (n = 3). Lower case letters indicate differences at α = 0.05 between different treatments at the same stage, and capital letters indicate differences in the same treatment between different stages.

Chicken manure has been found to be enriched in 95Mo with a mean value of 36.7 mg/kg compared with other manure and chemical fertilizers.47 For other group 1 elements (Table 4), higher elemental concentrations were observed predominantly during the middle growth stage compared to other stages. Although group 2 elements (27Al, 55Mn, 56Fe, 65Zn, 85Rb, and 138 Ba, Table 5) had elemental concentrations which varied with different fertilizer treatments and growth stages, most had a decreasing trend with growth except for 56Fe and 85Rb. There was a significant difference between organic and chemical treatments at the seedling stage for 55Mn, 65Zn, 85Rb, and 138Ba, but not for 27Al and 56Fe. 55Mn content was previously found to be higher in tomatoes and lettuces cultivated conventionally, while 65Zn was higher in organic tomato and lettuce cultivation because of animal feed supplements in organic manures.45

The concentrations of group 3 elements (such as 23Na, 24Mg, K, and 44Ca, Table 6) also varied with growth stages and different fertilizer treatments. For 23Na and 24Mg, there were significant differences between organic and chemical treatments at seedling and middle stages. However, these differences were observed only at the seedling stage for 39K and at the middle stage for 44Ca. Group 3 elemental comparisons between organic−chemical treatment and CK were found to be quite similar at all growth stages. It is reported that the sampling period is one of most influential factors that affects chemical composition.22 We also found that due to a large variation of elemental concentrations in B. chinensis at the different growth stages, it was difficult to discriminate between organic and chemical treatments based on elemental abundance alone. 39

G


Article


Figure 1. Plots of the first three principal components obtained by PCA analysis of all samples. OT, CT, and OCT represent the samples from organic, chemical, and organic−chemical treatments, respectively.

Figure 2. Results of LDA analysis. OT, CT, and OCT represent samples from organic, chemical, and organic−chemical treatments, respectively. TS represents samples from the testing set.

Organic and Conventional Discrimination Using Multivariate PCA and LDA Analysis. Although elemental and isotope values varied in different growth stages and application rates, the main aim of this study was to compare the ability of individual elements or isotopes to discriminate organically and conventionally grown B. chinensis, with an integrated approach using 30 elements and multiple stable isotopes. Of the 24 studied elements, 15 elements showed some promise to differentiate between organic manure and chemical fertilizer treatments, including 7Li, 23Na, 39K, 44Ca, 55Mn, 60Ni, 65 Zn, 85Rb, 95Mo, 111Cd, 133Cs, 138Ba, and 205Tl, but it was

difficult to discriminate between organic−chemical treatment and control (CK) samples because of overlapping elemental values. Although multi-isotopic or elemental analysis has some potential power to verify organic manure treatments from chemical fertilizers, it seemed harder to discriminate overlapping growing systems where no fertilizer had been added or where synthetic fertilizers had been added to organic systems. Previously, only multivariate elemental fingerprinting based on chemometric methods has been suggested to improve organic food discrimination.23,25,39 This study takes a further step to eliminate ambiguity between agricultural methods and H


Article


organic, chemical, and organic−chemical samples, respectively. Although the testing set correctly discriminated organic and organic−chemical treated samples at a lower confidence level (79.0 and 81.3%) than the chemical treated samples (94.2%), the use of LDA improved the confidence level beyond the limit of individual elements or isotopes so that conventionally grown produce would not be confused with organic or organic− chemical produce. Therefore, from these results, we propose that in a significant number of instances, chemical fertilizers added to organic systems would be detected using LDA. This study presents the results of 24 individual elemental fingerprints and six stable isotopes from B. chinensis samples treated with different fertilizer applications and suggests that it is difficult to discriminate between organic and chemical treated samples using univariate analysis. However, on the basis of LDA analysis using multivariate elemental and isotopic ratios of B. chinensis plants treated with different fertilizer applications, samples were successfully classified according to their fertilizer treatments (organic, chemical, or organic−chemical). It can be concluded that LDA based on PCA is a useful method for discriminating organic B. chinensis (and hence other leafy green vegetables) from conventional produce and that the LDA method shows good potential to authenticate other organic produce. We recommend that current organic inspection and certification schemes should include multielement and stableisotope analyses to improve consumer confidence and producer accountability. Future authentication and traceability studies of China’s organic and conventional agricultural produce could also include an assessment of China’s unique rare earth soil elements into the elemental suite, which may be an important discriminant when verifying domestic and imported produce.

individual techniques, integrating both elemental and stable isotope analyses to demonstrate the robustness of a combined chemometric approach. Principal component analysis was undertaken on the available elemental and isotope data from seedling, middle, and terminal stages (Tables 2−6). The first three principal components represented 39.0%, 20.0%, and 10.1% of the total variables, respectively. Figure 1 shows the plots of the first three principal components obtained by PCA, which separates the cluster groups of chemical and nonchemical treatments (including the organic and organic−chemical samples). However, there was no significant difference between the clusters of organic and organic−chemical treatments by PCA. The data set was then analyzed using linear discriminant analysis based on PCA scores. The 51 samples were randomly collected from the three fertilizer treatments to serve as a training set to establish a discriminant model, and the remaining samples were used as a testing set. In this case, Figure 2 shows the LDA results with the first 11 PCAs and two discrimination functions as follows: Function 1 = 0.1614x1 + 0.4800x 2 − 0.0008x3 + 0.1085x4 − 0.0698x5 + 0.2270x6 − 0.0304x 7 − 0.1565x8 − 0.2321x 9 + 0.1023x10 − 0.3082x11 + 0.0533

Function 2 = − 0.5770x1 − 1.0753x 2 + 0.1386x3 − 0.6782x4 − 0.8506x5 − 0.7689x6 − 0.2061x 7 + 0.0792x8 + 0.9438x 9 + 0.4129x10 + 1.1540x11

■

+ 0.3056

Function 1 can be used to discriminate the clusters of chemical and nonchemical treatments, and function 2 can be applied to discriminate the cluster of organic and organic− chemical treatments. In the training set, the 17th, 27th, and 54th samples are misclassified. The 10th, 21st, and 50th testing samples are correctly classified. LDA was run 2000 times to verify the method’s validity, and discriminant analysis results are reported in Table 7. The rate of correctly classified samples in the training set was 91.7, 94.5, and 87.2% for organic, chemical, and organic−chemical samples, respectively. In the testing set, LDA correctly classified 79.0, 94.2, and 81.3% of the

Corresponding Authors

*Y.Y.: e-mail, [email protected]. *K.M.R.: e-mail, [email protected]; tel, +64 4 5704636; fax, +64 4 5704656. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS This work was funded by the International Technological Cooperation of International Atomic Energy Agency (16567RO), Zhejiang Key Team of Science and Technology Innovation (2010R50028), Zhejiang Academy of Agricultural Sciences (2011R19Y01E01), and the International Scientific and Technological Cooperation Award of the People’s Republic of China (2012DFA31140). We also thank Dr. Xu Chunying from Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences for undertaking the stable isotope analyses.

Table 7. Discrimination of All Samples by LDA after Running 2000 Times discrimination (%)

data set training set

testing set

class organic treatment chemical treatment organic− chemical treatment organic treatment chemical treatment organic− chemical treatment

sample number

organic treatment

chemical treatment

organic− chemical treatment

17

91.66

0.00

8.34

17

0.00

94.47

5.53

17

12.82

0.00

87.18

1

78.95

0.00

21.05

1

0.00

94.20

5.80

1

18.35

0.40

81.25

AUTHOR INFORMATION

■

REFERENCES

(1) The World of Organic Agriculture Statistics and Emerging Trends 2015. FiBL-IFOAM Report; Willer, H., Lernoud, J., Eds.; Research Institute of Organic Agriculture (FiBL), Frick, and IFOAMOrganics International: Bonn, 2015. (2) FAO/ITC/CTA.World Markets for Organic Fruit and Vegetables − Opportunities for Developing Countries in the Production and Export of Organic Horticultural Products. Chapter 1: Requirements for Producing and Exporting Organic Products to Major Markets. http://www.fao.org/docrep/004/y1669e/y1669e00. htm#Contents (accessed January 27, 2016). I


Article


following microwave-assisted digestion. Microchem. J. 2013, 109, 145− 149. (23) Laursen, K. H.; Schjoerring, J. K.; Kelly, S. D.; Husted, S. B. Authentication of organically grown plants-advantages and limitations of atomic spectroscopy for multi-element and stable isotope analysis. TrAC, Trends Anal. Chem. 2014, 59, 73−82. (24) Laursen, K. H.; Schjoerring, J. K.; Olesen, J. E.; Askegaard, M.; Halekoh, U.; Husted, S. Multi-elemental fingerprinting as a tool for authentication of organic wheat, barley, faba bean, and potato. J. Agric. Food Chem. 2011, 59, 4385−4396. (25) Barbosa, R. M.; Batista, B. L.; Varrique, R. M.; Coelho, V. A.; Campiglia, A. D.; Barbosa, F., Jr. The use of advanced chemometric techniques and multi element levels for controlling the authenticity of organic coffee. Food Res. Int. 2014, 61, 246−251. (26) Brereton, R. G. Chemometrics for Pattern Recognition; John Wiley & Sons, Ltd: Chichester, UK. 2009. (27) Lu, Y.; Gao, B.; Chen, P.; Charles, D.; Yu, L. Characterisation of organic and conventional sweet basil leaves using chromatographic and flow-injection mass spectrometric (FIMS) fingerprints combined with principle component analysis. Food Chem. 2014, 154, 262−268. (28) Heberger, K.; Csomos, E.; Simon-Sarkadi, L. Principal Component and Linear Discriminant Analyses of Free Amino Acids and Biogenic Amines in Hungarian Wines. J. Agric. Food Chem. 2003, 51, 8055−8060. (29) Ramadan, Z.; Jacobs, D.; Grigorov, M.; Kochhar, S. Metabolic profiling using principal component analysis, discriminant partial least squares, and genetic algorithms. Talanta 2006, 68, 1683−1691. (30) Liu, L.; Cozzolino, D.; Cynkar, W. U.; Dambergs, R. G.; Janik, L.; O’Neill, B. K.; Colby, C. B.; Gishen, M. Preliminary study on the application of visible-near infrared spectroscopy and chemometrics to classify Riesling wines from different countries. Food Chem. 2008, 106, 781−786. (31) Galdón, B. R.; Rodríguez, L. H.; Mesa, D. R.; León, H. L.; Pérez, N. L.; Rodríguez, E. M. R.; Romero, C. D. Differentiation of potato cultivars experimentally cultivated based on their chemical composition and by applying linear discriminant analysis. Food Chem. 2012, 133, 1241−1248. (32) Cheng, X.; Zhao, T.; Wang, C.; Bligh, S. W. A.; Wang, Z. HPLC fingerprints combined with principal component analysis, Hierarchical cluster analysis and linear discriminant analysis for the classification and differentiation of Peganum sp. indigenous to China. Phytochem. Anal. 2010, 21, 279−289. (33) Gromski, P. S.; Correa, E.; Vaughan, A. A.; Wedge, D. C.; Turner, M. T.; Goodacre, R. Royston, G. A comparison of different chemometrics approaches for the robust classification of electronic nose data. Anal. Bioanal. Chem. 2014, 406, 7581−7590. (34) Mie, A.; Laursen, K. H.; Aberg, K. M.; Forshed, J.; Lindahl, A.; Thorup-Kristensen, K.; Olsson, M.; Knuthsen, P.; Larsen, E. H.; Husted, S. Discrimination of conventional and organic whit cabbage from a long-term field trial study using untargeted LC-MS-based metabolomics. Anal. Bioanal. Chem. 2014, 406, 2885−2897. (35) Vera, L.; Aceña, L.; Guasch, J.; Boqué, R.; Mestres, M.; Busto, O. Characterization and classification of the aroma of beer samples by means of an MS e-nose and chemometric tools. Anal. Bioanal. Chem. 2011, 399, 2073−2081. (36) Patel, I. I.; Trevisan, J.; Singh, P. B.; Nicholson, C. M.; Krishnan, R. K. G.; Matanhelia, S. S.; Martin, F. L. Segregation of human prostate tissues classified high-risk (UK) versus low-risk (India) for adenocarcinoma using Fourier-transform infrared or Raman microspectroscopy coupled with discriminant analysis. Anal. Bioanal. Chem. 2011, 401, 969−982. (37) Gundersen, M.; Bechmann, I. E.; Behrens, A.; Sturup, S. Comparative Investigation of concentrations of major and multi elements in organic and conventional Danish agricultural corps.1.onions(Allium cepaHysam) and peas(Pisumsativum Ping Pong). J. Agric. Food Chem. 2000, 48, 6094−6102. (38) Huang, S. W.; Jin, J. Y. Status of heavy metals in agricultural soils as affected by different patterns of land use. Environ. Monit. Assess. 2008, 139, 317−327.

(3) Tranter, R. B.; Bennett, R. M.; Costa, L.; Cowan, C.; Holt, G. C.; Jones, P. J.; Miele, M.; Sottomayor, M.; Vestergaard, J. Consumers’ willingness−to−pay for organic conversion−grade food: Evidence from five EU countries. Food Policy 2009, 34, 287−294. (4) Food safety concerns boost organic food sales in China. Meat International 2007, 17, 33−34. (5) Chen, J.; Lobo, A. Organic food products in China: determinants of consumers’ purchase intentions. International review of retail, distribution and consumer research 2012, 22, 293−314. (6) Siderer, Y.; Maquet, A.; Anklam, E. Need for research to support consumer confidence in the growing organic food market. Trends Food Sci. Technol. 2005, 16, 332−343. (7) Magkos, F.; Arvaniti, F.; Zampelas, A. Organic food: nutritious food or food for thought? A review of the evidence. Int. J. Food Sci. Nutr. 2003, 54, 357−371. (8) Rosen, C. J.; Allan, D. L. Exploring the benefits of organic nutrients sources for crop production and soil quality. HortTechnology 2007, 17, 422−430. (9) Bateman, A. S.; Kelly, S. M. Fertilizer nitrogen isotope signatures. Isot. Environ. Health Stud. 2007, 43, 237−247. (10) Choi, W. J.; Lee, S. M.; Ro, H. M.; Kim, K. C.; Yoo, S. H. Natural 15N abundances of maize and soil amended with urea and composted pig manure. Plant Soil 2002, 245, 223−232. (11) Yuan, Y. W.; Zhao, M.; Zhang, Z. H.; Chen, T. J.; Yang, G. L.; Wang, Q. Effect of different fertilizers on nitrogen isotope composition and nitrate content of Brassica campestris. J. Agric. Food Chem. 2012, 60, 1456−1460. (12) Choi, W. J.; Arshad, M. A.; Chang, S. X.; Kim, T. H. Grain 15N of crops applied with organic and chemical fertilizers in a four-year rotation. Plant Soil 2006, 284, 165−174. (13) Lim, S. S.; Choi, W. J.; Kwak, J. H.; Jung, J. W.; Chang, S. X.; Kim, H. Y.; Yoon, K. S.; Choi, S. M. Nitrogen and carbon isotope responses of Chinese cabbage and chrysanthemum to the application of liquid pig manure. Plant Soil 2007, 295, 67−77. (14) Schmidt, H. L.; Roßmann, A.; Voerkelius, S.; Schnitzler, W. H.; Georgi, M.; Grassmann, J.; Zimmermann, G.; Winkler, R. Isotope characteristics of vegetables and wheat from conventional and organic production. Isot. Environ. Health Stud. 2005, 41, 223−228. (15) Šturm, M.; Kacjan-Maršić, N.; Lojen, S. Can δ15N in lettuce tissues reveal the use of synthetic nitrogen fertiliser in organic production? J. Sci. Food Agric. 2011, 91, 262−267. (16) Yun, S. I.; Ro, H. M.; Choi, W. J.; Chang, S. X. Interactive effects of N fertilizer source and timing of fertilization leave specific N isotopic signatures in Chinese cabbage and soil. Soil Biol. Biochem. 2006, 38, 1682−1689. (17) Bateman, A. S.; Kelly, S. D.; Woolfe, M. Nitrogen isotope composition of organically and conventionally grown crops. J. Agric. Food Chem. 2007, 55, 2664−2670. (18) Rogers, K. M. Nitrogen isotopes as a screening tool to determine the growing regimen of some organic and nonorganic supermarket produce from New Zealand. J. Agric. Food Chem. 2008, 56, 4078−4083. (19) Yuan, Y. W.; Hu, G. X.; Zhao, M.; Chen, T. J.; Zhang, Y. Z.; Zhu, J. H.; Wang, Q. Variations in the natural 15N abundance of Brassica chinensis grown in uncultivated soil affected by different nitrogen fertilizers. J. Agric. Food Chem. 2014, 62, 11386−11392. (20) Laursen, K. H.; Mihailova, A.; Kelly, S. D.; Epov, V. N.; Bérail, S.; Schjoerring, J. K.; Donard, O. F. X.; Larsen, E. H.; Pedentchouk, N.; Marca-Bell, A. D.; Halekoh, U.; Olesen, J. E.; Husted, S. It is really organic?-Multi-isotopic analysis as a tool to discriminate between organic and conventional plants. Food Chem. 2013, 141, 2812−2820. (21) de Souza Araújo, D. F.; da Silva, A. M. R. B.; de Andrade Lima, L. L.; da Silva Vasconcelos, M. A.; Andrade, S. A. C.; Asfora Sarubbo. The concentration of minerals and physicochemical contaminants in conventional and organic vegetables. Food Control 2014, 44, 242−248. (22) Bressy, F. C.; Brito, G. B.; Barbosa, I. S.; Teixeira, L. S. G.; Korn, M. G. A. Determination of multi element concentrations in tomato samples at different stages of maturation by ICP OES and ICP-MS J


Article

Journal of Agricultural and Food Chemistry (39) Nziguheba, G.; Smolders, E. Smolders, E. Inputs of trace elements in agricultural soils via phosphate fertilizers in European countries. Sci. Total Environ. 2008, 390, 53−57. (40) Luo, L.; Ma, Y. B.; Zhang, S. Z.; Wei, D. P.; Zhu, Y. G. An inventory of trace element inputs to agricultural soils in China. J. Environ. Manage. 2009, 90, 2524−2530. (41) Zhou, D. M.; Hao, X. Z.; Wang, Y. J.; Dong, Y. H.; Cang, L. Copper and Zn uptake by radish and pakchoi as affected by application of livestock and poultry manures. Chemosphere 2005, 59, 167−175. (42) Bateman, A. S.; Kelly, S. D.; Jickells, T. D. Nitrogen isotope relationships between crops and fertilizer: implications for using nitrogen isotope analysis as an indicator of agricultural regime. J. Agric. Food Chem. 2005, 53, 5760−5765. (43) Saint-Laurent, D.; St-Laurent, J.; Duplessis, P.; Lavoie, L. Isotopic Record of Lead Contamination in Alluvial Soils and Tree Rings on Recent Floodplains (Southern Québec, Canada). Water, Air, Soil Pollut. 2010, 209, 451−466. (44) Ndung'u, K.; Hibdon, S.; Véron, A.; Flegal, A. R. Lead isotopes reveal different sources of lead in balsamic and other vinegars. Sci. Total Environ. 2011, 409, 2754−2760. (45) Kelly, S. D.; Bateman, A. S. Comparison of mineral concentrations in commercially grown organic and conventional crops-Tomatoes (Lycopersicon esculentum) and lettuces (Lactuca sativa). Food Chem. 2010, 119, 738−745. (46) Santos, J. S. d.; Santos, M. L. d.; Conti, M. M. Comparative study of metal contents in Brazilian coffees cultivated by conventional and organic agriculture applying principal component analysis. J. Braz. Chem. Soc. 2010, 21, 1468−1476. (47) Zhang, F. D. High quality organic fertilizer - chicken manure. Bulletin of Agricultural Science and Technology 1984, 6, 26 (in Chinese).

K


Improved Discrimination for Brassica Vegetables Treated with

Recommend Documents