The effect of the seasons on geographical traceability of salmonid based on multi-element analysis

Food Control 109 (2020) 106893

Contents lists available at ScienceDirect

Food Control journal homepage: www.elsevier.com/locate/foodcont

The effect of the seasons on geographical traceability of salmonid based on multi-element analysis

T

Cui Hana,b, Shuanglin Donga,b, Li Lia,b,∗, Fayi Weia,b, Yangen Zhoua,b, Qinfeng Gaoa,b a

Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao, 266003, PR China Function Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong Province, 266235, China

b

A R T I C LE I N FO

A B S T R A C T

Keywords: Salmonid Multi-elements Seasons Traceability Multivariate statistics

Salmonid samples collected from two sites in different aquaculture areas (Yantai and Liujiaxia, China) in four seasons were subjected to multi-element analysis. The amounts of 18 elements in fish were measured by inductively coupled plasma atomic emission spectrometry (ICP-AES). The results showed that concentrations of elements in fish from Yantai were stable with seasonal alternation. However, the element concentrations and compositions of fish obtained from Liujiaxia were vulnerable to seasonal change. Principal component analysis (PCA) and canonical discriminant analysis (CDA) were used to visualize the regional and seasonal distribution of samples, and it was determined that CDA was more distinct than PCA. To determine if seasonal effects would influence the discrimination of the geographical origin of salmonid, multivariate statistics including linear discriminant analysis (LDA), k-nearest neighbor (KNN), and partial least squares discriminant analysis (PLS-DA) were used to discriminate fish samples from the two different areas. The results showed that all discrimination techniques could effectively distinguish samples while remaining unaffected by seasonal effects.

1. Introduction

element analysis has discriminated shrimp that has originated from seawater or freshwater, wild or farmed salmon, and the geographic origins of salmon and croaker (Li, Han, Dong, & Boyd, 2019; Flem, Moen, Finne, Viljugrein, & Kristoffersen, 2017; Chaguri et al., 2015; Anderson, Hobbie, & Smith, 2010). Above all, multi-element analysis is a potential tool that can be used for aquatic food traceability. The elements found in fish tissues derive mainly from the water in their environments and the food they consume, and these will be significantly affected by many factors (Jia, Wang, Qu, Wang, & Yang, 2017; Alamdar et al., 2016; Ahmed, Shaheen, & Islam, 2015; Fu, Hu, Tao, Yu, & Zhang, 2013; Squadrone et al., 2013). Previous studies have reported that seasonal effect is an important factor affecting the elemental fingerprints of fish tissues (Fallah, Zeynali, Saei-Dehkordi, Rahnama, & Jafari, 2011; Mendil, Demirci, Tuzen, & Soylak, 2010). Some studies have indicated that there is a wide variation in the concentration of elements in water in different seasons (Alam, Mokhtar, Alam, Bari, & Ern, 2015; Soomro, Siyal, Mirjat, & Sial, 2014), which may affect element concentrations in fish. To create a stable and reliable method for fish product traceability, the seasonal effects on traceability should be considered (Sant’Ana, Ducatti, & Ramires, 2010; Bahar et al., 2008). The effects of seasonal variations on the traceability of vegetable foods have been reported (Drivelos, Danezis,

In addition to the increasing demand for aquatic products, the globalization of the aquatic products industry is also developing rapidly (Addisu & Takele., 2015; Shen, Kang, Chen, & He, 2016). Consequently, there is frequent occurrence of disordered markets selling aquatic products. With the globalization of the import and export trade, there have been substitution, mislabeling, and counterfeit behaviors that have increased consumer worries regarding food safety (Addisu & Takele, 2015; Leal, Pimentel, Ricardo, Rosa, & Calado, 2015; Li, Boyd, & Sun, 2016). Therefore, the authentication and traceability techniques used for fishery or aquaculture products are becoming increasingly important to ensure the safety and certainties of these foods (Addisu & Takele, 2015; Li et al., 2016; Sheikha & Xu, 2017). Multi-element analysis has been successfully applied in the discrimination of geographical origin of vegetable foods, such as rice, fruits, tea, and pepper (Kukusamude & Kongsri, 2018; Gumus, Celenk, Tekin, Yurdakul, & Ertas, 2017; Naccarato, Furia, Sindona, & Tagarelli, 2016; Ma et al., 2016; Hidalgo, Fechner, Marchevsky, & Pellerano, 2016; Joebstl, Bandoniene, Meisel, & Chatzistathis, 2010). In recent years, multi-element analysis has demonstrated the probability for the traceability of aquatic food. According to recent research, multi-

∗

Corresponding author. Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao, 266003, PR China E-mail address: [email protected] (L. Li).

https://doi.org/10.1016/j.foodcont.2019.106893 Received 3 July 2019; Received in revised form 21 August 2019; Accepted 12 September 2019 Available online 13 September 2019 0956-7135/ © 2019 Elsevier Ltd. All rights reserved.

Food Control 109 (2020) 106893

C. Han, et al.

measured using a pH meter (YSI pH100A, Fisher Scientific, Hanover Park, Illinois, USA) (Table 1). The feed samples were pretreated following the method used for the fillet.

Haroutounian, & Georgiou, 2016; Maietti et al., 2012), but there has been a lack of studies demonstrating whether seasonal variations affect the accuracy of discriminating the geographical origins of fish products. Salmonid, especially rainbow trout (Oncorhynchus mykiss) and Atlantic salmon (Salmo salar) are the most economically important cold-water aquaculture species. These species have been widely cultured in China since 1998, and their popularity has increased due to their unique nutritional value (Liu et al., 2018; Addisu & Takele, 2015; Sarma et al., 2013; Bechtel & Oliveira, 2010). In China, the natural reservoir system (NRS) and recirculating aquaculture system (RAS) are two typical methods used in salmonid aquaculture. NRS is a traditional aquaculture method applied in plateau geographical areas to farm rainbow trout (O. mykiss), and the aquatic conditions in NRS are absolutely determined by local climate. The RAS has been set up in coastal areas in China to culture Atlantic salmon (S. salar). RAS, a modern and controlled system, has been widely used because it offers advantages including saved water, improved waste management, and disease control (Zhang et al., 2011). However, according to a report by Bussel, Schroeder, Mahlmann, and Schulz (2014), elements will gradually accumulate in water in the RAS due to the minimal amount of water replacement that occurs. In this study, salmonid samples were collected from two typical aquaculture areas in China over four seasons. The aims were to: 1) detect the seasonal variance of multi-elements in salmonid from the two aquaculture areas, and 2) evaluate the effects of seasonal variations on the discrimination of the geographical origins of salmonid based on a multi-element analysis method.

2.2. Reagents A multi-element standard solution in 10% nitric acid was procured from Sigma Aldrich, USA. The HNO3 (65%, Sinopharm Chemical Reagent Co., Ltd.) and H2O2 (30%, Shanghai Shenbo Chemical Co., Ltd.) reagents were of analytical grade quality. Ultrapure water was obtained from an ultrapure water system (Milli-Q Advantage A10, Millipore, Bedford, MA, USA). 2.3. Sample digestion A 0.2 g solid fish or feed sample was weighed into the digestion tank and predigested with 6 mL HNO3 and 2 mL H2O2 for 31 min and diluted to 10 mL with ultrapure water. After predigestion, the digestion tank was put into a microwave digestion system (MWD-650, Metash Instruments Co., Ltd., Shanghai, China). The optimal digestion program was as follows: step 1) 1600 W, ramp time 5 min, temperature, 120 °C, hold time, 10 min; step 2) 1600 W, ramp time 5 min, temperature, 160 °C, hold time, 5 min; step 3) 1600 W, ramp time 5 min, temperature, 200 °C, hold time, 20 min. When the digestion was complete, the digestion tank was put in an acid-driver processor (SPH-2, Metash Instruments Co., Ltd., Shanghai, China) at 170 °C until the residue was less than 1 mL. Finally, the residue was cooled down and diluted in a 25-mL volumetric flask with ultrapure water. The residue was filtrated by a 0.45-μm membrane filter for analysis. Reagent blanks with no added fish or feed samples were digested and prepared in the same manner as those of the samples. Water samples were digested following the methods outlined by Prapaiwong and Boyd (2014). Briefly, a 50-mL preserved water sample was transferred to a 125-mL Erlenmeyer flask and heated on a hot plate, and evaporated until less than 5 mL of the sample was left. Following filtration, the solution was quantified in a 50-mL volumetric flask using ultrapure water. The solution was stored at 4 °C and analyzed by inductively coupled plasma atomic emission spectrometry (ICP-AES) within one week.

2. Materials and methods 2.1. Sample collection Fish were collected from two farms located in the Liujiaxia area (Gansu province, China, 35°54′11″N, 103°18′51″E) and Yantai area (Shandong, province, China, 37°42′33″N, 121°07′49″E) during in four seasons (April, July, and October 2018, and January 2019). Detailed information about sampling are listed in Table 1. The Liujiaxia reservoir is located in the northwest of China (plateau area) and Yantai is located in the northeast of China (coastal area) (Fig. 1). The natural reservoir system (NRS) and recirculating aquaculture system (RAS) were used to culture salmonid in the Liujiaxia and Yantai farms, respectively. Fish were anaesthetized using MS-222 and immediately dissected using a ceramic knife. The fillets were put on dry ice and transported to the laboratory of the Ocean University of China (Qingdao, China) where they were stored at -80 °C until analysis. The frozen fillets were freezedried to constant weight using a freeze dryer (FD-2A, Boyikang Laboratory Instruments Co., Ltd, Beijing, China) and homogenized via grinding. The samples were defatted using petroleum ether (30–60 °C) (Soxhlet Extraction) and stored in a vacuum desiccator until analysis. Water and feed samples were obtained in triplicate when the fish samples were collected. Each water sample (50 mL) was preserved with 1 mL of nitric acid (50% v/v) for further analysis (Li, Boyd, & Odom, 2014). The water quality parameters, temperature and dissolved oxygen, were measured on site using a dissolved oxygen meter (YSI 550A, Fisher Scientific, Hanover Park, Illinois, USA), and pH was

2.4. ICP-AES analysis Samples were analyzed for Ag, Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, Ga, K, Li, Mg, Mn, Na, Ni, Sr, and Zn using ICP-AES (ICAP-6300, Thermo, America). The instrumental conditions of ICP-AES were as follows: radio-frequency power 1150 W, cooling gas flow rate 12 L min−1, nebulizer gas flow rate 1.0 L min−1, auxiliary gas flow rate 0.5 L min−1, nebulizer gas pressure 0.2 MPa, sample introduction flow rate 1.0 mL min−1. The glass cyclonic spray chamber, concentric glass nebulizer, semi-demountable torch with a 1.5-mm bore quartz injector were installed in the machine. The wavelengths selected for the elements are listed in Table 2. To evaluate precision and accuracy, commercial standards were diluted with ultrapure water to provide six concentrations (Ca, Fe, K, Na range from 0.1 to 50 mg·L-1, other elements range from 0.01 to 5 mg·L-1) within the expected ranges of the

Table 1 Sampling information of salmonid. Water environment

Temperature (°C) Dissolved oxygen (mg·L−1) pH Number of samples

Liujiaxia area

Yantai area

Spring

Summer

Autumn

Winter

Spring

Summer

Autumn

Winter

12.5 7.05 8.2 8

20.3 6.96 8.46 5

13.1 6.46 7.7 12

4.6 8.21 8.3 12

13.88 13.19 7.06 12

15.3 12.05 6.85 9

14.1 12.3 6.63 12

13.5 12.52 7.05 12

2

Food Control 109 (2020) 106893

C. Han, et al.

Fig. 1. The location of sampling sites.

2.5. Statistical analysis

Table 2 Validation data of analytical procedure and wavelengths of elements. Elements

LOD (μg·g−1)

LQD (μg·g−1)

Recovery (%)

RSD

R-squared

Wavelengths (nm)

Ag Al Ba Ca Cd Co Cr Cu Fe Ga K Li Mg Mn Na Ni Sr Zn

0.030 0.694 0.039 0.085 0.011 0.024 0.214 0.193 0.125 0.020 2.780 0.377 0.053 0.012 1.307 0.025 0.013 0.010

0.091 2.104 0.118 0.258 0.033 0.073 0.649 0.586 0.380 0.061 8.425 1.141 0.160 0.038 3.960 0.076 0.040 0.031

95.00 91.39 99.04 101.79 97.33 99.40 95.33 96.50 96.19 98.52 103.18 94.61 104.62 97.42 102.15 102.15 98.86 102.53

1.33 0.50 0.94 0.32 0.76 0.34 2.99 0.91 1.37 0.42 1.57 2.08 3.05 0.69 1.23 1.26 0.76 0.57

0.9949 0.9961 0.9983 0.9943 0.9997 0.9990 0.9981 0.9932 0.9999 0.9985 0.9930 0.9964 0.9977 0.9998 0.9974 0.9998 0.9988 0.9993

328.068 396.152 455.403 393.366 226.502 237.862 284.325 324.754 238.204 294.364 766.491 670.784 285.213 257.610 589.592 221.647 407.771 206.200

Statistical analyses of the data were performed with SPSS 19.0 (SPSS, Inc. Chicago, IL). The normality and homogeneity of variances were checked using the Kolmogorov-Smirnov test and Levene's test, respectively. The data was log-transformed for subsequent analyses if it did not fit the assumption. The significant differences of individual elements in different seasons were determined by ANOVA at P < 0.05. If the difference was significant, Tukey (homogeneity) or Tamhane's T2 (inhomogeneity) multiple comparison test were further conducted to compare the difference of each element between any two seasons. The significant difference of the elements between the two areas was determined by Student's t-test. Spearman's correlation analysis was performed to study the relationship between the concentration of elements in fish and the elements in feed and water. Multivariate statistical analysis was performed with SAS 9.4 (SAS Institute, Inc. Cary, North Carolina, USA). Principal component analysis (PCA) and canonical discriminant analysis (CDA) were employed to visualize the potential differences of the elements in fish obtained in different seasons and regions (Jia, Wang, Cao, Li, & Yang, 2018; Li et al., 2019). Linear discriminant analysis (LDA), k nearest neighbor (KNN) and partial least squares discriminant analysis (PLS-DA) were used to create a classification model to discriminate salmonid from different geographic regions. A cross-validation procedure was used to test the validity of these analyses (Hidalgo et al., 2016; Ma et al., 2016; Li et al., 2014).

Note: LOD, Limit of detection; LOQ, Limit of quantification; RSD, average precision for repeatability of solutions.

samples. The standards were analyzed in triplicate. The calibration curves of all elements had good linearity (Table 2, R2 > 0.99). The recovery achieved between the standard concentrations and instrument results for each element and the average precision for the repeatability of solutions (RSD) are listed in Table 2. The ranges of recovery of all elements are 91.37–104.62%. Concentrations of the elements in ten reagent blanks were measured, and the standard deviation was calculated for each element. The limits of detection (LOD) and limits of quantification (LOQ) for each element were calculated as 3.3 and 10 times of the standard deviation, respectively (Table 2) (Bilandžić et al., 2018; Hidalgo et al., 2016). The concentrations (μg·g−1) of all elements sufficiently exceeded the LODs and LOQs.

3. Results and discussion 3.1. The multi-elements concentrations in fish from two areas Concentrations of 18 elements (Ag, Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, Ga, K, Li, Mg, Mn, Na, Ni, Sr, and Zn) were compared between fish samples collected from the two areas (Fig. 2). The mean concentrations of elements K, Ca, Na, and Mg in fish from both areas were > 200 μg g−1. The amounts of the elements Al, Fe, Zn, and Ga were > 20 μg g−1 except for the concentration of Ga in Liujiaxia, which was 13.65 μg g−1. The concentrations of Ag, Cr, Cu, Li, Mn, and Ni 3

Food Control 109 (2020) 106893

C. Han, et al.

Fig. 2. Boxplots of the element concentrations in fish samples collected from Yantai and Liujiaxia (Different lowercase denote significant differences at P < 0.05).

4

Food Control 109 (2020) 106893

C. Han, et al.

were > 2 μg g−1. The concentrations of Ba, Co, and Sr were lower than 2 μg g−1. Previous published studies included concentration ranges for multi-elements in fish (Anderson et al., 2010; Demirezen & Uruc, 2006; Jarapala, Kandlakunta, & Thingnganing, 2014; Mendil et al., 2010), and these were similar to the concentrations of the 18 elements we detected. The box plots display the concentrations of 18 elements in fish from the two areas (Fig. 2). The mean concentrations of 12 elements (Ba, Ca, Cr, Cu, Fe, Ga, K, Li, Mg, Mn, Ni, and Sr) were significantly different (P < 0.05) between the two areas. All 12 elements except Ga were significantly higher in fish from Liujiaxia than from Yantai. The boundaries of the box closest and farthest to zero represent the 25th percentile and 75th percentile, respectively, and indicate that the concentrations of 11 elements (Ba, Ca, Cr, Cu, Fe, K, Li, Mg, Mn, Ni, and Sr) fluctuated more widely in Liujiaxia than in Yantai (Fig. 2). Ga concentrations fluctuated more widely in Yantai than in Liujiaxia.

amounts of 11 elements (Ba, Ca, Cr, Fe, K, Li, Mg, Mn, Na, Ni, and Zn) in fish were significantly different (Table 5, P < 0.05) among the four seasons. The order of concentration of all elements varied greatly during different seasons, except for K, Ca, Na, Mg, and Cd (Table 3). In feed, 10 elements (Al, Ca, Cd, Cr, Cu, Fe, K, Li, Mg and Ni) collected from four seasons had significant difference (Table C, P < 0.05) in Liujiaxia farm. Correlation analysis showed there was a significant positive correlation (Table A: P < 0.05) between fish and feed for elements K, Na, and Sr, with Spearman's correlation coefficients of 0.27, 0.66, and 0.35, respectively. The concentration of Zn in fish was correlated with that in water (Table A, P < 0.05), with a Spearman's correlation coefficient of 0.35. The seasonal variations of elements K, Na, Sr, and Zn in fish may be related to changes in the amounts of multielements in feed and culture water in different seasons. The concentrations of elements Ca, Cr, Fe, Li, Mg and Mn in fish were lower in summer and winter (Table 5), and this change may have been influenced by the climates in different seasons. As shown in Table 1, the water temperatures in winter and summer were 4.6 °C and 20.3 °C, respectively. The optimal temperature range for salmonid growth was 8–18 °C (Liu et al., 2018). Either a lower or higher temperature that exceeded the optimal range would affect the activities of fish, such as feed intake and metabolism (Liu et al., 2018; Shi et al., 2018). Therefore, the lower concentrations of elements in fish during summer and winter may have been caused by decreased feed intake. In summary, the concentrations and compositions of elements in fish collected from the two farms exhibited significant differences, which provides the potential to discriminate the geographical origins of fish from different farms. However, the elemental compositions and concentrations in fish are vulnerable to seasonal effects in Liujiaxia, which may affect the accuracy of discrimination.

3.2. Seasonal variations of multi-elements in fish the Yantai area In Yantai, where a recirculating aquaculture system was used, the concentrations of the elements in fish basically followed the same order during the four seasons (Table 3). Although the order of the concentrations of four elements (Li, Ag, Cu, and Mn) changed slightly, those four elements were similar in concentration. Feed is one of the important sources of elements in fish (Maage, Lygren, & El-Mowafic, 2010; Shiau & Hsieh, 2001), and fluctuant culture water conditions influence fish feeding intake (Liu et al., 2018; Shi, Dong, Zhou, & Gao, 2018). As a controlled system, the water quality parameters such as temperature, dissolved oxygen, pH (Table 1) in the RAS were quite stable and less affected by seasonal climate. This may be one of the reasons for the more stable composition of elements in fish cultured in the RAS. By difference analysis, it was found that only five elements (Fe, K, Mg, Na, and Zn) in fish exhibited significant (P < 0.05) differences in different seasons (Table 4). The mean concentrations of Fe, K, Na, and Mg increased from spring to winter. Bussel et al. (2014) reported that elemental accumulation in the water of recirculating aquaculture systems occurs because there is only a small amount of water replacement in the system. In the current study, Spearman's correlation analysis showed that elemental Na in fish was positively correlated with the element in water (Table A: correlation coefficient = 0.39; P < 0.05). Moreover, feed is another important source of the elements in the bodies of fish (Ahmed, Shaheen, & Islam, 2015; Alam, Mokhtar, Alam, Bari, & Ern, 2015; Jia et al., 2017). The concentrations of elements Al, Ca, Fe, K, Li, Mg, Mn, Na, Sr and Zn in feed collected from the Yantai farm in four seasons had significant difference (Table B, P < 0.05). But only concentration of K in fish was positively correlated with the concentration of K in feed (Table A: correlation coefficient = 0.37; P < 0.05), as determined by Spearman's correlation analysis.

3.4. Principal component analysis (PCA) and canonical discriminant analysis (CDA) PCA has been frequently used as a dimension reduction technology in many studies (Li et al., 2019; Mottese et al., 2018; Choi et al., 2017; Flem et al., 2017; Gumus et al., 2017). In this study, PCA was performed on all 18 elements to reveal the distributions of samples from different areas and seasons. The contribution of the elements to the principal component (PC) was indicated by the value of the eigenvector for each element. Larger positive or negative values indicated that the elements had strong positive or negative weights on the PC. The first five principal components (PCs) with eigenvalues greater than 1.0 explained 66.41% of the total variability (Table D). The first PC explained 35.87% of the total variability. Elements Ag, Al, Ga, and Na had negative effects on the first PC with eigenvectors of −0.023, −0.067, −0.114 and −0.021, respectively. The elements Ca, Cr, Fe, K, Mg, Mn, and Zn had the strongest positive effects on the first PC with an eigenvector of 0.3. The first two PCs were used to draw a two-dimensional scatterplot (Fig. 3). Samples from Liujiaxia were more dispersed, and samples from Yantai were concentrated in the temporal distribution. The distribution overlapped for most of the samples from the two regions. PCA failed to clearly show the regional distribution of samples because the first two

3.3. Seasonal variations of multi-elements in fish from the liujiaxia area For Liujiaxia, where the natural reservoir system was applied, the

Table 3 The order of mean elements contents in fish collected from two areas in four seasons. Regions

Seasons

Order

Liujiaxia

Spring Summer Autumn Winter Spring Summer Autumn Winter

K K K K K K K K

Yantai

> > > > > > > >

Na Na Na Na Na Na Na Na

> > > > > > > >

Mg Mg Mg Mg Mg Mg Mg Mg

> > > > > > > >

Ca Ca Ca Ca Ca Ca Ca Ca

> > > > > > > >

5

Fe Al Fe Fe Fe Fe Fe Fe

> > > > > > > >

Al Fe Al Al Al Al Al Al

> > > > > > > >

Zn Zn Zn Ga Ga Ga Ga Ga

> > > > > > > >

Li > Cr > Ni > Ga > Cu > Ag > Mn > Co > Ba > Sr Ga > Cr > Ni > Cu > Mn > Ag > Ba > Sr > Li > Co Cr > Li > Ga > Ni > Cu > Mn > Ag > Co > Sr > Ba Zn > Cr > Ni > Cu > Li > Ag > Mn > Sr > Co > Ba Zn > Cr > Ni > Li > Ag > Cu > Mn > Co > Ba > Sr Zn > Cr > Ni > Ag > Cu > Li > Mn > Co > Ba > Sr Zn > Cr > Ni > Cu > Ag > Li > Mn > Co > Ba > Sr Zn > Cr > Ni > Cu > Li > Mn > Ag > Co > Ba > Sr

> > > > > > > >

Cd Cd Cd Cd Cd Cd Cd Cd

Food Control 109 (2020) 106893

C. Han, et al.

Table 4 Means and standard deviations for concentrations (μg·g−1 dry wt.) of elements in fish collected from Yantai area in four seasons. Elements Ag Al Ba Ca Cd Co Cr Cu Fe Ga K Li Mg Mn Na Ni Sr Zn

Spring

Summer a

3.96 ± 3.45 89.44 ± 37.33a 0.75 ± 0.65a 363.58 ± 90.00a 0.31 ± 0.16a 0.97 ± 0.84a 10.05 ± 2.70a 3.11 ± 2.72a 90.23 ± 7.00b 24.39 ± 26.72a 16645.33 ± 1992.86b 5.79 ± 5.25a 1049.48 ± 111.62b 2.19 ± 0.74a 2543.28 ± 215.67b 6.97 ± 1.83a 0.59 ± 0.46a 20.89 ± 4.29ab

Autumn a

Winter a

8.06 ± 7.18 68.49 ± 23.41a 0.83 ± 0.85a 374.07 ± 97.45a 0.38 ± 0.18a 1.63 ± 1.67a 10.80 ± 2.44a 3.71 ± 1.87a 93.91 ± 8.08b 41.56 ± 35.11a 15204.12 ± 1010.69b 3.61 ± 5.52a 1128.85 ± 92.76ab 2.72 ± 0.47a 2768.25 ± 523.52ab 6.76 ± 1.36a 0.51 ± 0.37a 24.53 ± 3.99a

3.33 ± 2.70 65.46 ± 32.43a 0.80 ± 0.52a 340.34 ± 42.90a 0.32 ± 0.16a 1.06 ± 0.60a 11.22 ± 2.54a 3.70 ± 3.04a 95.09 ± 5.97b 30.64 ± 37.67a 16130.94 ± 504.16b 2.66 ± 4.13a 1138.72 ± 71.65ab 2.24 ± 0.36a 2804.57 ± 185.58ab 7.05 ± 1.10a 0.73 ± 0.36a 19.62 ± 2.21b

P value a

1.67 ± 2.86 79.22 ± 31.72a 0.72 ± 0.43a 322.50 ± 38.22a 0.34 ± 0.16a 1.57 ± 1.38a 11.29 ± 2.25a 4.54 ± 2.35a 107.37 ± 13.81a 39.67 ± 28.61a 16943.27 ± 1288.39a 3.52 ± 4.55a 1157.52 ± 70.84a 2.48 ± 0.78a 2973.82 ± 282.40a 8.02 ± 1.36a 0.53 ± 0.30a 22.47 ± 3.78ab

0.15 0.28 0.98 0.33 0.74 0.42 0.60 0.60 < 0.01 0.56 0.03 0.46 0.02 0.22 0.02 0.18 0.52 0.02

Note: Values followed by the same lowercase letters were not different (P > 0.05) according to ANOVA and Turkey's or Tamhane's multiple comparison test. Table 5 Means and standard deviations for concentrations (μg·g−1 dry wt.) of elements in fish samples collected from Liujiaxia area in four seasons. Elements Ag Al Ba Ca Cd Co Cr Cu Fe Ga K Li Mg Mn Na Ni Sr Zn

Spring

Summer a

4.12 ± 5.90 76.79 ± 44.78a 2.05 ± 1.13a 740.18 ± 211.14a 0.35 ± 0.19a 2.14 ± 1.44a 17.11 ± 1.50a 5.52 ± 2.36a 145.82 ± 16.74a 5.52 ± 9.52a 26297.62 ± 1488.44a 21.06 ± 8.13ab 1706.76 ± 48.25a 4.05 ± 0.92a 2459.60 ± 303.70a 9.63 ± 2.09a 1.35 ± 0.54a 35.20 ± 9.59a

Autumn a

Winter a

2.63 ± 2.84 109.77 ± 24.10a 1.29 ± 0.72ab 492.19 ± 142.25bc 0.20 ± 0.12a 0.51 ± 0.40a 9.95 ± 2.10b 5.65 ± 4.66a 95.32 ± 12.13b 23.17 ± 34.83a 15793.16 ± 2888.86c 1.15 ± 2.29c 1130.05 ± 111.11b 2.78 ± 0.58ab 1561.92 ± 225.46c 7.07 ± 0.77ab 1.25 ± 0.62a 30.15 ± 8.74ab

2.42 ± 2.89 68.58 ± 30.63a 1.11 ± 0.95ab 601.63 ± 102.06ab 0.51 ± 0.46a 1.46 ± 1.20a 12.58 ± 4.57b 3.45 ± 2.20a 119.04 ± 23.78ab 7.96 ± 13.81a 20587.39 ± 3816.47b 9.14 ± 9.25bc 1377.15 ± 271.01b 2.94 ± 0.36a 2080.99 ± 303.73ab 7.07 ± 2.45b 1.16 ± 0.43a 30.41 ± 5.33a

P value a

2.44 ± 3.31 79.75 ± 30.55a 0.53 ± 0.61b 385.97 ± 131.66c 0.24 ± 0.11a 0.65 ± 0.51a 11.35 ± 3.67b 3.65 ± 2.46a 98.12 ± 23.76b 20.79 ± 23.57a 18686.01 ± 2618.34bc 2.48 ± 3.54c 1099.51 ± 270.25b 2.01 ± 0.64b 2127.04 ± 136.46ab 6.42 ± 1.56b 0.84 ± 0.68a 18.73 ± 4.58b

0.99 0.17 0.01 < 0.01 0.10 0.05 < 0.01 0.22 < 0.01 0.22 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 0.01 0.22 < 0.01

Note: Values followed by the same lowercase letters were not different (P > 0.05) according to ANOVA and Turkey's or Tamhane's multiple comparison test.

PCs only explained 46.39% of the total variability. To optimize group variations and minimize within-group differences, CDA was selected and conducted based on all 18 elements in the fish collected from different seasons and systems. CDA was verified to be an effective technique to visualize the distributions of shrimp cultured in seawater or freshwater, and of farmed or wild caught salmon (Anderson et al., 2010; Li et al., 2019). The default number of canonical variables generated was the minimum of the number of variables and the number of groups minus one, and in this study, seven canonical variables were generated, which explained 100% of the total variability. The characteristics and the statistical significance of the first two canonical variables are described in Table E. The first canonical variable explained 64.21% of the total variability. A higher absolute value of the eigenvector denotes a stronger weight of the parameter on the canonical variables. Positive and negative values respectively indicate that the parameters have positive or negative effects on the canonical variables. The elements Ba, Mn, and Sr had positive effects on the first canonical variable with eigenvectors of 0.404, 0.380, and 0.474, respectively, and Cd had a negative effect on the first canonical variable with an eigenvector of −0.273. The first two canonical variables explained 83.32% of the total variability (Table E) and were used to draw the scatterplot in Fig. 4.

Fig. 3. A 2-dimensional scatter plot of individual scores for fish collected from Liujiaxia and Yantai areas in four seasons based on the two, first principal components.

6

Food Control 109 (2020) 106893

C. Han, et al.

properties of partial least squares regression with a discrimination analysis (Ballabio & Consonni, 2013; Hidalgo et al., 2016). The performance and validity of the PLS were evaluated by root mean square error in calibration (RMSEC), root mean square error in cross-validation (RMSECV) and correlation coefficient (R2) (Albergamo et al., 2018). In the present study, RMSEC, RMSECV, and R2 of the PLS model for the fish samples from Liujiaxia and Yantai were 0.19, 0.25, 0.86 and 0.23, 0.27, 0.78, respectively. Sample grouping was mainly achieved through the first ten latent variables (LVs). In addition, the variable importance in projection (VIP) scores of the PLS-DA models were a measure of elements’ importance in the model (Albergamo et al., 2018). The VIP scores were > 0.8 for Ca (1.32), Cr (0.91), Fe (1.09), Ga (0.81), K (1.28), Mg (1.12), Mn (0.97), Na (2.28), Sr (1.08) and Zn (1.04). The PLS-DA classification model was built up by the 82 samples (total samples), and the original correct classification rate reached 98.78% by resubstitution (Table 6). KNN is a non-parametric linear classification method used to categorize samples according to the majority of their k-neighborhood members in the training set. The optimal efficiency of discrimination depends on parameter k, which is determined by a cross-validation method (Hidalgo et al., 2016; Li et al., 2014). In the present study, the higher correct classification rate was achieved by KNN when k = 7, 98.78% samples were correctly classified (Table 6). In classification models including LDA, KNN, and PLS-DA, resubstitution used all the data to make models, which then classified this same data set to give the original correct classification rate (Li et al., 2016). The cross-validation procedure was further used to evaluate three models built by LDA, KNN, and PLS-DA. Cross-validation is more accurate than resubstitution because each sample is removed from the dataset and tested against the discriminant function created by the remaining samples (Li et al., 2016). The overall correct classification rates for LDA, PLS-DA, and KNN methods were 97.56%, 97.56%, and 98.78%, respectively. These results indicate that the geographical origin of salmonid could be accurately classified based on the multielement analysis while remaining unaffected by seasonal effects.

Fig. 4. A 2-dimensional scatter plot of canonical discriminant analysis (CDA) results for the fish samples from different seasons and areas by the two canonical discriminant functions.

Both the distributions of PCA and CDA indicated that seasonal variations of the sample distributions were more visible in Liujiaxia than in Yantai. Compared to PCA, CDA more distinctly showed the regional distribution of samples. 3.5. Discrimination analysis To verify if the discrimination of fish from the two farms would be influenced by seasonal change, classification models including LDA, KNN, and PLS-DA were built to discriminate fish from the two different farms and to study the seasonal effects on geographic traceability. The model reliability was evaluated by resubstitution. These techniques have been applied in previous research in food products discrimination with great success (Hidalgo et al., 2016; Ma et al., 2016; Li et al., 2014; Ostermeyer, Molkentin, Lehmann, Rehbein, & Walte, 2014). LDA is a linear classification tool, and previous studies have used this method to successfully determine the geographical origins of tea and fruits (Hidalgo et al., 2016; Ma et al., 2016). In the present study, the linear discrimination functions for fish samples from Liujiaxia and Yantai farm were as follows: Liujiaxia farm = 0.19808 Ag + 0.12714 Al - 2.22994 Ba - 0.0009893 Ca + 6.10679 Cd - 1.95609 Co - 0.82715 Cr + 0.78239 Cu + 0.09381 Fe - 0.02742 Ga + 0.0004580 K + 0.08402 Li + 0.01553 Mg - 2.83711 Mn + 0.01590 Na + 0.63187 Ni + 4.34470 Sr + 0.22119 Zn - 41.07526; Yantai farm = 0.42538 Ag + 0.12772 Al - 2.84515 Ba - 0.02161 Ca + 7.16132 Cd - 2.73112 Co - 0.84231 Cr + 0.56734 Cu + 0.10279 Fe 0.01841 Ga - 0.00170 K + 0.30872 Li + 0.02793 Mg - 3.33522 Mn + 0.03160 Na + 0.83342 Ni + 3.02466 Sr + 0.52812 Zn 53.31299. As shown in Table 6, via resubstitution, the original correct classification rate of samples from Liujiaxia reached 100%, and in Yantai, only one sample was incorrectly classified. The overall correct classification rate was 98.78%. PLS-DA is also a linear classification method that combines the

4. Conclusion The element profiling in fish showed a certain degree of seasonal variation, especially in fish cultured in natural reservoir systems. However, all discriminate methods including LDA, KNN, and PLS-DA were effective in discriminating fish samples from different geographical origins while remaining unaffected by seasonal effects. While this pilot study was successful in achieving its aims, there are still limitations with respect to the coverage of sampling points and individual samples. Additional research should be conducted to include a greater number of sampling points, and laboratory dietary shift studies must also be carried out to investigate the effects of feed on traceability before the general use of the method.

Declaration of interest The authors declare that they have no conflict of interests.

Table 6 The discrimination results of different models for the samples collected from two areas. Number of samples

a

LDA PLS-DAb KNNc

Original % Correctly classified

Cross-validated % Correctly classified

Liujiaxia

Yantai

Liujiaxia

Yantai

Total

Liujiaxia

Yantai

Total

37 37 37

45 45 45

100 100 100

97.78 97.78 97.78

98.78 98.78 98.78

100 100 100

95.56 95.56 97.78

97.56 97.56 98.78

Note:a LDA, linear discriminant analysis.

b

PLS-DA, partial least squares discriminant analysis. 7

c

KNN, K Nearest Neighbor, k = 7.

Food Control 109 (2020) 106893

C. Han, et al.

Acknowledgments

Program of Shandong Province (No. 2016CYJS04A01) and the Fundamental Research Funds for the Central Universities (No. 201861028).

This work was supported by the National Key R&D Program of China (No. 2017YFE0122100), Primary Research and Development Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.foodcont.2019.106893. Appendix. A Table A Correlation coefficent between fish and feed, water in Liujiaxia and Yantai farm Correlation coefficent

Liujiaxia

Al Ca Cd Co Cr Cu Fe K Li Mg Mn Na Ni Sr Zn

Yantai

Feed

Water

Feed

Water

−0.12 −0.09 −0.10 0.39** 0.10 −0.32* 0.22 0.27* −0.14 −0.09 −0.10 0.66** −0.07 0.35** −0.26*

−0.11 0.07 −0.03 – −0.01 0.25 −0.42** −0.25 −0.31 0.08 −0.12 −0.34* – 0.10 0.35*

0.32* 0.19 0.12 −0.02 -.35* −0.21 −0.17 0.37* −0.20 0.17 0.03 −0.25 −0.23 −0.16 −0.37*

−0.04 0.04 0.01 – −0.10 −0.03 −0.07 0.25 0.08 −0.29 0.19 0.39** – 0.10 0.05

Note: * means that correlation is significant at 0.05 and ** implies that correlation is significant at 0.01. "-" indicates that the element is not detectable in water.

Table B

Means and standard deviations for concentrations (μg·g−1 dry wt.) of elements in feed collected from Yantai area in four seasons Elements Al Ca Cd Co Cr Cu Fe K Li Mg Mn Na Ni Sr Zn

Spring

Summer a

434.92 ± 17.70 20593.28 ± 308.11a 0.48 ± 0.13a 0.47 ± 1.94a 16.30 ± 3.57a 32.89 ± 4.53a 1003.49 ± 40.90b 7207.66 ± 105.25c 1.48 ± 0.20b 2099.97 ± 53.94c 158.06 ± 5.06a 10775.83 ± 208.64a 4.21 ± 4.51a 130.39 ± 3.20a 149.94 ± 9.92a

Autumn b

Winter c

369.87 ± 17.30 19377.99 ± 118.80b 0.55 ± 0.42a 2.25 ± 1.03a 13.17 ± 1.61a 25.47 ± 2.23a 1156.93 ± 31.43a 6744.36 ± 74.21d 1.34 ± 0.25b 1931.47 ± 14.48d 153.29 ± 0.86a 9483.79 ± 81.23b 4.47 ± 2.20a 112.00 ± 0.78b 144.08 ± 4.43a

314.56 ± 5.63 16906.35 ± 516.77c 0.35 ± 0.11a 5.01 ± 0.64a 11.69 ± 2.93a 28.51 ± 0.97a 755.70 ± 4.34c 10222.63 ± 87.39b 9.17 ± 2.46a 2237.65 ± 14.33b 82.58 ± 0.69b 6569.69 ± 83.34c 7.90 ± 0.02a 57.85 ± 0.92c 148.54 ± 2.38a

P value b

368.55 ± 11.43 13290.36 ± 192.73d 0.38 ± 0.19a 7.31 ± 6.95a 12.79 ± 3.69a 26.29 ± 3.34a 771.39 ± 6.28c 12148.75 ± 170.53a 14.59 ± 3.56a 2514.48 ± 14.50a 90.16 ± 0.78c 9700.48 ± 167.40b 6.70 ± 1.04a 67.51 ± 0.48d 112.77 ± 3.79b

< 0.01 < 0.01 0.74 0.19 0.35 0.07 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 0.30 < 0.01 < 0.01

Note: Values followed by the same lowercase letters were not different (P > 0.05) according to ANOVA and Turkey's or Tamhane's multiple comparison test.

Table C

Means and standard deviations for concentrations (μg·g−1 dry wt.) of elements in feed collected from Liujiaxia area in four seasons Elements

Spring

Summer

Autumn and Winter

P value

Al Ca Cd Co Cr Cu Fe K Li

525.49 ± 7.83a 18048.31 ± 1191.41b 0.14 ± 0.06b 1.84 ± 0.84a 10.89 ± 0.99b 21.85 ± 4.64b 1019.49 ± 8.03b 8266.27 ± 362.25b 2.34 ± 0.28b

339.13 ± 1.99c 20219.69 ± 197.69a 1.04 ± 0.31a 4.85 ± 1.41a 10.13 ± 0.78b 27.70 ± 2.23a 815.67 ± 16.55ab 6795.08 ± 42.28c 1.47 ± 0.19b

449.02 ± 13.36b 15724.99 ± 1033.64c 0.32 ± 0.08b 3.65 ± 3.44a 16.35 ± 4.25a 29.55 ± 2.17a 934.11 ± 41.19a 13967.87 ± 287.90a 11.94 ± 5.78a

< 0.01 < 0.01 < 0.01 0.11 0.01 0.03 < 0.01 < 0.01 < 0.01

(continued on next page) 8

Food Control 109 (2020) 106893

C. Han, et al.

Table C (continued) Elements Mg Mn Na Ni Sr Zn

Spring

Summer b

b

1987.00 ± 8.94 149.33 ± 1.13a 10091.97 ± 33.05a 3.22 ± 1.87b 128.21 ± 1.55a 143.46 ± 2.73a

2102.33 ± 175.76 110.64 ± 47.63a 10093.74 ± 1642.47a 2.96 ± 1.38b 90.07 ± 41.75a 163.87 ± 28.45a

Autumn and Winter

P value

a

< 0.01 0.11 0.34 < 0.01 0.26 0.32

2983.73 ± 143.21 80.82 ± 4.32a 11390.00 ± 483.06a 7.46 ± 0.70a 94.65 ± 7.01a 144.39 ± 4.35a

Note: The feed used in autumn and winter were same. Values followed by the same lowercase letters were not different (P > 0.05) according to ANOVA and Turkey's or Tamhane's multiple comparison test.

Table D Main parameters of the three determined principal components Principal component

Eigenvalue

Explained variance (%)

Cumulative explained variance (%)

1 2 3 4 5

6.46 1.90 1.33 1.24 1.03

35.87 10.53 7.36 6.90 5.75

35.87 46.39 53.76 60.66 66.41

Table E Statistical parameters of the canonical discrimination function Canonical variables

Eigenvalue

Explained variance (%)

Total variance (%)

Canonical correlation

P value

1 2

11.32 3.37

64.21 19.11

64.21 83.32

0.9585 0.8781

< 0.0001 < 0.0001

(RAS) changes body composition but not performance and health of juvenile turbot (Psetta maxima). Aquacultural Engineering, 61, 35–42. https://doi.org/10.1016/j. aquaeng.2014.05.003. Chaguri, M. P., Maulvault, A. L., Nunes, M. L., Santiago, D. A., Denadai, J. C., Fogaça, F. H., et al. (2015). Different tools to trace geographic origin and seasonality of croaker (Micropogonias furnieri). Lebensmittel-Wissenschaft und -Technologie- Food Science and Technology, 61, 194–200. https://doi.org/10.1016/j.lwt.2014.11.006. Choi, Y. H., Hong, C. K., Kim, M., Sun, O. J., Park, J., Oh, Y. H., et al. (2017). Multivariate analysis to discriminate the origin of sesame seeds by multi-element analysis inductively coupled plasma-mass spectrometry. Food Science & Biotechnology, 26, 375–379. https://doi.org/10.1007/s10068-017-0051-0. Demirezen, D., & Uruc, K. (2006). Comparative study of trace elements in certain fish, meat and meat products. Meat Science, 74(2), 255–260. https://doi.org/10.1016/j. meatsci.2006.03.012. Drivelos, S. A., Danezis, G. P., Haroutounian, S. A., & Georgiou, C. A. (2016). Rare earth elements minimal harvest year variation facilitates robust geographical origin discrimination: The case of PDO “fava santorinis”. Food Chemistry, 238–245. https://doi. org/10.1016/j.foodchem.2016.06.088. Fallah, A. A., Zeynali, F., Saei-Dehkordi, S. S., Rahnama, M., & Jafari, T. (2011). Seasonal bioaccumulation of toxic trace elements in economically important fish species from the Caspian Sea using GFAAS. Journal of Consumer Protection & Food Safety, 6(3), 367–374. https://doi.org/10.1016/j.microc.2011.02.007. Flem, B., Moen, V., Finne, T. E., Viljugrein, H., & Kristoffersen, A. B. (2017). Trace element composition of smolt scales from Atlantic salmon (Salmo salar L.), geographic variation between hatcheries. Fisheries Research, 190, 183–196. https://doi.org/10. 1016/j.fishres.2017.02.01. Fu, J., Hu, X., Tao, X., Yu, H., & Zhang, X. (2013). Risk and toxicity assessments of heavy metals in sediments and fishes from the Yangtze River and Taihu Lake, China. Chemosphere, 93, 1887–1895. https://doi.org/10.1016/j.chemosphere.2013.06.061. Gumus, Z. P., Celenk, V. U., Tekin, S., Yurdakul, O., & Ertas, H. (2017). Determination of trace elements and stable carbon isotope ratios in virgin olive oils from Western Turkey to authenticate geographical origin with a chemometric approach. European Food Research and Technology, 243, 1719–1727. https://doi.org/10.1007/s00217017-2876-4. Hidalgo, M. J., Fechner, D. C., Marchevsky, E. J., & Pellerano, R. G. (2016). Determining the geographical origin of Sechium edule fruits by multielement analysis and advanced chemometric techniques. Food Chemistry, 210, 228–234. https://doi.org/10.1016/j. foodchem.2016.04.120. Jarapala, S. R., Kandlakunta, B., & Thingnganing, L. (2014). Evaluation of trace metal content by ICP-MS using closed vessel microwave digestion in fresh water fish. J Environ Public Health, 1–8. 201506 https://doi.org/10.1155/2014/201506. Jia, Y., Wang, L., Cao, J., Li, S., & Yang, Z. (2018). Trace elements in four freshwater fish from a mine-impacted river: Spatial distribution, species-specific accumulation, and

References Addisu Demeke Teklemariam, F. T., & Takele, A. (2015). Review on evaluation of safety of fish and fish products. International Journal of Fisheries and Aquatic Studies, 3(2), 111–117. Ahmed, M. K., Shaheen, N., Islam, M. S., Habibullah-Al-Mamun, M., Islam, S., Mohiduzzaman, M., et al. (2015). Dietary intake of trace elements from highly consumed cultured fish (Labeo rohita, Pangasius pangasius and Oreochromis mossambicus) and human health risk implications in Bangladesh. Chemosphere, 128, 284–292. https://doi.org/10.1016/j.chemosphere.2015.02.016. Alamdar, A., Hanif, N., Ali, S. M., Fasola, M., Bokhari, H., Katsoyiannis, I. A., et al. (2016). Human exposure to trace metals and arsenic via consumption of fish from river Chenab, Pakistan and associated health risks. Chemosphere, 168, 1004–1012. https:// doi.org/10.1016/j.chemosphere.2016.10.110. Alam, L., Mokhtar, M. B., Alam, M. M., Bari, M. A., & Ern, L. K. (2015). Seasonal variation and preliminary risk assessment of trace element pollution in surface water from langat river, Malaysia. International Journal of Applied Environmental Sciences, 10(1), 19–40. Albergamo, A., Mottese, A. F., Bua, G. D., Caridi, F., Sabatino, G., Barrega, L., et al. (2018). Discrimination of the Sicilian prickly pear (opuntia ficus-indica L., CV. Muscaredda) according to the provenance by testing unsupervised and supervised chemometrics. Journal of Food Science, 83, 2933–2942. https://doi.org/10.1111/ 1750-3841.14382. Anderson, K. A., Hobbie, K. A., & Smith, B. W. (2010). Chemical profiling with modeling differentiates wild and farm-raised salmon. Journal of Agricultural and Food Chemistry, 58, 11768–11774. https://doi.org/10.1021/jf102046b. Bahar, B., Schmidt, O., Moloney, A. P., Scrimgeour, C. M., Begley, I. S., & Monahan, F. J. (2008). Seasonal variation in the C, N and S stable isotope composition of retail organic and conventional Irish beef. Food Chemistry, 106(3), 1299–1305. https://doi. org/10.1016/j.foodchem.2007.07.053. Ballabio, D., & Consonni, V. (2013). Classification tools in chemistry. Part 1: Linear models. PLS-DA. Analytical Methods, 5, 3790–3798. https://doi.org/10.1039/ c3ay40582f. Bechtel, P. J., & Oliveira, A. C. M. (2010). Chemical characterization of liver lipid and protein from cold water fish species. Journal of Food Science, 71, 480–485. https:// doi.org/10.1111/j.1750-3841.2006.00076. Bilandžić, N., Sedak, M., Čalopek, B., Đokić, M., Varenina, I., Kolanović, B. S., et al. (2018). Element contents in commercial fish species from the Croatian market. Journal of Food Composition and Analysis, 71, 77–86. https://doi.org/10.1016/j.jfca. 2018.02.014. Bussel, C. G. J. V., Schroeder, J. P., Mahlmann, L., & Schulz, C. (2014). Aquatic accumulation of dietary metals (Fe, Zn, Cu, Co, Mn) in recirculating aquaculture systems

9

Food Control 109 (2020) 106893

C. Han, et al.

023. Mottese, A. F., Albergamo, A., Bartolomeo, G., Bua, G. D., Rando, R., Pasquale, P. D., et al. (2018). Evaluation of fatty acids and inorganic elements by multivariate statistics for the traceability of the Sicilian Capparis spinosa L. Journal of Food Composition and Analysis, 72, 66–74. https://doi.org/10.1016/j.jfca.2018.05.009. Naccarato, A., Furia, E., Sindona, G., & Tagarelli, A. (2016). Multivariate class modeling techniques applied to multielement analysis for the verification of the geographical origin of chili pepper. Food Chemistry, 206, 217–222. https://doi.org/10.1016/j. foodchem.2016.03.072. Ostermeyer, U., Molkentin, J., Lehmann, I., Rehbein, H., & Walte, H. G. (2014). Suitability of instrumental analysis for the discrimination between wild-caught and conventionally and organically farmed shrimps. European Food Research and Technology, 239, 1015–1029. https://doi.org/10.1007/s00217-014-2298-5. Prapaiwong, N., & Boyd, C. E. (2014). Trace elements in waters of inland, low-salinity shrimp ponds in Alabama. Aquaculture Research, 45, 327–333. https://doi.org/10. 1111/j.1365-2109.2012.03230.x. Sant'Ana, L. S., Ducatti, C., & Ramires, D. G. (2010). Seasonal variations in chemical composition and stable isotopes of farmed and wild Brazilian freshwater fish. Food Chemistry, 122, 74–77. https://doi.org/10.1016/j.foodchem.2010.02.016. Sarma, D., Akhtar, M. S., Das, P., Shahi, N., Ciji, A., Mahanta, P. C., et al. (2013). Nutritional quality in terms of amino acid and fatty acid of five coldwater fish species: Implications to human health. National Academy Science Letters, 36, 385–391. https:// doi.org/10.1007/s40009-013-0151-1. Sheikha, A. F. E., & Xu, J. (2017). Traceability as a key of seafood safety: Reassessment and possible applications. Reviews in Fisheries Science, 25, 158–170. https://doi.org/ 10.1080/23308249.2016.1254158. Shen, Y., Kang, J., Chen, W., & He, S. (2016). DNA barcoding for the identification of common economic aquatic products in Central China and its application for the supervision of the market trade. Food Control, 61, 79–91. https://doi.org/10.1016/j. foodcont.2015.08.038. Shiau, S. Y., & Hsieh, J. F. (2001). Quantifying the dietary potassium requirement of juvenile hybrid tilapia (Oreochromis niloticus×O. aureus). British Journal of Nutrition, 85, 213–218. https://doi.org/10.1079/BJN2000245. Shi, K. P., Dong, S. L., Zhou, Y. G., & Gao, Q. F. (2018). Comparative evaluation of toleration to heating and hypoxia of three kinds of salmonids. Journal of Ocean University of China, 6, 1465–1472. https://doi.org/10.1007/s11802-018-3673-9. Soomro, A., Siyal, A. A., Mirjat, M. S., & Sial, N. B. (2014). Seasonal variations of trace elements and heavy metal concentrations in phuleli canal water (sindh), Pakistan. Sarhad Journal of Agriculture, 30, 73–82. Squadrone, S., Prearo, M., Brizio, P., Gavinelli, S., Pellegrino, M., Scanzio, T., et al. (2013). Heavy metals distribution in muscle, liver, kidney and gill of European catfish (Silurus glanis) from Italian Rivers. Chemosphere, 90, 358–365. https://doi.org/ 10.1016/j.chemosphere.2012.07.028. Zhang, S. Y., Li, G., Wu, H. B., Liu, X. G., Yao, Y. H., Tao, L., et al. (2011). An integrated recirculating aquaculture system (RAS) for land-based fish farming: The effects on water quality and fish production. Aquacultural Engineering, 45(3), 93–102. https:// doi.org/10.1016/j.aquaeng.2011.08.001.

risk assessment. Environmental Science & Pollution Research, 25, 1–10. https://doi.org/ 10.1007/s11356-018-1207-z. Jia, Y., Wang, L., Qu, Z., Wang, C., & Yang, Z. (2017). Effects on heavy metal accumulation in freshwater fishes: Species, tissues, and sizes. Environmental Science & Pollution Research, 24, 1–8. https://doi.org/10.1007/s11356-017-8606-4. Joebstl, D., Bandoniene, D., Meisel, T., & Chatzistathis, S. (2010). Identification of the geographical origin of pumpkin seed oil by the use of rare earth elements and discriminant analysis. Food Chemistry, 123, 1303–1309. https://doi.org/10.1016/j. foodchem.2010.06.009. Kukusamude, C., & Kongsri, S. (2018). Elemental and isotopic profiling of Thai jasmine rice (Khao Dawk Mali 105) for discrimination of geographical origins in Thung Kula Rong Hai area, Thailand. Food Control, 91, 357–364. https://doi.org/10.1016/j. foodcont.2018.04.018. Leal, M. C., Pimentel, T., Ricardo, F., Rosa, R., & Calado, R. (2015). Seafood traceability: Current needs, available tools, and biotechnological challenges for origin certification. Trends in Biotechnology, 33, 331–336. https://doi.org/10.1016/j.tibtech.2015. 03.003. Li, L., Boyd, C. E., & Odom, J. (2014). Identification of Pacific white shrimp (Litopenaeus vannamei) to rearing location using elemental profiling. Food Control, 45, 70–75. https://doi.org/10.1016/j.foodcont.2014.03.013. Li, L., Boyd, C. E., & Sun, Z. (2016). Authentication of fishery and aquaculture products by multi-element and stable isotope analysis. Food Chemistry, 194, 1238–1244. https:// doi.org/10.1016/j.foodchem.2015.08.123. Li, L., Han, C., Dong, S. L., & Boyd, C. E. (2019). Use of elemental profiling and isotopic signatures to differentiate Pacific white shrimp (Litopenaeus vannamei) from freshwater and seawater culture areas. Food Control, 95, 249–256. https://doi.org/10. 1016/j.foodcont.2018.08.015. Liu, C. Y., Zhou, Y. G., Kang, D., Sun, D. J., Gao, Q. F., & Dong, S. L. (2018). Differences in fatty acid composition of gill and liver phospholipids between Steelhead trout (Oncorhynchus mykiss) and Atlantic salmon (Salmo salar) under declining temperatures. Aquaculture, 495, 815–822. https://doi.org/10.1016/j.aquaculture.2018.06. 045. Maage, A., Lygren, B., & El-Mowafic, A. F. A. (2010). Manganese requirement of Atlantic salmon (Salmo salar) fry. Fisheries Science, 66(1), 1–8. https://doi.org/10.1046/j. 1444-2906.2000.00001.x. Maietti, A., Tedeschi, P., Stagno, C., Bordiga, M., Travaglia, F., Locatelli, M., et al. (2012). Analytical traceability of melon (cucumis melo var reticulatus): Proximate composition, bioactive compounds, and antioxidant capacity in relation to cultivar, plant physiology state, and seasonal variability. Journal of Food Science, 77(6)https://doi. org/10.1111/j.1750-3841.2012.02712.x. Ma, G., Zhang, Y., Zhang, J., Wang, G., Chen, L., Zhang, M., et al. (2016). Determining the geographical origin of Chinese green tea by linear discriminant analysis of trace metals and rare earth elements: Taking Dongting Biluochun as an example. Food Control, 59, 714–720. https://doi.org/10.1016/j.foodcont.2015.06.037. Mendil, D., Demirci, Z., Tuzen, M., & Soylak, M. (2010). Seasonal investigation of trace element contents in commercially valuable fish species from the Black sea, Turkey. Food and Chemical Toxicology, 48, 865–870. https://doi.org/10.1016/j.fct.2009.12.

10