Toward the Authentication of European Sea Bass ... - ACS Publications

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Towards the authentication of European sea bass origin through a combination of biometric measurements and multiple analytical techniques Federica Farabegoli, Maurizio Pirini, Magda Rotolo, Marina Silvi, Silvia Testi, Sergio Ghidini, Emanuela Zanardi, Daniel Remondini, Alessio Bonaldo, Luca Parma, and Anna Badiani J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b00505 • Publication Date (Web): 08 Jun 2018 Downloaded from http://pubs.acs.org on June 8, 2018

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Journal of Agricultural and Food Chemistry

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Towards the authentication of European sea bass origin through a combination of biometric

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measurements and multiple analytical techniques

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Federica FARABEGOLIa*, Maurizio PIRINIa, Magda ROTOLOa, Marina SILVIa, Silvia TESTIa,

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Sergio GHIDINIb, Emanuela ZANARDIb, Daniel REMONDINIc#, Alessio BONALDOa, Luca

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PARMAa, Anna BADIANIa.

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a

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Sopra 50, 40064 Ozzano dell’Emilia (BO), Italy.

Department of Veterinary Medical Science (DIMEVET), University of Bologna, Via Tolara di

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b

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c

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40127 Bologna (BO), Italy.

Department of Food Science, University of Parma, Via del Taglio 10, 43126 Parma (PR), Italy.

Department of Physics and Astronomy (DIFA), University of Bologna, Viale Berti Pichat 6/2,

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*

Corresponding author.

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E-mail address: [email protected]

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Postal address: Department of Veterinary Medical Science, Via Tolara di Sopra 50, 40064

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Ozzano dell’Emilia (BO), Italy Tel: (+34) 986 469 373

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#

Co-corresponding author.

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Email address: [email protected]

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Tel: (+39) 051 20 9 5127

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1 ACS Paragon Plus Environment


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Abstract:

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The authenticity of fish products has become an imperative issue for authorities involved in the

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protection of consumers against fraudulent practices and in the market stabilization. The present

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study aimed to provide a method for authentication of European sea bass (Dicentrarchus labrax)

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according to the requirements for seafood labels (Regulation 1379/2013/EU).

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Data on biometric traits, fatty acid profile, elemental composition, and isotopic abundance of

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wild and reared (intensively, semi-intensively and extensively) specimens from 18 Southern

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European sources (n = 160) were collected and clustered in 6 sets of parameters, then subjected to

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multivariate analysis.

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Correct allocations of subjects according to their production method, origin and stocking density

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were demonstrated with good approximation rates (94%, 92% and 92%, respectively) using fatty

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acid profiles. Less satisfying results were obtained using isotopic abundance, biometric traits, and

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elemental composition. The multivariate analysis also revealed that extensively reared subjects

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cannot be analytically discriminated from wild ones.

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Keywords: Seafood labeling, Dicentrarchus labrax, sea bass authentication, analytical fingerprinting, stocking density, fatty acid profile, isotope analysis, elemental composition.

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1 Introduction

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The demand for sea products for human consumption is in continuous expansion, with

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aquaculture gaining importance in compensating for the deficit of global capture fisheries 1; as a

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result, an increasing number of new farmed species have entered the marketplace over the past

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decade. Public interest in food quality and origin, in particular the awareness of health benefits

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derived from fish consumption, has strongly increased in recent years. Differentiation of seafood

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products may influence consumer preferences, especially concerning discrimination between wild

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and farmed fish. Actually, consumer choice between wild and reared products seems to be strongly

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affected by beliefs resulting from stereotypes; for instance, cultured fish are usually associated with

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the use of antibiotics and growth promoters, while wild products are considered tastier and healthier

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than reared fish 2. Thus, regulatory interventions aim to avoid mislabeling, or substituting wild fish

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with farmed specimens, and to mitigate risks to consumer confidence and health.

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In the European context, due to the variety of sources of fish products (from seawater or

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freshwater; from intensive, semi-intensive, or extensive rearing), Regulation (EU) No 1379/2013

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3

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traceability of fish products. Since wild fish are generally put on the market at a higher price,

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there may be a temptation to falsely label farmed products to sell them at a better price. The

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removal of specific external traits during product processing makes the identification of fish

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species difficult, and the risk of fraudulent substitution of sought-after species with less valuable

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ones grows. Accidental or intentional mislabeling may also occur due to the global production of

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seafood, which allows the supply of the same products from different areas 4. Moreover, strong

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competition among the main producing countries in the Mediterranean region (Italy, Greece,

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Spain, and Turkey, above all) demands detailed characterization and differentiation of sea bass

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quality5. Verifying the authenticity of fish products is therefore imperative, in order to check

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correspondence with the label, establish the real commercial value, avoid unfair competition, and

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ensure consumer protection against fraudulent practices.

was issued to give consumers clear and accurate information on the main features and



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European sea bass (Dicentrarchus labrax) represents one of the main aquaculture fish products

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in the European Union 6. European sea bass is a demersal opportunistic species, inhabiting coastal

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waters (down to about 100 m depth), and shallow waters like estuaries, lagoons, and tidal flats.

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European sea bass is intensively farmed in floating cages or in inshore ponds, and reared semi-

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intensively or extensively in brackish lagoons 7. The different rearing systems and feeding regimes

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in sea bass farming may affect flesh quality, especially in terms of fat level and composition 8,

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which necessarily influences the quality of the product

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estuarine waters, which offer dietary fatty acids (FAs) from the fauna of those areas, whereas

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commercial feeds contain quite different FAs, typically fish products obtained from the open oceans

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4

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nitrogen (N); the former varies according to the nature of the dietary C sources as well, while the

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latter depends on the trophic position of the subjects’ diet 11.

9,10

. Wild sea bass feed in inshore and

. The lipid content of the flesh also influences the ratios of the stable isotopes of carbon (C) and

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According to some authors, the employment of chemometric techniques, in support to

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documental routine, would be a useful tool to improve transparency and consumers’ trust in the

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food trade. To date, the most promising approach to characterize a fish product seems to be the

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application of different analytical techniques on the same matrix, followed by multivariate analysis

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of the data obtained 12. The multivariate analysis applied to the FA profiles, obtained using gas

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chromatography

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(NIRS)

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geographical origin. Other marine species have also been authenticated by employing stable

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isotope analysis

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assess origin, production method, and even characterize the species. Biometric parameters are

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often used to authenticate sea bass type of production, since they are the easiest, quickest and

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cheapest techniques and do not require laborious or expensive equipment, or expert knowledge

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or specialization 12. Elemental composition of fish flesh is generally used as a chemical signature

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of the particular water body where the subject has grown, since marine fish incorporate different

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4,10,12,13

, nuclear magnetic resonance (NMR)

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or near-infrared spectroscopy

, has been previously used to authenticate wild and farmed sea bass, as well as the

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or by coupling the stable isotope ratio with multi-element analyses


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, to

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trace elements, from the environment and the diet, into their skeletal tissues and organs 18. The

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variation on elemental compositions has been also investigated in otoliths of different fish

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species, for distinguishing wild from farmed environments; however, divergent results were

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obtained due to an inter-annual variability in elemental composition of otoliths which

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confounded the origin determinations of subjects

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. The use of molecular genetic markers is

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considered by Brown et al. (2015) the most suitable and informative tool for discrimination of

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wild and farmed fish, since they are not influenced by the age of the subject 20. Unfortunately,

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these techniques are among the most expensive and time-consuming currently available, making

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them unavailable to many sectors 19. Previous studies have assessed the capacity of different analytical techniques to distinguish wild from

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, and to determine their geographical origin 4, although these preliminary

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farmed sea bass

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investigations examined a limited number of sources and/or subjects, sampled in a limited time

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span. The findings of these studies highlighted the difficulty in characterizing the farmed subjects

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10

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samples from various geographical locations 4, considering the inter-seasonal variability for

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improving the robustness of the models 10.

, suggesting the need to introduce new variables and to apply this method on a wide range of fish

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Prompted by all these considerations, the present study aimed to provide a method for

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authentication of the origin of European sea bass specimens by combining data from a number of

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chemical analyses. Being one of the main European aquaculture products, European sea bass

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(Dicentrarchus labrax) was chosen due to the availability of multiple product typologies in the

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Mediterranean market. For the first time, a vast number of samples have been analyzed, from

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sources properly scattered during a one-year sampling among the southern European area, from

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both aquaculture and fisheries sectors; moreover, a “stocking density factor” has been introduced

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as a variable in the multivariate statistical analysis.

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2 Materials and Methods 5 ACS Paragon Plus Environment


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2.1 Sampling design

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Wild and farmed European sea bass subjects were sampled from 18 different Italian and foreign

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sources. Batches of 10 specimens were ordered from every source, equally split into two

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“macroseasons” (autumn-winter, AW; spring-summer, SS) to guarantee a perfect balance (equal

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number of specimens from each macroseason for each source). The geographic distribution for each

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sampling site is reported in Fig. 1: wild subjects (n = 45) came from four main areas, three in the

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Mediterranean Sea and one close to the French Atlantic coast. Intensively reared subjects (IR, n =

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85) were collected from fish farms equipped with either floating or submersible cages; in two cases,

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sea bass belonged to semi-intensive farms (SIR, n = 20), and in a single case they came from an

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extensive farm (ER, n = 10). Supplementary material Table S1 reports the respective FAO fishing

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area, subarea and division for each fishing site, the country of origin, and the locality of sea bass

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farms, as required by Regulation (EU) No 1379/2013, equipment features, and stocking density for

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breeding. Since the studies of Xiccato et al. (2004)5 and Carbonara et al. (2015)21 suggested that

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stocking density in production influences fish composition, an additional categorical variable was

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used, taking one of the following labels: “0” for wild specimens, “1” for extensively farmed

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specimens (up to 0.0025 kg m−3), “2” for semi-intensively farmed specimens (up to 1 kg m−3), and

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“3” for intensively farmed specimens (up to 30 kg m−3). Stocking density of production was

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recently recognized as a potential chronic stress factor in several species of fish; high density of

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rearing can adversely affect the quality of the farmed product, due to a negative effect on fish

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growth rate and on survival and feeding rates

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was based on the statements of individual farmers, through the compilation of a special form

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attached to every consignment.

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. Source allocation according to stocking density

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2.2 Biometric traits and sample preparation Each of 10 subjects from every batch was analyzed following the US EPA procedure for fish sampling

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. Every subject, after measurement of weight and length, was eviscerated, and data on 6 ACS Paragon Plus Environment

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the weight of eviscerated fish, viscera, liver, gonads (if any), and perivisceral fat were recorded.

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Wild and reared subjects, both national and foreign, were purchased from large-scale retail trade;

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and they were of commercial size. Fillets from each subject were homogenized by means of a food

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processor (Multiquick System ZK 100, Braun, Kronbergim Taunus, Germany) and quartered by

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hand twice; thereafter, samples were put into plastic bags and compacted, then frozen and stored at

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−20 °C. The complete description of the procedure is reported in section 2.2 of supporting

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information on Materials and Methods.

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2.3 Moisture determination, lipid analysis and fatty acid profile

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The moisture content of flesh was measured following AOAC procedure 950.46B 25, according

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to which 10 g of homogenized flesh were put into a porcelain crucible and heated at 100–102 °C

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during 18 h to determine dry weight before calculating the moisture.

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The extraction of total lipids (TL) was done in duplicate for each sample, following a procedure

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proposed by Bligh and Dyer (1959) 26, and adopting some adjustments, briefly described as follows:

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around 4 g of homogenized fillets were put into a tube immersed in an ice bath, and mixed with 18

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mL of a solution of chloroform and methanol 1:1 for 1 min using an Ultra-Turrax T25 (IKA-Werke

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GmbH & Co., Staufen, Germany); then 12 mL of a solution of chloroform and deionized water 1:1

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were added to the sample and mixed. After centrifugation (4000 rpm at 4 °C for 10 min), the lower

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phase containing chloroform and dissolved TL, was separated and passed through a layer of

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anhydrous sodium sulfate. One milliliter of the lipid extract was completely desolvated on a hot

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plate at 50 °C, the lipid residue was weighed and the percentage of TL was calculated.

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Quantification of phospholipids (PL) in TL was carried out by colorimetric determination of

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phosphorus, following the procedure reported by Marinetti (1962) 27. Briefly, 5 µg of TL extracted

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sample was mineralized with perchloric acid at 250 °C. Afterwards, ammonium heptamolybdate

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and 1-amino-2-hydroxy-4-naphthalene sulfonic acid were added to the samples; they were kept at

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100 °C for 7 min, till a colored complex was obtained. The absorbance of this complex was 7 ACS Paragon Plus Environment


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measured at 830 nm to estimate the amount of phosphorus in the samples; PL quantification was

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obtained by multiplying the calculated amount of phosphorus by 25.

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Neutral lipids (NL) were separated from PL following a normal phase SPE (solid phase

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extraction) method, described by Bayır et al. (2010) 28, which uses 12 mL Strata SI-1 Silica (55 µm,

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70 Å) columns, with 2 g of substrate (Phenomenex, Torrance, CA, USA). Briefly, after

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equilibration with 3 mL of chloroform, silica columns were loaded with TL sample, and diluted in 3

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mL of chloroform. After that, the NL fraction was separated by charging the cartridge with 3 mL of

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chloroform eight times. After vacuum drying of the column, PL were eluted with four aliquots of 3

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mL of methanol and then four aliquots of 3 mL of a solution of chloroform/methanol 3 : 7 (v : v).

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Fatty acid methyl esters (FAME) from TL, NL and PL were obtained by transmethylation 29

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involving sulfuric acid as catalyst, following the method proposed by Christie (1989)

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samples were dried under a gentle nitrogen stream and diluted with 100 µL of toluene and 1 mL of

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the methylating solution of 1% sulfuric acid in methanol (96% purity). Samples were kept at 50 °C

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in a heater for 12 h. Then, 1 mL of 5% NaCl buffer and 900 µL of hexane were added to the

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samples, which were successively vortex-mixed and centrifuged for 10 min at 2000 rpm. The

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supernatant containing FAME was then collected and injected into a Varian 3380 gas

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chromatograph (Agilent Technologies, Palo Alto, USA) fitted with a CP-8200 Varian autosampler,

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a split injector set at 230 °C, and a flame ionization detector system set at 300 °C. Chromatographic

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separation of FAME was attained by means of a 30 m × 0.32 mm (i.d.) × 0.25 µm (film thickness)

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fused silica-bonded phase column (DB-23, J&W Scientific); the oven temperature was programmed

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from 150 to 230 °C at a rate of 5 °C min−1, with a final isotherm. FAs were identified by comparing

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the retention times of unknown FAME with those of known FAME standard mixtures, their content

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being reported as a percentage of the sum of FAME (% FAME) of TL, NL and PL, respectively.

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The complete description of the procedures for moisture determination, lipid analysis and fatty acid

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profile is reported in section 2.3 of supporting information on Materials and Methods.

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. Briefly,

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2.4 Determination of macro-, microelements and toxic elements

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Mineralization was performed by adding 6 mL of nitric acid (Ultrapure Merck, Darmstadt,

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Germany, 67% purity) and 2 mL of hydrogen peroxide (Ultrapure Merck, 31% purity) to 1 g of

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sample, through a MULTIWAVE 3000 microwave system (Perkin Elmer). Detection of the

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elements was obtained by means of an Optima 2100 inductively coupled plasma atomic emission

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spectrometer (ICP-OES; PerkinElmer, Waltham, MA, USA). For determination of the

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macroelements a Meinhard cyclonic spray chamber was employed, with radial viewing

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configuration. Microelements and toxic elements were determined with a CETAC U5000 ultrasonic

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nebulizer (Thermo Fisher Scientific, Waltham, MA, USA) in axial view configuration. The

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complete description of the procedure is reported in section 2.4 of supporting information on

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Materials and Methods.

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2.5 Stable isotopes analysis

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Determination of C and N isotopic composition in sea bass muscle was carried out through the

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procedure described below: 0.07 ± 0.01 and 0.7 ± 0.1 mg, respectively for δ13C and δ15N analysis,

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of freeze dried fillets (< 0.6 mm grain size), were analyzed by means of an EA/NA-1100 elemental

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analyzer with CHN configuration (Thermo Finnigan), in helium continuous flow mode and coupled

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to a Finnigan Delta Plus XP mass spectrometer. Sample isotope ratios were then calculated

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according to international reference standard V-PDB for C, and atmospheric nitrogen for N. The

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complete description of the procedure is reported in section 2.5 of supporting information on

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Materials and Methods.

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2.6 Quality of analytical data

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Analyses of samples were performed in duplicate and data were constantly monitored by means

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of standard reference materials (SRMs). During analysis, each SRM was processed in duplicate

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three times, following the same procedures. The accuracy and precision of the measurements, 9 ACS Paragon Plus Environment


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calculated on the basis of repeated analysis of SRMs, was ± 0.2‰. The descriptions of the SMRs

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used are reported in section 2.6 of supporting information on Materials and Methods.

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2.7 Data processing and statistical analysis

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MATLAB software was used for data processing, PCA analysis and for sample classification

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through Discriminant Analysis (MATLAB “classify” function embedded into ad-hoc built scripts to

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apply crossvalidation procedures and to estimate classification performance).

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Every source was characterized using biometric parameters and compositional characteristics of

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the entire sea bass fillets (as edible part), grouped into the following sets of variables: biometric

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parameters, FA composition of TL, FA composition of NL, FA composition of PL, elemental

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composition, and isotopic abundances (Table 1). The whole set of measures, provided by each

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analytical technique, was statistically analyzed in order to obtain an “analytical signature” for every

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source; first of all, average, range, standard deviation, and coefficient of variation were calculated

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for each source. Therefore, for the automatic classification of samples, mono- and multivariate

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statistical analyses were employed, with the aim of predicting the production method (wild or

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farmed sea bass), geographical origin (FAO fishing subarea for wild subjects, and country of origin

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for farmed ones), and the so-called “stocking density” factor.

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Analyses of single parameters were carried out for each classification target, to determine if

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different groups of subjects were significantly different from each other. To compare wild and

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farmed sea bass, Student’s t-test was applied, accepting as significant P values ≤ 0.05; to compare

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wild subjects from different FAO fishing subareas, farmed subjects from different countries, and

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sea bass classified according to stocking density, one-way analysis of variance (ANOVA) was

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conducted, accepting as significant P values ≤ 0.05, plus a post hoc Bonferroni correction due to the

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high number (175) of parameters analyzed. Multiparametric analyses (principal component analysis

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and classification) were conducted on each aforementioned set of variables, separately. The

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principal component analysis (PCA) procedure was applied to give an overview of the ability of 10 ACS Paragon Plus Environment

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selected parameters to discern samples according to production method, geographical origin, and

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stocking density, by visual inspection of the parameter space. The aim of the multivariate statistical

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analysis was to assess the classification performance of each set of variables, and to determine an

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optimal and restricted parameter set for sample classification. For these purposes, samples were

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categorized according to quadratic discriminant classifier. The “leave-one-out cross-validation”

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procedure allowed the assessment of method robustness during classification. This validation

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method consists of removing from the whole sample set, one at time, every single subject, which is

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going to be classified on the basis of information concerning all the other samples 30.

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3 Results and Discussions

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Each sample was processed to collect data about biometric characteristics and chemical

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composition (moisture, lipid content, FA composition, macro- and microelements, and isotopic

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abundance). Data obtained are reported in Supplementary Material Table S2.

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3.1 Biometric traits Weight and length of wild subjects analyzed in this study were similar to those reported in other 4,8,24

. Condition factor is considered a good indicator of dietary condition of fish species 32;

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studies

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likewise what reported in other works

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hepatosomatic index were found among subjects grouped according to the stocking density (P

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