Food Research International 121 (2019) 723–729

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Food Research International

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Survey of mislabelling across finfish supply chain reveals mislabelling both T outside and within Canada ⁎ Hanan R. Shehataa,b, Danielle Bourquea,b, Dirk Steinkeb, Shu Chenc, Robert Hannera,b, a Department of Integrative Biology, University of Guelph, Guelph, ON, Canada b Biodiversity Institute of Ontario, University of Guelph, Guelph, ON N1G 2W1, Canada c Laboratory Services Division, University of Guelph, Guelph, ON, Canada

ARTICLE INFO ABSTRACT

Keywords: has become one of the most heavily traded food commodities in the era of globalization. International Seafood seafood supply chains are complex and contend with many difficulties in bringing an enormous variety of Substitution products to market. A major challenge involves accurately labelling products such that they comply with a BOLD diverse set of regulatory frameworks, ranging from country-of-origin through to the final point of consumer sale. DNA barcoding Thanks to DNA barcoding, seafood mislabelling is now recognized as a global problem, with potentially negative Importer impacts on human health, economy and the environment. Mislabelling can result from species misidentification, Retailer Regulatory framework use of inappropriate common names, incomplete and/or out-dated regulatory frameworks, or through market substitution. While prior studies have focused primarily on retail and food service establishments, this study used barcoding to assess rates of finfish mislabelling at multiple points in the supply chain within Ontario, Canada.A total of 203 specimens from 12 key targeted species were collected from varied importers, registered processing plants and retailers in Southern Ontario and identified using DNA barcoding. Species identity of samples was used to assess conformity of labelling against the Canadian Food Inspection Agency's (CFIA) List, which revealed an overall mislabelling rate of 32.3% among targeted species. The mislabelling rate was significantly different between samples collected from importers and retailers. Among the mislabelled samples wereseven samples that originated from US and were properly labelled according to US Food and Drug Administration (FDA) Seafood List. This study evaluated the integrity of chain of custody documents and identified dis- crepancies in 43 samples (21.4%). Implementing seafood traceability throughout the supply chain and har- monizing labelling regulations between countries can help to ensure industry compliance in a globalized market, while sampling at multiple points in the supply chain can help to reveal causes.

1. Introduction unlawful practices such as illegal, unreported and unregulated fishing (IUU) and poor regulations on (Spink & Moyer, 2011; Seafood mislabelling is a serious problem that demonstrates the Pardo et al., 2016; Jacquet & Pauly, 2008). vital role and need for authenticity and traceability measures to control Complex supply chains are a major contributing factor to seafood food fraud and its associated health risks (Spink & Moyer, 2011). Pre- mislabelling, as seafood is the most traded food commodity worldwide vious studies conducted over the past five years in Canada and world- (Pardo et al., 2016; Koonse, 2016). The seafood supply chains consist of wide reported mislabelling rates ranging from 5% to 100%, averaging several steps starting from fishing (fisheries) or production (aqua- at 30% (Naaum et al., 2016; Pardo et al., 2016). The most common culture), transport to first buyer or primary processor, processing/ form of mislabelling is species substitution; however, other forms of packaging, transport to wholesalers, distribution to retailers and res- mislabelling exist such as substituting a wild caught fish with a farmed taurants and finally to consumers. As a consequence, pinpointing where one, which may contain varying levels of chemicals and mislabelling occurs becomes more challenging (Leal et al., 2015; (Cabello et al., 2013; (FDA). Eating Fish: What Pregnant Women and Muñoz-Colmenero et al., 2016). It may be intentional, and economic- Parents Should Know, 2017). Seafood mislabelling poses a threat to the ally motivated, at any point in the supply chain, but may also be un- economy, to consumer health, and to sustainable management of intentional; for example, caught fish may be misidentified during overexploited fish species. Additionally, seafood mislabelling facilitates fishing due to similarity in physical/morphological characteristics

⁎ Corresponding author at: Centre for Biodiversity Genomics, Biodiversity Institute of Ontario, University of Guelph, Guelph, ON N1G 2W1, Canada. E-mail address: [email protected] (R. Hanner). https://doi.org/10.1016/j.foodres.2018.12.047 Received 8 February 2018; Received in revised form 4 December 2018; Accepted 22 December 2018 Available online 24 December 2018 0963-9969/ © 2018 Published by Elsevier Ltd. H.R. Shehata et al. Food Research International 121 (2019) 723–729

(Muñoz-Colmenero et al., 2016). Errors may also happen during dis- Mexico (10), New Zealand (7), Norway (2), Pakistan (2), Peru (5), tribution especially in instances when a large volume of fish needs to be Philippines (2), Portugal (7), Russia (1), Senegal (1), Sri Lanka (3), processed in a short time period (Muñoz-Colmenero et al., 2016). Once Trinidad (5), US (34), Vietnam (6) and UK (6). The declared storage processed, products can be difficult to identify and mix-ups can occur. conditions of collected samples were frozen for 55 samples, refrigerated However, patterns of market substitution suggest economically moti- for 119 samples, refrigerated but previously frozen for 28 samples and vated adulteration is not uncommon. one live sample. Twenty-one samples were Marine Stewardship Council Modern molecular methods such as DNA barcoding provide useful (MSC) certified, one sample was Aquaculture Stewardship Council tools for seafood authentication. These methods are particularly valu- (ASC) certified, and five samples were Ocean Wise recommended able for processed specimens where morphological features are lost (Table A1). To comply with the minimum information required for (Pardo et al., 2016; Muñoz-Colmenero et al., 2016; Chin et al., 2016). market surveys using DNA barcoding, metadata included inspector's DNA barcoding depends on of an ~650 bp fragment of the name, date/time of collection, declared common name, brand name, mitochondrial cytochrome oxidase I gene (COX-I) which has been weight, country of origin, packer/manufacturer, storage conditions, widely used for species identification (Chin et al., 2016; Hanner et al., registered establishment name and address, city, type of registered es- 2011; Ward et al., 2009). The retrieved sequences are queried against tablishment (importer, retailer, registered processing plant), detailed reference databases such as the Barcode of Life Data Systems (BOLD) or location, and photographs for the product, master carton and labels (if the National Center for Biotechnology Information (NCBI) GenBank to applicable) (Naaum et al., 2015). infer a species identification of an unknown based on its barcode se- quence. 2.3. DNA extraction, PCR amplification of COX-1 gene, sequencing and Most previous surveys for seafood mislabelling focused on retail sequence analysis outlets and food service establishments and are unable to assess where in the supply chain problems arise. Through collaboration with the Muscle tissues of finfish samples were subsampled for DNA extrac- Canadian Food Inspection Agency (CFIA), finfish samples from com- tion (~10 mg of tissue). Subsampling tools were cleaned using monly mislabelled products were collected from different points in the ELIMINase® (04-355-32, Fisher Scientific) before handling the first supply chain including importers, registered processing plants and re- sample, between samples and after handling the last sample by dipping tailers. Furthermore, chain of custody documents were made available the tools into ELIMINase for 5 s, followed by three washes in deionized for this study in order to evaluate their integrity. The objectives of this water. DNA extraction was performed using the Qiagen DNeasy® Blood study were to use DNA barcoding technology to study prevalence of and Tissue kit (Qiagen, Mississauga, Canada) according to manufac- finfish mislabelling among targeted taxa at different stages ofthesea- turer's instructions. food supply chain (in an effort to pinpoint the sources of mislabelling Fish cocktail primers were used to amplify the COX-1 gene from and to determine to what extent each step in the supply chain con- finfish DNA (Table 1). When fish cocktail primers failed to amplify tributes to finfish mislabelling in Southern Ontario), to establish base- COX-1 gene from finfish DNA, mammal cocktail primers were used line data on key commodities, and to evaluate discrepancies in chain of (Table 1). Mammal cocktail primers were designed for barcoding of custody documents for finfish products. mammals but were found to perform well with seafood when fish pri- mers failed (Ivanova et al., 2007). When both fish cocktail and mammal 2. Materials and methods cocktail primers failed, mini-barcoding primers were used (Table 1). Mini-barcode primers amplify a shorter region (below 200 bp), which 2.1. Specimen collection is advantageous when attempting to amplify degraded DNA (Ivanova et al., 2012). Please see supplemental methods for detailed methods. A total of 203 finfish samples (Table A1) were collected during the PCR products were submitted to the Genomics facility at the summer of 2016 by CFIA inspectors to study finfish mislabelling rate in Advanced Analysis Centre (AAC) at the University of Guelph for se- Southern Ontario, Canada as a case study. Samples were collected from quencing (Table 1 and supplemental methods). The resulting sequences different points in the supply chain: 141 samples (69.5%) from retailers, were queried against a reference dataset (BOLD: DATASET-FISHREG1) 51 samples (25%) from importers, and 11 samples (5.5%) from regis- containing species included on the CFIA Fish List and the FDA Seafood tered processing plants. Samples were collected in the Greater Toronto List, as well as a number of species that are globally traded to assess Area (GTA): 90 samples from Toronto, 45 from Mississauga, 29 from species identity. Samples were classified as successfully identified tothe Vaughan, 26 from Brampton, and 13 from Markham (Table A1). species level, successfully identified to the genus level, or failed (either Targeted commodities included products labelled as: red snapper, mahi due to poor sequence quality or lack of match). mahi, , , , halibut, dover sole, , Chilean seabass, , white , and snapper. The list of target 2.4. Assessing labelling accuracy species was compiled based on CFIA Issues Management System (IMS) complaints with “mislabel” or “misrep” or “substit” in the subject line Because this study was conducted in Canada, to determine whether under marine products, on CFIA fish species sampling activity, and on a sample was appropriately labelled, the species identified through seafood species substitution reports by the US Food and DNA barcoding was searched on the CFIA Fish List (http://www. Drug Administration (FDA) (http://www.fda.gov/Food/ inspection.gc.ca/active/scripts/fssa/fispoi/fplist/fplist.asp?lang=e, FoodScienceResearch/RFE/ucm071528.htm) and by Oceana (Warner accessed November 2016) to determine if it is acceptable for the de- et al., 2013). Only raw fresh or frozen single species finfish products clared common name of the product tested. If the species name was were sampled. Collected specimens were kept frozen and then were listed on the CFIA Fish List and the product declared common name transferred on ice packs to the Biodiversity Institute of Ontario for matched an acceptable market name for that species, the sample was analysis. considered as properly labelled. If the species name did not exist on the CFIA Fish List or if it existed but was not the corresponding species for 2.2. Provenance data the declared common name, the sample was considered mislabelled in the broad sense (Shehata et al., 2018). Next, mislabelled samples were The country of origin was known for 158 samples: Argentina (1), classified into two categories: Obvious mislabelling and ambiguous Canada (5), Chile (7), China (mainland 17, Taiwan 4), Columbia (2), mislabelling. Obvious mislabelling is when both market and scientific Costa Rica (1), Ecuador (1), France (1), Greece (5), Guyana (5), Holland names were found in the regulatory framework but did not match (e.g. (2), Honduras (2), Iceland (9), Indonesia (1), Ireland (2), Japan (2), Melanogrammus aeglefinus labelled as cod). Ambiguous mislabelling is

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Table 1 Primers used for PCR and sequencing reactions of COX-1 gene.

Primer name Ratio Primer sequence Size (bp) Ref

Fish cocktail 652 (Ivanova et al., 2007) VF2_t1 1 TGTAAAACGACGGCCAGTCAACCAACCACAAAGACATTGGCAC FishF2_t1 1 TGTAAAACGACGGCCAGTCGACTAATCATAAAGATATCGGCAC FishR2_t1 1 CAGGAAACAGCTATGACACTTCAGGGTGACCGAAGAATCAGAA FR1d_t1 1 CAGGAAACAGCTATGACACCTCAGGGTGTCCGAARAAYCARAA Mammal cocktail 658 (Ivanova et al., 2007) LepF1_t1 1 TGTAAAACGACGGCCAGTATTCAACCAATCATAAAGATATTGG VF1_t1 1 TGTAAAACGACGGCCAGTTCTCAACCAACCACAAAGACATTGG VF1d_t1 1 TGTAAAACGACGGCCAGTTCTCAACCAACCACAARGAYATYGG VF1i_t1 3 TGTAAAACGACGGCCAGTTCTCAACCAACCAIAAIGAIATIGG LepR1_t1 1 CAGGAAACAGCTATGACTAAACTTCTGGATGTCCAAAAAATCA VR1d_t1 1 CAGGAAACAGCTATGACTAGACTTCTGGGTGGCCRAARAAYCA VR1_t1 1 CAGGAAACAGCTATGACTAGACTTCTGGGTGGCCAAAGAATCA VR1i_t1 3 CAGGAAACAGCTATGACTAGACTTCTGGGTGICCIAAIAAICA Mini-barcoding AquaF2 ATCACRACCATCATYAAYATRAARCC ~200 (Ivanova et al., 2012) FishR2_t1 1 CAGGAAACAGCTATGACACTTCAGGGTGACCGAAGAATCAGAA FR1d_t1 1 CAGGAAACAGCTATGACACCTCAGGGTGTCCGAARAAYCARAA Sequencing primers (Messing, 1983) M13F (−21) TGTAAAACGACGGCCAGT M13R (−27) CAGGAAACAGCTATGAC

Fig. 1. Summary of the methods used for data analysis in this study. when a market name or a scientific name or both were not found ina samples collected at different points in the supply chain and from dif- regulatory framework (e.g. Solea solea for dover sole or Oreochromis ferent cities. aureus for ). Ambiguous mislabelling may be the result of an in- DNA was extracted from all 203 samples, followed by successful complete regulatory framework. Lastly, chain of custody documents PCR amplification of COX-1 barcode region using fish cocktail primers, were examined and compared for discrepancies and samples were mammal cocktail primers or mini-barcode primers. To assess species classified as samples showing discrepancies in chain of custody docu- identity, we used a special reference dataset of 2677 sequences on ments if discrepancies were found between names declared on the BOLD (DATASET-FISHREG1) rather than using the entire public BOLD package, master carton, invoice or retail label (Fig. 1). database because using public databases for regulatory purposes could be risky given that public databases are open for anyone to deposit 2.5. Statistical analyses sequences, which may result in inclusion of unpublished or untrusted records. Dedicated databases are thus needed for proper regulatory A Chi-square and effect size (Odds Ratio; OR) tests were performed measures. using GraphPad Prism 6 to determine whether a significant difference Species (or genus) identification was successful in 201 samples exists between the mislabelling rates between samples collected at (99%) (Table A1). This success rate was higher than reported in pre- different points in the supply chain, between samples collected from vious studies (Chin et al., 2016; Hanner et al., 2011; Nwani et al., different storage condition, and between samples collected from dif- 2011), potentially because the samples collected here were not heavily ferent cities. processed, the majority being fresh or frozen fillet or whole fish. The two samples (samples 33 and 111) that failed to gain identification, best 3. Results and discussion matched Cynoscion parvipinnis with only 92–93% similarity. Among the 201 successful samples, 199 samples were identified to the species 3.1. Sample collection and barcoding success level, while two could only be identified to the genus level (samples 16: Sebastes sp. and sample 143: Lopholatilus sp.). The presence of uni- In this study, inspectors were able to collect a total of 203 samples dentified samples in this study indicate that reference sequence libraries from all targeted species except for white tuna, as it is not commonly are not fully parameterized and hence more work is needed to complete sold in Canada at retail. The term ‘white tuna’ is commonly used for them (Ward et al., 2009). The genus Sebastes has been shown to be a escolar at food service establishments, mainly restaurants (Ling challenging case for the barcoding system because of the recent di- et al., 2008). Some species were underrepresented compared to others versification of the group and the occurrence of introgressive hy- reflecting their availability in the market, and this was also truefor bridization between close relatives (Steinke et al., 2009), suggesting

725 H.R. Shehata et al. Food Research International 121 (2019) 723–729 that additional genetic markers may also be required in some cases. Table 2 Among the 201 successful samples, 12 samples failed to amplify List of mislabelled finfish specimens from this study. with the fish primers but successfully amplified with the mammal pri- mers. It was interesting to find that 8 out of 10 samples identified as Epinephelus morio in this study were among the 12 samples that am- plified with the mammal primers. This may indicate that mammal primers work better with this particular species and that development of new PCR primers for fish barcoding is necessary (Becker et al., 2011). Two samples (samples 63 and 172) failed amplification using both the fish and mammal cocktail primers because of bacterial contamination (retrieved sequences matched Pseudomonas sp. with 91% and 92% si- milarity, respectively). Sample 63 was refrigerated but previously frozen while sample 172 was refrigerated. The bacterial contamination of these samples may indicate issues with cold chain integrity. Both samples were successfully identified using mini-barcode primers as Dissostichus mawsoni (63) and Epinephelus morio (172).

3.2. Finfish mislabelling throughout the supply chain

Following the search for the species names identified through DNA barcoding on the CFIA Fish List to determine whether they matched the declared common name, 65 samples out of 201 samples (32.3%) were considered mislabelled according to the CFIA Fish List (Table 2). This mislabelling rate demonstrates mislabelling among targeted species and may not represent overall mislabelling rate in finfish in Southern On- tario. However, similar mislabelling rates were previously reported. Pardo et al. found the mean mislabelling rate in seafood mislabelling studies conducted between 2010 and 2015 to be 30% (Pardo et al., 2016), while Stawitz et al. found the mean of mislabelling rates to be 35% in seafood DNA barcoding studies (Stawitz et al., 2017). Interestingly, the rate of mislabelling varied among samples col- lected from retailers, registered processing plants and importers, where mislabelling rates were 38.1%, 27.3%, and 17.6%, respectively. The difference between mislabelling rates in samples collected fromre- tailers and from importers was significant (OR = 2.88; 95% CI: 1.31to 6.53; P-value = .0076, Chi-square). The higher mislabelling rate in samples collected from retailers, compared to that for samples collected from importers, indicates the role of distribution and repackaging in seafood mislabelling. In other words, seafood mislabelling seems to be cumulative throughout the supply chain and results from both mis- labelled imports and mislabelling within Canada.

3.3. Finfish mislabelling among the declared storage conditions and among sampling cities

The mislabelling rate varied slightly, but not significantly among the declared storage conditions (p-value = .32, Chi-square). The rate was 42.9% for refrigerated but previously frozen samples, and 31.9% and 26.4% for refrigerated and frozen samples, respectively. The higher mislabelling rates in previously frozen seafood may be attributed to more handling. The mislabelling rates for samples collected from dif- ferent cities were: Markham (46.2%), Toronto (34.4%), Mississauga (31.8%), Brampton (28%), and Vaughan (24.1%). These were not sig- nificantly different (p-value = .66, Chi-square) and justify averaging them regionally.

3.4. Finfish mislabelling in certified samples

Although MSC, ASC and Ocean Wise certification are not directly concerned with proper labelling of seafood, the rate of mislabelling among samples with these certifications was evaluated. MSC assures that a certified fish can be traced to a sustainable source. ASC manages labelling of aquaculture products, while Ocean Wise assures a certified fish to be an ocean-friendly choice. Among the 21 samples that were MSC certified, seven samples were mislabelled (49, 56, 66, 87, 90, 166, 173). All seven samples were declared as Chilean seabass, seabass,

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Patagonian sea bass, or but found to be either was considered mislabelled, this scientific name is acceptable for Dissostichus mawsoni () or Dissostichus eleginoides on the FDA Seafood List. In another case, a sample (94)im- (Patagonian toothfish). The issues surrounding the nomenclature of ported from the US was labelled yellowedge grouper and was found to toothfishes warrant consideration as prices have escalated and trade be Hyporthodus flavolimbatus. Neither of this common nor scientific volume has decreased (Grilly et al., 2015), prompting sustainability name is acceptable according to the CFIA Fish List. In a similar case, concerns and the need for accurate catch data scheme reporting to the three samples that were labelled red snapper and found to be Rhom- Commission for the Conservation of Antarctic Marine Living Resources boplites aurorubens (47, 92, and 139) originated from the US. All five (CCAMLR) under the Convention on International Trade in Endangered samples were properly labelled in their country of origin but once im- Species (CCAMLR, 2016). ported in Canada, were considered mislabelled. Over all, seven out of The sample that was ASC certified (155) was declared as tilapia; eleven samples that originated from the US and were considered mis- however, it was identified as Oreochromis aureus (ambiguous mislabel- labelled according to the CFIA Fish List, were properly labelled in the ling). This species is not included for tilapia according to CFIA, al- US. To resolve these issues in traded commodities, it is essential to though it is commonly regarded as a tilapia elsewhere (e.g. FDA). Three harmonize seafood labelling standards (Chan et al., 2015). out of the five Ocean Wise recommended samples were mislabelled Given these findings, we attempted to compare between the CFIA with two of them declared as Chilean seabass, which is not an accep- Fish List and the FDA Seafood List. Both lists were accessed and table common name in the CFIA Fish List. The third sample was de- downloaded in December 2016. Comparing the complete CFIA Fish List clared as tilapia but identified as Oreochromis aureus (ambiguous mis- (908 entries) and the FDA Seafood List entries (1854) showed that only labelling). 602 entries are common between both lists. As of December 2016 the FDA Seafood List showed 1252 unique entries and the CFIA Fish List 3.5. Overall mislabelling in finfish had 306 unique entries. The huge difference observed between the two lists demonstrates the need for more harmonized approach between the Among 65 mislabelled samples, for only 24 samples both the sci- two countries. Furthermore, in some instances one common name can entific name and the declared common name were on the CFIA FishList be acceptable for more than one species in a regulatory framework but (obvious mislabelling), while the declared common names or scientific not for the other. For example, red snapper is an acceptable name for names were not on CFIA Fish List for 27 and 23 samples, respectively. both Lutjanus campechanus and Sebastes ruberrimus on the CFIA Fish List, Furthermore, in nine of these cases, both the common name and the while the name is only acceptable for Lutjanus campechanus on the FDA scientific names were not on CFIA Fish List. Seafood List, where yelloweye rockfish is the acceptable name for In 24 out of 65 cases of mislabelling in this study, both common and Sebastes ruberrimus. According to the Integrated Taxonomic Information scientific names were acceptable based on the CFIA Fish List; however System (ITIS) (https://www.itis.gov), red snapper applies to Lutjanus the names were not acceptable names for each other. In five samples campechanus and Lutjanus gibbus. This further demonstrates the need for (66, 95, 176, 183, 199), Dissostichus mawsoni or Dissostichus eleginoides harmonization and for updating the CFIA Fish List. This is further re- were labelled as sea bass while only Dicentrarchus labrax can be labelled commended to resolve some inconsistency issues within the list. For sea bass in Canada. Dissostichus mawsoni and Dissostichus eleginoides are example, the CFIA Fish List includes both “seabream” and “sea bream” acceptable as Antarctic Toothfish and Patagonian Toothfish, respec- as common names but each of these names is acceptable for different tively. In one case (90), Dissostichus mawsoni was mislabelled as species. Such discrepancies complicate the compliance when dealing Patagonian Toothfish. Another common case of mislabelling wasob- with these two common names. served in six samples where Rhomboplites aurorubens was labelled as red Another reason the CFIA Fish List needs to be systematically re- snapper (3, 47, 92, 139, 144, and 186). Rhomboplites aurorubens is ac- viewed is because of changes in . An example is the name cepted as B-liner or beeliner on the CFIA Fish List but not as red “Pangasius hypophthalmus” on CFIA Fish List, which is a synonym of snapper. “Pangasianodon hypophthalmus”, the name used on BOLD and accepted In 27 of the mislabelling cases, the declared common names, cor- as scientifically valid. Such changes lead to serious discrepancies be- responding to 16 different common names, were not acceptable ac- tween sequence databases, which are being continuously updated and cording to CFIA Fish List (Table 3). For 23 of the mislabelling cases, the regulatory frameworks, which are rarely updated and hence complicate identified scientific names, corresponding to 17 scientific names, were the efforts to assess labelling compliance using DNA barcoding. not on the CFIA Fish List. Updating the CFIA Fish List, for example, by adding the scientific names, Carcharhinus limbatus, Oreochromis aureus, 3.7. Discrepancies between names declared on chain of custody documents Leucoraja ocellata, Solea solea, and Kajikia audax could resolve several of the mislabelling cases. Based on these naming issues, 41 of the 65 Besides the high level of species substitution reported above, 43 mislabelling cases were categorized as ambiguous mislabelling versus samples (21.4%) showed discrepancies between the names displayed on obvious mislabelling in 24 cases. invoice, master carton, retail sign or retail packages. Interestingly, 31 of samples that showed discrepancies were mislabelled. The mislabelling 3.6. Case study between trading partners: discrepancies between the CFIA rate was significantly different between samples that showed dis- and the US FDA regulatory frameworks crepancies versus samples with no discrepancies (p-value < .0001, Chi-square). Furthermore, four samples (187, 188, 189, 190) collected Some mislabelled samples found in this study were imported from from retailer had no sign posted and common names were declared the US. To investigate naming conventions, we examined the impacts of verbally. the regulatory frameworks and found some cases would have been Notably, 19 samples were not labelled as previously frozen although properly labelled in the US but were considered mislabelled after being their invoice or master carton indicated previously frozen. Two of these imported to Canada. Mislabelled samples according to the CFIA Fish samples were labelled “PF” on the retail sign, which may not be re- List were searched in the FDA Seafood List (http://www.accessdata.fda. cognized by consumers as previously frozen. In fact, selling previously gov/scripts/fdcc/?set=seafoodlist, accessed November and December frozen seafood as fresh seafood is, itself, a violation of the Food and 2016). Out of 17 scientific names that did not exist on the CFIA Fish Drug Regulations. Similarly, eight of these 19 samples (42%) were List, 13 were on the FDA Seafood List (Table 3). Interestingly, sample mislabelled; however, there was no significant difference in the mis- 121 that originated from the US was labelled flounder and was found to labelling rate among these 19 samples compared to mislabelling rate be Hippoglossoides robustus. Although this scientific name is not accep- among the other 182 samples (p-value-0.39, Chi-square). table according to CFIA Fish List for flounder, and as such, this sample Seafood traceability, through collecting information regarding the

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Table 3 Common and scientific names of specimens tested in this study that do not exist on CFIA FishList.

No. Common name not on CFIA Fish List Corresponding scientific name on FDA Seafood List

1 Chilean seabass Dissostichus eleginoides or Dissostichus mawsoni 2 Black grouper Mycteroperca bonaci 3 White snapper pinky NA 4 Red sea bream NA 5 Golden pomfret NA 6 Vermilion snapper Rhomboplites aurorubens 7 Yellowfin grouper Mycteroperca venenosa 8 Green bass NA 9 Tile grouper NA 10 Patagonian seabass NA 11 Jade NA 12 White snapper Macolor niger 13 Grouper yellow NA 14 Black barracuda NA 15 Yellowedge grouper Hyporthodus flavolimbatus 16 Spigola NA

No. Scientific name not on CFIA Fish List Corresponding common name on FDA Seafood List

1 Caulolatilus microps or blueline tilefish 2 Carcharhinus limbatus blacktip or shark 3 Pagrus auratus squirefish or porgy 4 Mycteroperca microlepis grouper or gag 5 Oreochromis aureus tilapia 6 Leucoraja ocellata winter skate or skate 7 Pseudupeneus prayensis NA (Fishbase: West African goatfish) 8 Trachinotus ovatus derbio or pompano 9 Hyporthodus flavolimbatus yellowedge grouper or grouper 10 Cynoscion virescens green weakfish or weafish or corvina 11 Hippoglossoides robustus flounder or bering flounder 12 Solea solea European dover sole, common sole or sole 13 Kajikia audax striped marlin 14 barcoo Barcoo grunter, tigerperch or grunter 15 Scomberomorus munroi NA (Fishbase: Australian spotted ) 16 Serranus atricauda NA (Fishbase: blacktail comber) 17 Sphyraena putnamae NA (Fishbase: Sawtooth barracuda) path of seafood, throughout the supply chain from boat or farm to 4. Conclusions consumers will help control seafood fraud. Furthermore, as seafood price depends on species, freshness, production method, origin, and The findings of this study show that seafood mislabelling continues sustainable production, information about how and where the fish was to be an issue in Canada and that mislabelling happens before im- caught or farmed, as well as species name, commercial name, produc- porting into Canada, as well as throughout the supply chain. Results tion method and storage conditions, should be made available and clear also demonstrate the need for continued work on building sequence to consumers (Leal et al., 2015; Muñoz-Colmenero et al., 2016; Warner libraries, developing new primers, and systematically revising seafood et al., 2016; Watson et al., 2016). The inclusion of species name on the regulatory frameworks. Detection of bacterial contamination in two of master carton is recommended for imported fish. Indeed, the aquatic the samples may indicate issues with cold chain integrity and hence health regulations require that the species name is visible on all present a health concern. Future studies on seafood microbiome will imported aquatic to Canada. A recent study found that im- help detect foodborne pathogens (Pegoraro et al., 2015), as well as help proved regulations and media attention did not improve the seafood with tracing seafood sources. Furthermore, the discrepancies observed mislabelling issue in Los Angeles (Willette et al., 2017). However, en- in the chain of custody documents indicate that additional issues exist forcement of proper regulations and traceability in the European Union in the supply chain and suggest that review of chain of custody docu- were successful in reducing mislabelling rates (Mariani et al., 2015). ments is an important first step in prioritizing samples for genetic Average seafood mislabelling rates in the European Union dropped analysis. It is recommended for the seafood industry sector to compile from over 20% between 2003 and 2011 to ~8% in 2015 upon im- and maintain traceability data to control seafood mislabelling. It is also plementing enhanced IUU regulations and traceability (Warner et al., essential to achieve harmonized labelling frameworks between coun- 2016). tries to facilitate industry compliance in a globalized market. Lack of harmonized legislations between countries makes trace- ability difficult to achieve. It is thus essential to develop harmonized regulations. The Canadian-European Union Comprehensive Economic Acknowledgements and Trade Agreement (CETA) is now in place (Canada Go. Opportunities and benefits of CETA for Canada's fish and seafood ex- The authors acknowledge support from the CFIA and would like to porters, 2016), and further harmonization between Canada and the specifically thank Michelle Amio for all her help and Raquel Delmas European Union could be explored. Applying Latin names in Canada and Hanan Hassan, CFIA inspectors, who collected the samples. The will support compliance with CETA (Roebuck et al., 2017), and could authors thank the Genomics facility at the Advanced Analysis Centre be an important step in achieving the United Nations Food and Agri- (AAC) at the University of Guelph for conducting the sequencing. This culture Organization (FAO) call for harmonization (Reilly, 2018). paper is also a contribution to the Food from Thought research program supported by the Canada First Research Excellence Fund.

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