1. Unmasking seafood mislabeling in U.S. markets: DNA barcoding as a
unique technology for food authentication and quality control
Ramin Khaksar a, *
, Traci Carlson a
, Donald W. Schaffner b
, Mahni Ghorashi a
, Dieter Best c
,
Srikanth Jandhyala c
, Julie Traverso a
, Sasan Amini a, *
a
Food Authentication Laboratories, Research and Development Division, Clear Labs Inc., Menlo Park, CA, USA
b
Department of Food Science, Rutgers University, New Brunswick, NJ, USA
c
Engineering and Bioinformatics Division, Clear Labs Inc., Menlo Park, CA, USA
a r t i c l e i n f o
Article history:
Received 9 February 2015
Received in revised form
7 March 2015
Accepted 9 March 2015
Available online 17 March 2015
Keywords:
DNA barcoding
Food authenticity
Seafood
Mislabeling
DNA sequencing
Species identification
a b s t r a c t
Seafood mislabeling has been practiced both to meet market demand, as well as for economic gain,
whereby cheaper species are substituted for high-value seafood items. Traditional methods of identifying
species based on fish morphological characteristics are not always practical to unambiguously identify
seafood products available for purchase. The use of DNA sequencing for species identification is a reliable
alternative to traditional methods like morphological determination, especially when food items have
been subject to various forms of processing. Results show a rate of mislabeling among restaurants of
16.3% (28 out of 172 samples) in three regions of the United States. The rate of false labeling is essentially
equivalent between these different regions. This study shows the usefulness of targeting the mito-
chondrial cytochrome oxidase I (COI) region to uniquely identify fish and seafood samples from across
the United States.
© 2015 Elsevier Ltd. All rights reserved.
1. Introduction
Consumer demand for healthy foods has increased dramatically
over the last several decades (Jensen, Denver, & Zanoli, 2011;
Tarabella & Burchi, 2012) encouraging consumers to pay more
attention to their diet and the quality of the food they eat (Ding,
Veeman, & Adamowicz, 2013; Janssen & Hamm, 2012; Verbeke,
Vermeir, & Brunsø, 2007). An increase in media coverage about
the health benefits of fish and seafood (Yadavalli & Jones, 2014) has
resulted in increased demand for these items, as consumers seek
out a healthier diet. Though consumers are becoming more so-
phisticated and informed about what they are eating, their efforts
may be undermined by a lack of means for detecting fraudulent
food items (Carvalho, Neto, Brasil, & Oliveira, 2011; Cawthorn,
Steinman, & Witthuhn, 2012; Changizi, Farahmand, Soltani,
Darvish, & Elmdoost, 2013; Comi, Iacumin, Rantsiou, Cantoni, &
Cocolin, 2005; Filonzi, Chiesa, Vaghi, & Nonnis Marzano, 2010;
Garcia-Vazquez et al., 2011; Huang et al., 2014).
Species identification is a key factor in biodiversity research and
food security associated with seafood products (Cutarelli et al.,
2014). Species identification is also critical for detection of com-
mercial fraud, which often occurs with the intention of increasing
monetary gain (e.g. replacing higher value species with alternatives
of lesser value). Consumer inability to discriminate between
different fish species, and financial incentives for substitution can
lead to intentional or non-intentional mislabeling (Marko et al.,
2004).
Traditional speciation methods, such as morphological identi-
fication, cannot be used to identify mislabeled processed food
products as such products may lack specific morphological fea-
tures. Alternative identification methods such as DNA based ap-
proaches can be applied to identify animal species (Teletchea,
2009) to help consumers, retailers, and government to correctly
identify food species (Cawthorn et al., 2012). Currently DNA-based
methods are widely employed for testing and confirming food
authenticity (Maralit et al., 2013; Melo Palmeira et al., 2013;
Teletchea, 2009; Woolfe & Primrose, 2004; Yancy et al., 2008).
DNA sequencing is a reliable method for food authentication
because of its high sensitivity, and target DNA sequence diversity
(Ardura, Planes, & Garcia-Vazquez, 2013; Cutarelli et al., 2014;
* Corresponding authors. Tel./fax: þ1 650 462 1676.
E-mail addresses: ramin.khaksar@clearlabs.com (R. Khaksar), sasan.amini@
clearlabs.com (S. Amini).
Contents lists available at ScienceDirect
Food Control
journal homepage: www.elsevier.com/locate/foodcont
http://dx.doi.org/10.1016/j.foodcont.2015.03.007
0956-7135/© 2015 Elsevier Ltd. All rights reserved.
Food Control 56 (2015) 71e76

2. Hanner, Becker, Ivanova, & Steinke, 2011). Nucleic acids can be very
stable and may be preserved throughout various food processing
procedures including heat exposure, mixing, and pressure (Barbuto
et al., 2010; Wong & Hanner, 2008). DNA barcoding is a molecular
phylogenetics method that uses relatively short DNA markers
present in an organism's genome as a proxy for its taxonomic
identification. This method is one of the most widely used and
adopted tools available for food traceability (Ardura, Pola, Linde, &
Garcia-Vazquez, 2010; Galal-Khallaf, Ardura, Mohammed-Geba,
Borrell, & Garcia-Vazquez, 2014).
DNA barcoding has been successfully used for authenticating
fish samples in the past (Barbuto et al., 2010; Cline, 2012; Filonzi
et al., 2010; Smith, McVeagh, & Steinke, 2008; Yancy et al., 2008),
and it is the benchmark set by the US Food and Drug Administration
for seafood identification (Yancy et al., 2008). DNA barcode data has
also been included in FDA's Regulatory Fish Encyclopedia as a tool
to help prevent product mislabeling and fish species substitution.
The main advantage of fish DNA barcoding is its ability to identify
species even after industrial processing, however some highly
processed products (e.g. smoked or canned fish items) can present
challenges in DNA extraction (Smith et al., 2008; Wong & Hanner,
2008).
This study examined the prevalence of seafood mislabeling in
supermarkets and sushi venues in three US cities recognized as
ones where consumers are more likely to demand high-quality,
authentic food: Austin TX, New York NY, and San Francisco CA.
The mitochondrial cytochrome oxidase I (COI) region was used due
to its unique discriminatory power in identification of fish species
(Ward, Hanner, & Hebert, 2009) to investigate the authenticity of a
variety of seafood products.
2. Materials and methods
2.1. Sample collection
Fish samples were collected over a four-month period
(JuneeSeptember 2014) from three U.S. locations: New York City
(NY), Austin (TX) and the San Francisco Bay area (CA). A total of 228
samples were collected, of which 49 (21.4%) were obtained from the
wholesalers/retailers in the San Francisco Bay Area, while 179
(78.5%) were obtained directly from different sushi restaurants in
all three regions. Restaurants were chosen based on consumer
popularity in the region, and multiple types of commonly ordered
fish were obtained at each location. All samples were either directly
dropped off, or shipped by overnight delivery, within 16 h packed in
dry ice, to the processing laboratory following collection. Once
received, sample preparation started immediately.
2.2. DNA extraction
DNA was extracted from 200 mg of fresh fish sample using the
Nucleospin Food kit (Macherey Nagel GmbH & Co., Duren, Ger-
many) following the manufacturer's instructions. The concentra-
tions of the extracted DNA were assessed with a Qubit®
2.0
Fluorometer (Life Technologies, Carlsbad, USA) using the Qubit®
dsDNA HS (High Sensitivity) Assay Kit.
2.3. Polymerase chain reaction
A ~650 base pair (bp) fragment of mitochondrial COI was
amplified by polymerase chain reaction (PCR) using the M13-tailed
primer cocktail (Ivanova, Zemlak, Hanner, & Hebert, 2007). A 25 mL
reaction mixture was prepared using EmeraldAmp GT PCR Master
Mix (Clontech, Mountain View, CA, USA), template (less than
500 ng), forward and reverse primers (0.2 mM each). PCR
amplifications were performed in a PTC-100 PCR machine (MJ
Research™ Incorporated, Boston, USA) utilizing the following
thermal cycling parameters: initial denaturation at 94 C for 2 min,
35 cycles of denaturation at 94 C for 20 s, primer annealing at 52 C
for 30 s and chain elongation at 72 C for 60 s, followed by final
extension at 72 C for 10 min. The success of amplification was
assessed by 2% agarose gel (Invitrogen, Life Technologies, Carlsbad,
USA) electrophoresis, with AlphaImager Mini visualization under
an ultraviolet light (Protein simple, Santa Clara, California, USA).
2.4. Sequencing
PCR amplification products were purified with an ExoSAP-IT
PCR Clean-up Kit (Affymetrix, Santa Clara, CA USA) following the
instructions of the manufacturer. Sequencing of the purified PCR
products was performed using BigDye Terminator v3.1 Cycle
Sequencing Kit and Applied Biosystems 3730xl DNA Analyzer to
separate the sequencing products and collect the fluorescence data
according to the manufacturer's instructions.
2.5. Data analysis
The DNA sequences were analyzed according to the following
procedure: Both strands of the double-stranded PCR products were
sequenced. An attempt was made to merge the forward sequence
and the reverse complement of the reverse sequence in order to
obtain a higher quality sequence. The program megamerger from
the EMBOSS suite (Rice, Longden, Bleasby, 2000) of sequence
analysis tools was employed for this purpose. Sequences should
ideally be identical in their region of overlap, and a minimum
overlap of 20 bases was required. If a mismatch was encountered,
the base that is closest to the start of a sequence was chosen. After
this merging procedure, a list of merged sequences was obtained. If
merging was unsuccessful, the corresponding pair of forward and
reverse sequences was retained for further analysis.
The second step of the analysis procedure matched this list with
annotated sequences in a database. The program BLAST (Altschul,
Gish, Miller, Myers, Lipman, 1990) and the NCBI nt database of
annotated nucleotide sequences were used. A minimum perfect
match of 20 bases was required with a similarity of at least 80
percent. The match with the highest expectation value (E-value) of
the BLAST program was retained as potential species identification.
The E-value is a parameter that describes the number of hits one
can “expect” to see just by chance when searching a database of a
particular size (Altschul et al., 1990). The common/market names
were added to the species name and compared to the product name
under which the queried sample had been sold.
In order to test the ability to measure pure samples, recon-
structed sequences were clustered using the neighbor-joining al-
gorithm according to Saitou and Nei (Saitou Nei, 1987).
A one tailed t-test was used for comparing retailers and res-
taurants mislabeling in the San Francisco Bay area and an unpaired
t-test was used for showing differences between regions. GraphPad
Prism (GraphPad Software, Inc. La Jolla, CA, USA) was applied for
the statistical analysis.
3. Result and discussion
Fig. 1 shows the result for a subset of sequences that have been
trimmed by 40 base pairs at the 50 and 30 ends in order to remove
low quality bases. In addition sequences with no-calls (i.e. type of
base could not be determined by sequencing procedure) were
removed from this analysis. The phylogenetic tree (Fig. 1) depicts
the results of clustering the sequences according to the neighbor-
joining method (Saitou Nei, 1987). This serves as corroborating
R. Khaksar et al. / Food Control 56 (2015) 71e7672

3. evidence that the analysis generated by the BLAST NCBI nt database
was correct. A requirement for good identification is that barcode
clusters be specific, consistent, and non-overlapping with other
species clusters (Wong Hanner, 2008). The phylogenetic tree
corroborates that identifications made in this study are indeed
accurate.
A total of 216 of the 228 fish and seafood samples were ampli-
fied and sequenced successfully. Table 1 reflects the expected
versus observed scientific names of the samples obtained from
restaurants in all three regions. Table 2 shows the same information
regarding retailers in the San Francisco Bay Area.
Our results indicate, using a one tailed t-test, that the rate of
mislabeling in the San Francisco Bay area, in restaurants (14.8%) is
much higher than retailers (2.2%) (p 0.01). Strong competition
among retailers to protect their brands, increasing customer
transparency, and labeling systems are likely the main contributing
factors to higher quality observed in retail settings (He, Wang,
Cheng, 2013). Mislabeling can happen at different points in the
supply chain (Cawthorn et al., 2012), and restaurants might be
victims of mislabeling as well. It would be interesting to extend this
study to retailers operating in Austin and New York City to un-
derstand whether this is a nation-wide trend or specific to the San
Francisco Bay Area.
The mislabeling rates by region can be found in Table 3. An
unpaired t-test showed no significant difference (p 0.1) in the rate
of restaurant sample mislabeling between regions. Our study
shows that retailers in California have a lower rate of false labeling
compared to restaurants.
A similar study was done by Warner, Timme, Lowell, and
Hirschfield (2013), their results show that 33% of fish sold in the
United States are mislabeled; the results from the current study
(Table 3) indicate that the rate of fish and seafood mislabeling in the
Fig. 1. Phylogenetic tree for sequenced fish samples collected from restaurants in Austin TX, New York NY, and restaurants and retail establishments in San Francisco CA. The
phylogenetic tree was constructed using the neighbor-joining method by Saitou and Nei (1987).
R. Khaksar et al. / Food Control 56 (2015) 71e76 73

4. three cities investigated is less than half of that number (e.g. 12.8%
in our study).
Many restaurants do not label their menu items with extensive
details about the origin of products. A common example is salmon,
which could be either found as Atlantic or Pacific salmon, with the
latter category generally being more expensive. An interesting
observation was that, among the 25 unspecified salmon samples
tested here, 24 were Atlantic variety and only 1 was of a Pacific
variety (Table 1). This indicates that in the absence of a fully
transparent labeling convention consumers are more likely to
receive a version of the product of lesser value.
The most commonly mislabeled fish in the study was red
snapper, with 100% of the red snapper samples from this study
being substituted with red seabream or tilapia. Warner et al. (2013)
also reported similarly high rates of mislabeling of red snapper in
the same three states (CA: 100%, TX: 90%, NY: 78%). Results by
Table 1
Expected versus observed scientific names of fish and seafood samples collected from restaurants in Austin TX, New York NY, and San Francisco CA.
Advertised name Expected
scientific name(s)
# Of
samples
# Falsely
advertised
Actual scientific name(s)
and number observed
Accession numbers
associated with
fraud items
Expected accession
number
Chilean Seabass Dissostichus eleginoides 2 0 Dissostichus eleginoides (2) n/a AB723627.1
Seabass, bass Morone spp. 14 4 Oreochromis niloticus (1)
Pagrus major (2)
Paralichthys dentatus (1)
Morone spp. (10)
KC789549.1
AP002949.1
KC015755.1
EU524140.1 (M. chrysops)
Pacific Salmon (king salmon,
sockeye salmon, Alaskan
salmon)
Oncorhynchus nerka
Oncorhynchus tshawytscha
Oncorhynchuys keta
2 1 Salmo salar (1) Oncorhynchus
nerka(1)
KF792729.1 KF278770.1 (O. nerka)
AP010773.1 (O. keta)
HQ167683.1 (O. tshawytscha)
Atlantic Salmon (Scottish
salmon, Norwegian
salmon)
Salmo salar 8 0 Salmo salar (8) n/a KF792729.1
Salmon* Salmo salar
Oncorhynchus nerka
Oncorhynchus tshawytscha
Oncorhynchuys keta
25 0 Salmo salar (24)
Oncorhynchus keta (1)
n/a KF278770.1 (O. nerka)
AP010773.1 (O. keta)
HQ167683.1 (O. tshawytscha)
KF792729.1 (S. salar)
Yellowfin Tuna Thunnus albacares 1 0 Thunnus albacares (1) n/a JN086153.1
Tuna Thunnus spp. 31 0 Thunnus spp. (31) n/a JN086152.1 (T. obesus)
JN086153.1 (T. albacares)
KF906720.1 (T. thynnus)
JN086150.1 (T. maccoyii)
N086151.1 (T. alalunga)
Snapper (Tai Snapper,
Japanese Snapper)
Pagrus auratus
Lutjanus erythropterus
Lutjanus campechanus
9 1 Oreochromis spp. (1)
Pagrus auratus (8)
Lutjanus erythropterus (1)
C789549.1 AP002949.1 (P. auratus)
GQ265897.1 (L. erythropterus)
Red Snapper (Japanese
Red Snapper)
Lutjanus campechanus 16 16 Oreochromis spp. (8)
Pagrus major (8)
C789549.1
AP002949.1
KF461195.1
Pacific Cod, Alaskan Cod Gadus macrocephalus
Gadus ogac
2 0 Gadus ogac (2) n/a DQ356938.1 (G. macrocephalus)
DQ356941.1 (G. ogac)
Atlantic Cod Gadus morhua 1 0 Gadus morhua (1) n/a HG514359.1
Spanish Mackerel Acanthocybium spp.
Grammatorcynus spp.
Scomberomorus spp.
1 1 Trachurus japonicus (1) HM180926.1 e
Mackerel Rastrelliger spp.
Scomber spp.
Acanthocybium spp.
Grammatorcynus spp.
Scomberomorus spp.
Gasterochisma spp.
Trachurus spp.
Nealotus spp.
Thyrsitoides spp.
Gempylus spp.
Nesiarchus spp.Thyrsitops
spp.
Pleurogrammus spp.
1 0 Scomber scombrus (1) n/a AB120717.1 (Scomber scombus)
Yellowtail Seriola quinqueradiata 31 0 Seriola quinqueradiat (31) n/a AB517556.1
Dover Sole Microstomus pacificus 1 0 Microstomus pacificus (1) n/a JQ354230.1
Fluke Paralichthys dentatus 17 6 Paralichthys olivaceus (6)
Paralichthys
dentatus (11)
EU266369.1 KC015755.1
Grouper Mycteroperca microlepis 1 0 Serranidae (family containing
grouper) (1)
JQ840283.1 JQ842600.1
Tilapia Oreochromis spp. 4 0 Oreochromis spp. (4) n/a GU477624.1
(O. niloticus)
Octopus Octopus vulgaris 1 0 Octopus vulgaris (1) n/a FN424380.1
Sweet Shrimp Pandalus platyceros 1 0 Pandalus platyceros (1) n/a GU442235.1
Shrimp Litopenaeus vannamei 1 0 Litopenaeus vannamei (1) n/a EF584003.1
Eel Anguilla anguilla 1 0 Anguilla anguilla (1) n/a KJ564259.1
Basa Pangasius bocourti 1 1 Pangasianodon hypophthalmus (1) KC846907.1 JN021312.1
The number (in bold) reflects of number of each species observed for that particular advertised name.
R. Khaksar et al. / Food Control 56 (2015) 71e7674

5. Wong and Hanner (2008) also found a high mislabeling rate (77%)
for red snapper sold in New York City. Other fish types found to be
falsely advertised in our study were sea bass, fluke, Spanish
mackerel, basa, black mussel, canned tuna, and salmon (Tables 1
and 2). Fig. 2 shows overall distribution of mislabeling events
observed among the samples collected from restaurants.
If the 12.8% mislabeling rate in this study (Table 3) is repre-
sentative for the United States as a whole, it is a sizable portion of
the $16.5 billion fish market size (Griffiths et al., 2014). Accurate
determination of financial loss to the consumer is not very simple,
but does appear significant.
Effects of mislabeling are not limited to financial impact. A lack
of trust between consumers and producers or decreased con-
sumption of fish as a healthy alternative to other proteins are also
potential fallouts (Jacquet Pauly, 2008; Meyer, Coveney,
Henderson, Ward, Taylor, 2012; Migliore, Schifani, Cembalo,
2015). Increasing consumer awareness about mislabeling of sea-
food products may raise demand for authentic food from the
market. The lower rates of mislabeling found in this study in
comparison to previous reports (Warner et al., 2013; Wong
Hanner, 2008) may likely be a consequence of increased con-
sumer awareness. Consumers can be comforted by the low rates of
false advertisement among retailers and can be more confident
about the identity and quality of fish and seafood products they
purchase.
DNA testing has been used in the food industry for many years
to ensure food quality and purity, though existing technology is
limited by the cost, depth of the analysis, and accuracy (Melo
Palmeira et al., 2013; Woolfe, Gurung, Walker, 2013). The latest
innovations in DNA sequencing have enabled researchers and
regulatory agencies to use this platform as a reliable method for
DNA-based food authentication (Ratnasingham Hebert, 2007).
The role of media and scientific publications in increasing public
awareness is undeniable; indeed, they will raise the demand for
enforcement of more rigorous inspection and audit processes in
the food supply chain. The FDA's effort in establishing reference
datasets, such as Regulatory Fish Encyclopedia (Yancy et al., 2008),
is just the first step toward creating a system which can serve as a
benchmark for the food industry, academic institutions and regu-
latory agencies. Surveillance studies like this will help further
Table 2
Expected versus observed scientific names of fish and seafood samples collected from San Francisco Bay Area retailers.
Advertised name Expected scientific
name
# Of
samples
# Falsely
advertised
Actual scientific name(s)
and number observed
Accession numbers
associated with
sample
Expected accession number
Chilean Seabass Dissostichus eleginoides 2 0 Dissostichus eleginoides (2) n/a AB723627.1
Pacific Salmon (king salmon,
sockeye salmon, Alaskan
salmon)
Oncorhynchus nerka
Oncorhynchus
tshawytscha
Oncorhynchuys keta
8 0 Oncorhynchus nerka (4)
Oncorhynchus tshawytscha (2)
Oncorhynchuys keta (2)
n/a KF278770.1 (O. nerka)
AP010773.1 (O. keta)
HQ167683.1 (O. tshawytscha)
Atlantic Salmon (Scottish
salmon, Norwegian salmon)
Salmo salar 6 0 Salmo salar (6) n/a KF792729.1
Salmon Salmo salar
Oncorhynchus nerka
Oncorhynchus tshawytscha
Oncorhynchuys keta
2 0 Salmo salar (1)
Oncorhynchus nerka (1)
KJ443700.1 KF278770.1 (O. nerka)
AP010773.1 (O. keta)
HQ167683.1 (O. tshawytscha)
KF792729.1 (S. salar)
Yellowfin Tuna Thunnus albacares 1 0 Thunnus albacares (1) n/a JN086153.1
Tuna Thunnus spp. 3 0 Thunnus albacares (1)
Thunnus obesus (2)
n/a JN086152.1 (T. obesus)
JN086153.1 (T. albacares)
KF906720.1 (T. thynnus)
JN086150.1 (T. maccoyii)
N086151.1 (T. alalunga)
Pacific Cod, Alaskan Cod Gadus macrocephalus
Gadus ogac
2 0 Gadus ogac (2) n/a DQ356938.1 (G. macrocephalus)
DQ356941.1 (G. ogac)
Atlantic Cod Gadus morhua 4 0 Gadus morhua (4) n/a HG514359.1
Sole Eopsetta jordani
Microstomus pacificus
2 0 Eopsetta jordani (1)
Microstomus pacificus (1)
n/a JQ354087.1 (E. jordani)
JQ354230.1 (M. pacificus)
Tilapia Oreochromis spp. 4 0 Oreochromis sp. 'red tilapia' (2)
Oreochromis niloticus (2)
n/a GU477624.1 (O. niloticus)
HM067614.1 (O. sp 'red tilapia')
Black Mussel Choromytilus
meridionalis
1 1 Mytilus trossulus (1) KM192132.1 e
Calamari Doryteuthis pealeii 1 0 Doryteuthis pealeii (1) n/a AF207910.1
Little neck clam Mercenaria mercenaria 1 0 Mercenaria mercenaria (1) n/a HM124619.1
rockfish Sebastes spp. 1 0 Sebastes melanostictus (1) n/a DQ678311.1
Swordfish Xiphias gladius 1 0 Xiphias gladius (1) n/a AP006036.1
Mahi Mahi Coryphaena hippurus 2 0 Coryphaena hippurus (1) n/a KF814117.1 KF719178.1
Halibut Hippoglossus spp. 1 0 Hippoglossus stenolepis (1) n/a AM749126.1
Catfish Ictalurus punctatus 1 0 Ictalurus punctatus (1) n/a AF482987.1
Steelhead Oncorhynchus mykiss 1 0 Oncorhynchus mykiss (1) n/a DQ288270.1
The number (in bold) reflects of number of each species observed for that particular advertised name.
Table 3
Comparative analysis of mislabeling incidents across restaurants in Austin TX, New York NY, and San Francisco Bay Area CA restaurants and retail outlets.
Source Number of samples Number mislabeled Percent mislabeled
Austin, TX (restaurants) 53 8 15.1%
New York, NY (restaurants) 58 11 19.0%
SF Bay Area, CA (restaurants) 61 9 14.8%
SF Bay Area, CA (retailers) 44 1 2.2%
Total 216 29 12.8%
R. Khaksar et al. / Food Control 56 (2015) 71e76 75

6. refine the scope of such efforts and identify existing knowledge
gaps.
Acknowledgment
We thank Zack Naqvi, Michaela Rollings, Tiffany Huynh, John
Robert Reed, Jordan Bresler Jordan Plews for helping us collect
market samples from New York City, Austin and the San Francisco
Bay Area. We applaud the efforts of the FDA in creating a reference
library of fish species to aid in more stringent DNA testing across
the U.S. supply chain.
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Fig. 2. Percentage of mislabeling based on product type from restaurants in Austin TX,
New York NY, and San Francisco CA. *Basa and Spanish mackerel are only based on 1
sample each.
R. Khaksar et al. / Food Control 56 (2015) 71e7676