Development of a MALDIâTOF MS-Based Protein Fingerprint

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Article

Development of a MALDI-TOF MS based protein fingerprint database of common food fish, allowing fast and reliable identification of fraud and substitution Antje Stahl, and Uwe Schröder J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.7b02826 • Publication Date (Web): 26 Jul 2017 Downloaded from http://pubs.acs.org on July 31, 2017

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Journal of Agricultural and Food Chemistry

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Development of a MALDI-TOF MS based protein fingerprint database of

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common food fish, allowing fast and reliable identification of fraud and

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substitution

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Antje Stahl * and Uwe Schröder

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Intertek Food Services GmbH

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Olof-Palme-Straße 8

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28719 Bremen

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Germany

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* Corresponding Author

Antje Stahl

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[email protected]

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+49 421 65727 1

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ACS Paragon Plus Environment


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ABSTRACT

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Fish substitution and fish fraud are widely observed on the global food market. To detect and

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prevent substitution, DNA-based methods do not always meet the demand of being time- and

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cost-efficient; therefore, methodology improvements are needed. The use of species-specific

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protein patterns, as determined by matrix-assisted laser desorption/ ionization time-of-flight

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(MALDI-TOF) mass spectrometry has recently improved species identification of

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prokaryotes, both time- and cost-wise. We have used the method to establish a database

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containing protein patterns of common food fish prone to substitution. The database currently

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comprises 54 fish species. Aspects such as the sensitivity of identification on species level

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and the impact of bacterial contamination of fish filet are assessed. Most database entries are

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characterized by low intraspecies but high interspecies variability. Hitherto, 118 validation

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samples were successfully determined. The herein presented results underline the potential

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and reliability of eukaryotic species identification via MALDI-TOF mass spectrometry.

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KEYWORDS

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MALDI Biotyper · Food Authenticity · Protein Pattern · Mislabeling · Fish Species

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Identification · MALDI Fingerprinting

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INTRODUCTION

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The substitution and mislabeling of food fish is a common practice in the global food market.1

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Substitution becomes particularly difficult to identify when the fish have already been

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processed and characteristic features such as head, fins, and skin are missing. Cases of

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substitution do appear unintentionally but may also be deliberate, thus leading to food fraud.

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With the aim of criminal economic gain, high value fish are substituted with fish of lower

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value.2,3 Moreover, the consumption of mislabeled fish may cause health risks in specific

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cases.4,5 The appearance of endangered species on the market, being sold under different

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names is an additional issue.6

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The substitution rate of fish is often dependent on the species.1,7–10 Particular species are

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prone to substitution, as there are grouper (Ephinephelus sp.),9,11,12 northern red snapper

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(Lutjanus campechanus)

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(Solea solea).

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cod (Gadus morhua and Gadus macrocephalus) are frequently substituted.12,18,19

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To monitor correct labeling of fish, suitable methods for safe and fast species identification

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are needed. Currently, food authenticity with reference to species identification is facilitated

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by molecular DNA methods. These methods are well established and reliable; however, they

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are still rather cost-intensive and time consuming under certain circumstances. These

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circumstances include non-targeted approaches or when there is no fast detection method,

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such as real time PCR options, available for certain selected species.

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An alternative method of species identification does not focus on DNA but on a species-

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specific protein pattern, a so-called protein fingerprint, that is obtained by matrix-assisted

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laser desorption/ ionization – time of flight mass spectrometry (MALDI-TOF MS). This type

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of species identification, also known as MALDI fingerprinting, is established for

16,17

13–15

or flatfish with a high market value, such as the common sole

However, also rather common food fish species like Atlantic and Pacific

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microorganisms such as bacteria,20–22 yeast,23 and other fungi.24 Its advantages are found in its

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low-cost, time efficient performance 25–27 and reliability, and it has found wide application in

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clinical diagnostics 28 and develops in the veterinary sector.29,30 Initial research has shown that

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the applicability of protein pattern-based species identification is also possible for higher

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eukaryotes.31–34 With regards to marine fauna, the primary feasibility of the MALDI

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fingerprinting method is already shown for the identification of fish

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like scallops

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demonstrated, comprehensive databases that are available for routine verification of fish and

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other types of seafood remain to be generated.

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In this study, we aimed to establish fish species identification via MALDI fingerprinting for

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routine applications in e.g. service laboratories. A protein fingerprint database of common

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food fish, obtained by application of MALDI-TOF MS was developed. Emphasis was set on

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those species that are prone to substitution, to allow the database to be used for efficient

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identification of mislabeling and fraud. Known samples were used, to test the quality and

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reliability of both the methodology and the database. Further parameters like the impact of

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bacterial contamination of filet on species determination were assessed.

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35,36

and other seafoods

and shrimp.38,39 However, although the practical possibility has been

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MATERIALS AND METHODS

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Sample material (database and validation samples). Based on literature research, a list of

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fish species prone to substitution and mislabeling was prepared. Hence, priority for the build-

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up of the database was set on those fish species previously associated with cases of

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substitution. Due to feasible availability, a second emphasis was set on species that are mainly

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offered on the mid-European market. Most individuals were obtained via collaboration with a

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food retailer. A minority of fish samples for database build-up were received from

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supermarkets, fish shops, and fishmongers (both frozen and chilled fish). As a prerequisite, all

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individuals belonging to one species were caught on different dates. Where possible, further

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attention was paid to variation in parameters like origin (oceanic region; wild catch or

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aquaculture) and variation in the provider.

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Validation samples were received from supermarkets, fish shops, and fishmongers.

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MALDI-TOF MS sample preparation and measurement. All preparation steps were

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conducted under cooling conditions (handling of fish on cool packs, use of cooling blocks for

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sample tubes). Representative sample aliquots were retrieved from fresh, first cuts of a filet,

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whereas surface areas and distinctive parts like skin, scales, or bones were omitted. Aliquots

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were immediately processed or kept frozen at -20 °C until further use. 0.220 +/- 0.005 g filet

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were placed in 2 mL lysis tubes (lysis matrix B, MP Biomedicals, Eschwege, Germany) and

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combined with 500 µL cooled 0.1% trifluoroacetic acid (TFA; 2-8 °C; Sigma-Aldrich,

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Steinheim, Germany) prepared in LC-MS grade water (Fluka Analytical, Steinheim,

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Germany). Cells were broken by bead mill treatment (20 s, 4.0 m sc-1; MP FastPrep). Samples

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were kept at 2-8 °C for 15 min as a potential pause- and re-cooling point. Sample tubes were

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centrifuged afterwards (15 s, 20,000 rcf). The sample supernatant was diluted with cooled 5



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TFA (0.1%) by a factor 5. From the diluted sample, 1 µL was applied to the sample spot of a

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MALDI target (MSP 96 target polished BC, Bruker Daltonik, Bremen, Germany). After

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drying at room temperature, the spot was covered with 1 µL α-cyano-4-hydroxycinnamic acid

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solution (HCCA, 0.01 g mL-1; Bruker Daltonik). HCCA solution was prepared in ready-made

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2.5% TFA in 50% acetonitrile (LC-MS grade, Fluka Analytical).

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Protein mass spectra were determined with a Bruker microflex LT instrument (Bruker

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Daltonik), operated in linear positive mode (mass range 2-20 kDa (m/z)). ‘Bacterial Test

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Standard’ (Bruker Daltonik) was used for calibration (eight protein masses in a range from

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3637.8 to 16952.3 Da). Sum spectra were generated and obtained from 240 laser shots (6 x 40

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shots, minimal accepted intensity 400).

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Generation of database entries (main spectra, MSPs), testing of intraspecies similarity,

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and determination of validation samples. One database entry, generated from a set of

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quality checked single spectra, is commonly termed main spectrum (MSP). For each MSP,

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one sample was spotted ten times onto the target, from each spot three sample spectra were

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determined (30 single spectra in total). One species in the database was to be represented by

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three individuals. From each individual, two MSPs were generated (in total six MSPs per

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species). FlexAnalysis software version 3.4 (Bruker Daltonik) was used for quality testing

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according to the following parameters: only those mass spectra being a sum of 6 x 40 (=240)

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laser shots were considered, the most intense peak of one spectrum had to have a relative

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intensity of at least 10,000. In a range from 3,000 to 10,000 Da, all spectra of one set were

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checked each 1,000 Da for peak deviation with reference to the same mass peak. A maximum

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of 500 ppm difference between lowest and highest mass observed between spectra was

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accepted. Single spectra not fulfilling these prerequisites were removed from the dataset.

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Minimum 20 single spectra were mandatory for the generation of one MSP. MSPs were 6


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generated with MALDI Biotyper 3 software (Bruker Daltonik; 3,000 to 15,000 Da, S/N = 3,

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max. 70 peaks).

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To test the intraspecies similarity, those single spectra that were detected for the preparation

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of an MSP were compared to the MSPs of the other individuals of the same species. When

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doing so, the two MSPs of the ‘own’ individual were removed to avoid a match ‘against

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itself’. Obtained result-scores were documented. Dendrograms were generated with MALDI

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Biotyper 3 software.

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Validation samples were processed once and spotted three times onto the MALDI target.

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From each spot, three mass spectra were determined (in total nine spectra per sample).

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Obtained mass spectra were compared to the database. Top species hits and result-scores were

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recorded.

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Species confirmation via DNA barcoding. The species of fish samples was confirmed by

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DNA barcoding. In detail, the 5’ region of the cytochrome c oxidase I (COI) was sequenced.40

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DNA was extracted using a Maxwell MDx instrument (Promega, Madison, USA). In brief,

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200 mg of fish muscle tissue was combined with 1000 µL CTAB lysis-buffer (AppliChem,

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Darmstadt, Germany) and incubated under constant shaking (15 min, 65 °C). 40 µL

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proteinase K (Promega) was added and samples again incubated (75 min, 65 °C). Solids were

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pelleted afterwards by centrifugation (10 min, 16,000 rcf). 300 µL of sample supernatant was

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loaded into a ‘Maxwell FFS Nucleic Acid Extraction System’ and combined with 300 µL

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lysis buffer (Promega). DNA was extracted according to the manufacturer’s settings.

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Extracted DNA was finally obtained in 100 µL elution buffer (Promega).

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A ~650 bp fragment was amplified from the mitochondrial COI gene. The fish specific primer

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pair cocktail COI-3 was used,41 the PCR protocol was modified by increasing the annealing

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temperature to 53 °C. PCR reaction mix was prepared with ‘TaqMan Fast Universal PCR 7



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Master Mix (2x)’ (Applied Biosciences, Foster City, USA) according to the manufacturer’s

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specifications. PCR products were cleaned with a purification kit to remove surplus primer

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and contaminations 2.357), the closest and all further relatives can be excluded as a

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result when they are not available in the database (in case of a genetic similarity of >95%).

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The applied threshold is now defined as 2.400. This formulated principle is applicable to the

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data shown in

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Table 4: even if M. molva is not available in the database, a safe species determination of B.

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brosme is possible, as they share a genetic similarity of 2.400, when each being

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compared with the other MSP.

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Certain entries belong to genera that comprise many species, as the species of the genera

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Coregonus, Oreochromis, or Sebastes. In addition, many species within those genera share

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high genetic similarity. Even if all members of one genus are available in the database,

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species differentiation is difficult due to their high similarities. Identification of such species

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thus can only be made on the genus level, with the possibility of excluding some species with

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lower genetic similarity (Supporting Information - Table S4).

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In summary, closest relatives and their corresponding genetic similarities of all database

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entries is determined for all database entries (Supporting Information - Table S5-S10).

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Emphasis is placed on those entries sharing ≤95% to their closest relative (Table 1, marked *).

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To exclude a close relative which is not in the database, a score of ≥2.400 is mandatory. This

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threshold is feasible, as 83.9% of the tested validation samples exceed this value. If this

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threshold is not exceeded, the result needs to respect the fact that a second species cannot be

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excluded for certain.

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Impact of bacterial contamination on fish identification. Food fish usually carry microbial

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microflora.44–47 Observed bacterial cell numbers highly depend on storage conditions such as

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cooling, freezing parameters, and storage time.44,46 In theory, the protein load resulting from

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filet-borne bacteria may affect clear identification of the filet. To assess the impact of

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bacterial proteins in the present study, Escherichia coli biomass is added to three different

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fish filet samples, G. macrocephalus, Sander lucioperca, and Solea solea in a mass fraction of

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~1% and ~10% (w/w). The added amounts correspond to an actual cell number of 9.08 x 109

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(± 1.32 x 108) cells g-1 filet (~1%) and 6.70 x 1010 (± 1.97 x 109) cells g-1 filet (~10%). These 16


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test concentrations exceed reported bacterial concentrations in fish, which are mainly found in

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a range of 101 to 108 cells g-1 filet. Only in particular cases like spoilage are bacterial numbers

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up to 109 cells g-1 observed.47-50 Results show that the identification of the filet in the presence

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of 1-2% E. coli is possible. Obtained scores do not show significant decreases in the scores of

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reference samples (p95%

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genetic similarity, thus showing little difference between scores) may be strengthened by the

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search for species-specific protein masses, biomarkers, respectively, within spectra. Small 17



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mass differences in protein spectra have little impact on the score differentiation. A detailed

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(manual) observation of biomarker in the actual spectra will however enhance the reliability

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of species differentiation. Initial tests are conducted on members of the genus Oncorhynchus

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and Gadus (data not shown). The combination with additional techniques, as there is MS/MS,

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may offer further analytical options in this context.

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Higher eukaryotic organisms are characterized by differentiated tissue (in comparison to

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single-cell bacteria or yeast). To a large extent, bacterial protein masses detected via MALDI

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fingerprinting represent ribosomal proteins,51,52 and this may theoretically also be the case for

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eukaryotic protein spectra. Nevertheless, a database should only refer to protein patterns

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obtained from one type of tissue. Variation in protein patterns, generated from different types

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of tissue is identified in the current study, where skin and muscle tissue (filet) are compared

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(data not shown). A skin sample might still be identified as the correct fish species as top hit

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when compared to a database comprising muscle tissue, however the score may be well below

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2.000 (data not shown). Further studies supported the observation of variant spectra obtained

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from different tissues of fish.36

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ABBREVIATIONS USED

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BLAST = Basic Local Alignment Search Tool; BOL (database) = Barcode of Life (database);

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COI (gene) = cytochrome c oxidase I (gene); MSP = main spectrum (database entry,

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generated from quality-checked single spectra); NCBI = National Center of Biotechnology

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Information; TFA = trifluoroacetic acid.

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ACKNOWLEDGEMENT

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The authors would like to thank the ‘METRO Cash & Carry Deutschland GmbH’ and

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‘METRO Italia Cash and Carry S.p.A.’ for providing an extensive variety of fish species. The

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authors further thank Candice Thorstenson, Peter Mahady, and Gerhard Rimkus for critical

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review of the manuscript.

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SUPPORTING INFORMATION

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Supporting Information - Table S1: Matching of protein mass spectra of one individual

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against MSPs of remaining individuals, belonging to the same species (intraspecies

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similarity).

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Supporting Information - Figure S2. Exemplary protein mass spectra of three different fish

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species.

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Supporting Information - Table S3: Testing of validation samples.

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Supporting Information - Table S4: Defining the level of identification.

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Supporting Information - Table S5. Genetic similarity of all members of the taxonomic

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order Gadiformes.

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Supporting Information - Table S6. Result-scores of MSPs, compared to MSPs of relatives

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of the same order (order Gadiformes).

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order Pleuronectiformes.

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Supporting Information - Table S8. Result-Scores of MSPs, compared to MSPs of relatives

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of the same order (order Pleuronectiformes).

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order Salmoniformes.

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Supporting Information - Table S10. Result-scores of MSPs, compared to MSPs of

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relatives of the same order (order Salmoniformes).

21



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REFERENCES

442 443

(1)

sector. Food Control 2016, 62, 277–283.

444 445

Pardo, M. Á.; Jiménez, E.; Pérez-Villarreal, B. Misdescription incidents in seafood

(2)

Barbuto, M.; Galimberti, A.; Ferri, E.; Labra, M.; Malandra, R.; Galli, P.; Casiraghi,

446

M. DNA barcoding reveals fraudulent substitutions in shark seafood products: The

447

Italian case of “palombo” (Mustelus spp.). Food Res. Int. 2010, 43 (1), 376–381.

448

(3)

Rasmussen, R. S.; Morrissey, M. T. DNA-based methods for the identification of

449

commercial fish and seafood species. Compr. Rev. food Sci. food Saf. 2008, 7, 280–

450

295.

451

(4)

Cohen, N. J.; Deeds, J. R.; Wong, E. S.; Hanner, R. H.; Yancy, H. F.; White, K. D.;

452

Thompson, T. M.; Wahl, M.; Pham, T. D.; Guichard, F. M.; et al. Public health

453

response to puffer fish (Tetrodotoxin) poisoning from mislabeled product. J. Food

454

Prot. 2009, 72 (4), 810–817.

455

(5)

Lowenstein, J. H.; Burger, J.; Jeitner, C. W.; Amato, G.; Kolokotronis, S.-O.;

456

Gochfeld, M. DNA barcodes reveal species-specific mercury levels in tuna sushi that

457

pose a health risk to consumers. Biol. Lett. 2010, 6 (5), 692–695.

458

(6)

Agnew, D. J.; Pearce, J.; Pramod, G.; Peatman, T.; Watson, R.; Beddington, J. R.;

459

Pitcher, T. J. Estimating the worldwide extent of illegal fishing. PLoS One 2009, 4 (2),

460

e4570.

461

(7)

Mariani, S.; Griffiths, A. M.; Velasco, A.; Kappel, K.; Jerome, M.; Perez-Martin, R. I.;

462

Schröder, U.; Verrez-Bagnis, V.; Silva, H.; Vandamme, S. G.; et al. Low mislabeling

463

rates indicate marked improvements in European seafood market operations. Front.

464

Ecol. Environ. 2015, 13 (10), 536–540.

465

(8)

Bénard-Capelle, J.; Guillonneau, V.; Nouvian, C.; Fournier, N.; Le Loët, K.; Dettai, A. 22


Page 22 of 36

Page 23 of 36


Fish mislabelling in France: Substitution rates and retail types. PeerJ 2015, 2, e714.

466 467

(9)

Commission, E. Commission recommendation on a coordinated control plan with a

468

view to establishing the prevalence of fraudulent practices in the marketing of certain

469

foods. 2015.

470

(10)

Di Pinto, A.; Mottola, A.; Marchetti, P.; Bottaro, M.; Terio, V.; Bozzo, G.; Bonerba,

471

E.; Ceci, E.; Tantillo, G. Packaged frozen fishery products: Species identification,

472

mislabeling occurrence and legislative implications. Food Chem. 2015, 194, 279–283.

473

(11)

Asensio, L.; González, I.; Rojas, M.; García, T.; Martín, R. PCR-based methodology

474

for the authentication of grouper (Epinephelus marginatus) in commercial fish fillets.

475

Food Control 2009, 20, 618–622.

476

(12)

Di Pinto, A.; Di Pinto, P.; Terio, V.; Bozzo, G.; Bonerba, E.; Ceci, E.; Tantillo, G.

477

DNA barcoding for detecting market substitution in salted cod fillets and battered cod

478

chunks. Food Chem. 2013, 141, 1757–1762.

479

(13)

Khaksar, R.; Carlson, T.; Schaffner, D. W.; Ghorashi, M.; Best, D.; Jandhyala, S.;

480

Traverso, J.; Amini, S. Unmasking seafood mislabeling in U.S. markets: DNA

481

barcoding as a unique technology for food authentication and quality control. Food

482

Control 2015, 56, 71–76.

483

(14)

Marko, P. B.; Lee, S. C. R.; Amber M; Gramling, J. M. F.; Tara M; McAlister, J. S. H.;

484

George R. Moran, A. L. Mislabelling of a depleted reef fish. Nature 2004, 430, 309–

485

310.

486

(15)

Logan, C. A.; Alter, S. E.; Haupt, A. J.; Tomalty, K.; Palumbi, S. R. An impediment to

487

consumer choice: Overfished species are sold as Pacific red snapper. Biol. Conserv.

488

2008, 141, 1591–1599.

489 490

(16)

Kappel, K.; Schröder, U. Substitution of high-priced fish with low-priced species: Adulteration of common sole in German restaurants. Food Control 2016, 59, 478–486. 23



491

(17)

Pappalardo, A. M.; Ferrito, V. DNA barcoding species identification unveils

492

mislabeling of processed flatfish products in southern Italy markets. Fish. Res. 2015,

493

164, 153–158.

494

(18)

the European seafood industry. Ecol. Environ. 2014, 8 (10), 517–521.

495 496

(19)

Filonzi, L.; Chiesa, S.; Vaghi, M.; Nonnis Marzano, F. Molecular barcoding reveals mislabelling of commercial fish products in Italy. Food Res. Int. 2010, 43, 1383–1388.

497 498

Miller, D. D.; Mariani, S. Smoke, mirrors, and mislabeled cod: Poor transparency in

(20)

Szabados, F.; Woloszyn, J.; Richter, C.; Kaase, M.; Gatermann, S. Identification of

499

molecularly defined Staphylococcus aureus strains using matrix-assisted laser

500

desorption/ionization time of flight mass spectrometry and the Biotyper 2.0 database. J.

501

Med. Microbiol. 2010, 59, 787–790.

502

(21)

Seng, P.; Drancourt, M.; Gouriet, F.; La Scola, B.; Fournier, P.-E.; Rolain, J. M.;

503

Raoult, D. Ongoing revolution in bacteriology: Routine identification of bacteria by

504

matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Clin Infect

505

Dis 2009, 49, 543–551.

506

(22)

Eigner, U.; Holfelder, M.; Oberdorfer, K.; Betz-Wild, U.; Bertsch, D.; Fahr, A. M.

507

Performance of a matrix-assisted laser desorption ionization-time-of-flight mass

508

spectrometry system for the identification of bacterial isolates in the clinical routine

509

laboratory. Clin. Lab. 2009, 55, 289–296.

510

(23)

Bader, O.; Weig, M.; Taverne-Ghadwal, L.; Lugert, R.; Gross, U.; Kuhns, M.

511

Improved clinical laboratory identification of human pathogenic yeasts by matrix-

512

assisted laser desorption ionization time-of-flight mass spectrometry. Clin. Microbiol.

513

Infect. Off. Publ. Eur. Soc. Clin. Microbiol. Infect. Dis. 2011, 17, 1359–1365.

514 515

(24)

De Carolis, E.; Posteraro, B.; Lass-Flörl, C.; Vella, A.; Florio, A. R.; Torelli, R.; Girmenia, C.; Colozza, C.; Tortorano, A. M.; Sanguinetti, M.; et al. Species 24


Page 24 of 36

Page 25 of 36


516

identification of Aspergillus, Fusarium and Mucorales with direct surface analysis by

517

matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Clin.

518

Microbiol. Infect. 2012, 18, 475–484.

519

(25)

Dhiman, N.; Hall, L.; Wohlfiel, S. L.; Buckwalter, S. P.; Wengenack, N. L.

520

Performance and cost analysis of matrix-assisted laser desorption ionization-time of

521

flight mass spectrometry for routine identification of yeast. J. Clin. Microbiol. 2011, 49

522

(4), 1614–1616.

523

(26)

Lagacé-Wiens, P. R. S.; Adam, H. J.; Karlowsky, J. A.; Nichol, K. A.; Pang, P. F.;

524

Guenther, J.; Webb, A. A.; Miller, C.; Alfa, M. J. Identification of blood culture

525

isolates directly from positive blood cultures by use of matrix-assisted laser desorption

526

ionization-time of flight mass spectrometry and a commercial extraction system:

527

Analysis of performance, cost, and turnaround time. J. Clin. Microbiol. 2012, 50 (10),

528

3324–3328.

529

(27)

Laakmann, S.; Gerdts, G.; Erler, R.; Knebelsberger, T.; Martínez Arbizu, P.; Raupach,

530

M. J. Comparison of molecular species identification for North Sea calanoid copepods

531

(Crustacea) using proteome fingerprints and DNA sequences. Mol. Ecol. Resour. 2013,

532

1–15.

533

(28)

Croxatto, A.; Prod’hom, G.; Greub, G. Applications of MALDI-TOF mass

534

spectrometry in clinical diagnostic microbiology. FEMS Microbiol. Rev. 2012, 26, 380-

535

407.

536

(29)

Guardabassi, L; Damborg, P.; Stamm, I.; Kopp, P.A.; Broens, E.M.; and Toutain, P.-L.

537

Diagnostic microbiology in veterinary dermatology: Present and future. Vet. Dermatol.

538

2017, 28, 146–e30.

539 540

(30)

Stamm, I.; Hailer, M.; Depner, B.; Kopp, P.A.; Rau, J. Yersinia enterocolitica in diagnostic fecal samples from European dogs and cats: Identification by fourier 25



541

transform infrared spectroscopy and matrix-assisted laser desorption ionization–time of

542

flight mass spectrometry. J. Clin. Microbiol. 2013, 51 (3), 887–893.¶

543

(31) Barbano, D.; Diaz, R.; Zhang, L.; Sandrin, T.; Gerken, H.; Dempster, T. Rapid

544

characterization of microalgae and microalgae mixtures using matrix-assisted laser

545

desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). PLoS One

546

2015, 1–13.

547

(32)

Flaudrops, C.; Armstrong, N.; Raoult, D.; Chabrière, E. Determination of the animal

548

origin of meat and gelatin by MALDI-TOF MS. J. Food Compos. Anal. 2015, 41, 104–

549

112.

550

(33)

Yssouf, A.; Socolovschi, C.; Leulmi, H.; Kernif, T.; Bitam, I.; Audoly, G.; Almeras,

551

L.; Raoult, D.; Parola, P. Identification of flea species using MALDI-TOF MS. Comp.

552

Immunol. Microbiol. Infect. Dis. 2014, 37, 153–157.

553

(34)

Rau, J.; Eisenberg, T.; Wind, C.; Lasch, P.; Sting, R. MALDI-UP – An internet

554

platform for the exchange of MALDI-TOF mass spectra. Asp. Food Control Anim.

555

Heal. 2016, ejournal 1, 1–17.

556

(35)

Mazzeo, M. F.; De Giulio, B.; Guerriero, G.; Ciarcia, G.; Malorni, A.; Russo, G. L.;

557

Siciliano, R. A. Fish authentication by MALDI-TOF mass spectrometry. J. Agric. Food

558

Chem. 2008, 56, 11071–11076.

559

(36)

Volta, P.; Riccardi, N.; Lauceri, R.; Tonolla, M. Discrimination of freshwater fish

560

species by matrix-assisted laser desorption/ionization-time of flight mass spectrometry

561

(MALDI-TOF MS): A pilot study. J. Limnol. 2012, 71 (1), 164–169.

562

(37)

Stephan, R.; Johler, S.; Oesterle, N.; Näumann, G.; Vogel, G.; Pflüger, V. Rapid and

563

reliable species identification of scallops by MALDI-TOF mass spectrometry. Food

564

Control 2014, 46, 6–9.

565

(38)

Ortea, I.; Canas, B.; Calo-Mata, P.; Barros-Velázquez, J.; Gallardo, J. M. Arginine 26


Page 26 of 36

Page 27 of 36


566

kinase peptide mass fingerprinting as a proteomic approach for species identification

567

and taxonomic analysis of commercially relevant shrimp species. J. Agric. Food Chem.

568

2009, 57, 5665–5672.

569

(39)

for identification of shrimp. Anal. Chim. Acta 2013, 794, 55–59.

570 571

(40)

(41)

Ivanova, N. V.; Zemlak, T. S.; Hanner, R. H.; Hebert, P. D. N. Universal primer cocktails for fish DNA barcoding. Mol. Ecol. Notes 2007, 7, 544–548.

574 575

Ratnasingham, S.; Hebert, P. D. N. Barcoding BOLD: The Barcode of Life data system (www.barcodinglife.org). Mol. Ecol. Notes 2007, 7, 355–364.

572 573

Salla, V.; Murray, K. K. Matrix-assisted laser desorption ionization mass spectrometry

(42)

Pruitt, K. D.; Tatusova, T.; Maglott, D. R. NCBI reference sequences (RefSeq): A

576

curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic

577

Acids Res. 2007, 35 (Database issue), 61–65.

578

(43)

species (genus Thunnus). PLoS One 2009, 4 (10), e7606.

579 580

Vinas, J.; Tudela, S. A validated methodology for genetic identification of Tuna

(44)

Parlapani, F. F.; Kormas, K. A.; Boziaris, I. S. Microbiological changes, shelf life and

581

identification of initial and spoilage microbiota of sea bream fillets stored under

582

various conditions using 16S rRNA gene analysis. J. Sci. Food Agric. 2015, 95 (12),

583

2386–2394.

584

(45)

Tong Thi, A. N.; Samapundo, S.; Devlieghere, F.; Heyndrickx, M. Microbiota of

585

frozen Vietnamese catfish (Pangasius hypophthalmus) marketed in Belgium. Int. J.

586

Food Contam. 2016, 3 (17), 1–9.

587

(46)

Wang, H.; Luo, Y.; Huang, H.; Xu, Q. Microbial succession of grass carp

588

(Ctenopharyngodon idellus) filets during storage at 4°C and its contribution to biogenic

589

amines’ formation. Int. J. Food Microbiol. 2014, 190, 66–71.

590

(47)

Lee, J. L.; Levin, R. E. Quantification of total viable bacteria on fish fillets by using 27



591

ethidium bromide monoazide real-time polymerase chain reaction. Int. J. Food

592

Microbiol. 2007, 118, 312–317.

593

(48)

markets in the Republic of Bulgaria. Int. Food Res. J. 2015, 22 (1), 64–69.

594 595

Stratev, D.; Vashin, I.; Daskalov, H. Microbiological status of fish products on retail

(49)

Armani, M.; Civettini, M.; Conedera, G.; Favretti, M.; Lombardo, D.; Lucchini, R.;

596

Paternolli, S.; Pezzuto, A.; Rabini, M.; Arcangeli, G. Evaluation of hygienic quality

597

and labelling of fish distributed in public canteens of Northeast Italy. Ital. J. Food Saf.

598

2016, 5, 185–190.

599

(50)

packed fish. Int. J. Food Microbiol. 1995, 26, 319–333.

600 601

(51)

Ryzhov, V.; Fenselau, C. Characterization of the protein subset desorbed by MALDI from whole bacterial cells. 2001, 73 (4), 746–750.

602 603

Dalgaard, P. Qualitative and quantitative characterization of spoilage bacteria from

(52)

Dieckmann, R.; Helmuth, R.; Erhard, M.; Malorny, B. Rapid classification and

604

identification of salmonellae at the species and subspecies levels by whole-cell matrix-

605

assisted laser desorption ionization-time of flight mass spectrometry. Appl. Environ.

606

Microbiol. 2008, 74 (24), 7767–7778.

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FIGURE CAPTIONS

608 609

Figure 1. Similarities of database entries (MSPs) of varying fish species. a) The MSP

610

similarities of ten different fish species are shown. Every species is represented by at least six

611

database entries (three individuals per species; two MSPs per individual; indicated by at least

612

six dendrogramm branches per species). b) MSP similarities of exclusively Gadus

613

macrocephalus and Gadus morhua.

614 615

Figure 2. Theoretical correlation between result-score and genetic similarity.

616 617

Figure 3. Genetic-similarity values plotted against result-scores. Each data point represents

618

the result-score of one database entry (MSP), compared to the database entries of a chosen

619

relative that belongs to the same taxonomic order. All entries of the orders Gadiformes,

620

Salmoniformes, and Pleuronectiformes were used according to this procedure.

621 622

Figure 4. Impact of bacterial contamination on fish sample identification (supplemented with

623

E. coli). Significant result-score decreases are marked with an asterisk (p

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