The use of molecular markers in the verification of fish and seafood authenticity and the detection of adulteration

Differentiation of three commercial tuna species through GC-Q-TOF and UPLC-Q/Orbitrap mass spectrometry-based metabolomics and chemometrics

Food fraud is common in the tuna industry because of the economic benefits involved. Ensuring the authenticity of tuna species is crucial for protecting both consumers and tuna stocks. In this study, GC-Q-TOF and UPLC-Q/Orbitrap mass spectrometry-based metabolomics were used to investigate the metabolite profiles of three commercial tuna species (skipjack tuna, bigeye tuna and yellowfin tuna). A total of 22 and 77 metabolites were identified with high confidence using GC-Q-TOF and UPLC-Q/Orbitrap mass spectrometry, respectively. Further screening via chemometrics revealed that 38 metabolites could potentially serve as potential biomarkers. Hierarchical cluster analysis showed that the screened metabolite biomarkers successfully distinguished the three tested tuna species. Furthermore, a total of 27 metabolic pathways were identified through enrichment analysis based on the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways.

Fish DNA Sensors for Authenticity Assessment-Application to Sardine Species Identification

Food and fish adulteration is a major public concern worldwide. Apart from economic fraud, health issues are in the forefront mainly due to severe allergies. Sardines are one of the most vulnerable-to-adulteration fish species due to their high nutritional value. Adulteration comprises the substitution of one fish species with similar species of lower nutritional value and lower cost. The detection of adulteration, especially in processed fish products, is very challenging because the morphological characteristics of the tissues change, making identification by the naked eye very difficult. Therefore, new analytical methods and (bio)sensors that provide fast analysis with high specificity, especially between closely related fish species, are in high demand. DNA-based methods are considered as important analytical tools for food adulteration detection. In this context, we report the first DNA sensors for sardine species identification. The sensing principle involves species recognition, via short hybridization of PCR-amplified sequences with specific probes, capture in the test zone of the sensor, and detection by the naked eye using gold nanoparticles as reporters; thus, avoiding the need for expensive instruments. As low as 5% adulteration of Sardina pilchardus with Sardinella aurita was detected with high reproducibility in the processed mixtures simulating canned fish products.

Rapid and Simultaneous Authentication of Six Laver Species Using Capillary Electrophoresis-Based Multiplex PCR

Lavers are typically consumed in dried or seasoned forms. However, commercially processed lavers can lead to seafood fraud because it is impossible to authenticate the original species based on morphological characteristics alone. In this study, we developed a capillary electrophoresis-based multiplex polymerase chain reaction (PCR) to authenticate six different laver species. The species-specific primer sets to target the chloroplast rbcL or rbcS genes were newly designed. We successfully established both singleplex and multiplex conditions, which resulted in specific amplicons for each species (N. dentata, 274 bp; N. yezoensis, 211 bp; N. seriata, 195 bp; N. tenera, 169 bp; N. haitanensis, 127 bp; P. suborbiculata, 117 bp). Moreover, the assays were sensitive enough to detect DNA ranging from 10 to 0.1 pg of DNA. The optimized capillary electrophoresis-based multiplex PCR was successfully applied to 40 commercial laver products. In addition to detecting the laver species as stated on the commercial label, the assay discovered cases where less expensive species were mixed in. With its advantageous properties, such as short amplicon size, high specificity, and superior sensitivity, this assay could be used for the authentication of the six laver species.

Current State of Milk, Dairy Products, Meat and Meat Products, Eggs, Fish and Fishery Products Authentication and Chemometrics

Food fraud is a matter of major concern as many foods and beverages do not follow their labelling. Because of economic interests, as well as consumers' health protection, the related topics, food adulteration, counterfeiting, substitution and inaccurate labelling, have become top issues and priorities in food safety and quality. In addition, globalized and complex food supply chains have increased rapidly and contribute to a growing problem affecting local, regional and global food systems. Animal origin food products such as milk, dairy products, meat and meat products, eggs and fish and fishery products are included in the most commonly adulterated food items. In order to prevent unfair competition and protect the rights of consumers, it is vital to detect any kind of adulteration to them. Geographical origin, production methods and farming systems, species identification, processing treatments and the detection of adulterants are among the important authenticity problems for these foods. The existence of accurate and automated analytical techniques in combination with available chemometric tools provides reliable information about adulteration and fraud. Therefore, the purpose of this review is to present the advances made through recent studies in terms of the analytical techniques and chemometric approaches that have been developed to address the authenticity issues in animal origin food products.

The use of telomeric length as authenticity marker in fish and seafood - a new perspective in the detection of adulteration

In this review we propose the use of telomeric length (TL) as an authenticity marker that could provide an alternative method for differentiating fish and seafood samples or detecting fraud. Considering the ever-growing number of incidents of economically motivated fish and seafood adulteration using even more sophisticated methods to overcome current authenticity markers, the need to identify novel authenticity markers becomes essential. The TL of fish and seafood depends on individual characteristics (e.g., sex, age) and the environmental stimuli (e.g., temperature, water quality) to which these are exposed. Hence, both wild marine and freshwater populations occupying different geographical origin habitats might differ substantially because of the environmental cues affecting them. Moreover, the implementation of various rearing practices in aquaculture, such as different levels of fish and seafood density and increased ambient noise combined with site-specific environmental cues could affect TL, providing regulatory authorities with valuable information by distinguishing wild from reared populations and organic from conventional ones. In the present review the effects of both the environmental conditions and individual characteristics on the telomeric stability of fish and seafood telomeres are discussed, suggesting TL as a potential prospect authenticity marker that could be used to prevent fish and seafood adulteration.

Development of Multiplex PCR and Melt-Curve Analysis for the Molecular Identification of Four Species of the Mullidae Family, Available in the Market

The authentication of food products and the verification of their identity are of major importance for consumers. Food fraud through mislabeling is an illegal practice consisting of the substitution of an expensive food product by a relatively cheaper one, misleading false labelling of their origin and adulteration in processed or frozen products. This issue is particularly of high importance concerning fish and seafood, which are easily adulterated primarily due to difficult morphological identification. Fish species of the Mullidae family are considered among the most high-valued seafood products traded in Greece and Eastern Mediterranean in general, in terms of the price and demand. Specifically, the red mullet (Mullus barbatus) and the striped red mullet (Mullus surmuletus) are both indigenous in the Aegean (FAO Division 37.3.1) and the Ionian (FAO Division 37.2.2) Seas, with high levels of consumers' preferences. However, they could be easily adulterated or misidentified by the invasive Aegean Sea Lessepsian migrator goldband goatfish (Upeneus moluccensis) as well as by the imported West African goatfish (Pseudupeneus prayensis). Keeping this in mind, we designed two novel, time-saving and easy-to-apply multiplex PCR assays and one multiple Melt-Curve analysis real-time PCR for the identification of these four species. These methodologies are based on species-specific primers targeting single nucleotide polymorphisms (SNPs) detected via sequencing analysis of the mitochondrial cytochrome C oxidase subunit I (CO1) and of the cytochrome b (CYTB) genes in newly collected individuals, with additional comparison with congeneric and conspecific haplotypes obtained from the GenBank database. Both methodologies, targeting CO1 or CYTB, utilize one common and four diagnostic primers, producing amplicons of different length that are easily and reliably separated on agarose gel electrophoresis, yielding a single clear band of diagnostic size for each species or a certain Melt-Curve profile. The applicability of this cost-effective and fast methodology was tested in 328 collected specimens, including 10 cooked samples obtained from restaurants. In the vast majority (327 out of the 328) of the specimens tested, one single band was produced, in agreement with the expected products with a single exception a M. barbatus sample that was identified as M. surmuletus, the identity of which was confirmed using sequencing, indicating erroneous morphological identification. The developed methodologies are expected to contribute to the detection of commercial fraud in fish authentication.

Establishment and evaluation of multiple adulteration detection of camellia oil by mixture design

Multiple adulteration is a common trick to mask adulteration detection methods. In this study, the representative multiple adulterated camellia oils were prepared according to the mixture design. Then, these representative oils were employed to build two-class classification models and validate one-class classification model combined with fatty acid profiles. The cross-validation results indicated that the recursive SVM model possessed higher classification accuracy (97.9%) than PLS-DA. In OCPLS model, the optimal percentage of RO, SO, CO and SUO was 2.8%, 0%, 7.2%, 0% respectively in adulterated camellia oil, which is the most similar to the authentic camellia oils. Further validation showed that five adulterated oils with the optimal percentage could be correctly identified, indicating that the OCPLS model could identify multiple adulterated oils with these four cheaper oils. Moreover, this study serves as a reference for one class classification model evaluation and a solution for multiple adulteration detection of other foods.

Bioinformatics approaches and big data analytics opportunities in improving fisheries and aquaculture

Aquaculture has witnessed an excellent growth rate during the last two decades and offers huge potential to provide nutritional as well as livelihood security. Genomic research has contributed significantly toward the development of beneficial technologies for aquaculture. The existing high throughput technologies like next-generation technologies generate oceanic data which requires extensive analysis using appropriate tools. Bioinformatics is a rapidly evolving science that involves integrating gene based information and computational technology to produce new knowledge for the benefit of aquaculture. Bioinformatics provides new opportunities as well as challenges for information and data processing in new generation aquaculture. Rapid technical advancements have opened up a world of possibilities for using current genomics to improve aquaculture performance. Understanding the genes that govern economically relevant characteristics, necessitates a significant amount of additional research. The various dimensions of data sources includes next-generation DNA sequencing, protein sequencing, RNA sequencing gene expression profiles, metabolic pathways, molecular markers, and so on. Appropriate bioinformatics tools are developed to mine the biologically relevant and commercially useful results. The purpose of this scoping review is to present various arms of diverse bioinformatics tools with special emphasis on practical translation to the aquaculture industry.

A rapid and closed-tube method based on the dual-color fluorescence loop-mediated isothermal amplification for visual detection of Atlantic salmon (Salmo salar)

Loop-mediated isothermal amplification (LAMP) visual detection based on hydroxyl naphthol blue (HNB) and SYTO 9 is often confounded by the narrow color variation window and the requirement of empirical preset of cutoff intensity value. To improve the suitability for naked-eye inspection, the present work proposed a strategy based on the fluorescence property of SYTO 9 and HNB to enlarge the contrast and a novel dual-color fluorescence LAMP (dfLAMP) assay was developed for visual detection of Atlantic salmon. Specifically, HNB of 26.25 μM, blended with SYTO 9 of 0.75-1.5 μM, was added in the mixture before amplification, producing light green fluorescence for both positive and negative samples. After amplification, green or yellow-green fluorescence was observed for positive samples, while only orange-red fluorescence emitted for negative ones, enabling an easy and rapid distinguish. The optimized dfLAMP assay has proved its specificity and can detect as little as 1 fg Atlantic salmon DNA.

Identification of Fish Species and Targeted Genetic Modifications Based on DNA Analysis: State of the Art

Food adulteration is one of the most serious problems regarding food safety and quality worldwide. Besides misleading consumers, it poses a considerable health risk associated with the potential non-labeled allergen content. Fish and fish products are one of the most expensive and widely traded commodities, which predisposes them to being adulterated. Among all fraud types, replacing high-quality or rare fish with a less valuable species predominates. Because fish differ in their allergen content, specifically the main one, parvalbumin, their replacement can endanger consumers. This underlines the need for reliable, robust control systems for fish species identification. Various methods may be used for the aforementioned purpose. DNA-based methods are favored due to the characteristics of the target molecule, DNA, which is heat resistant, and the fact that through its sequencing, several other traits, including the recognition of genetic modifications, can be determined. Thus, they are considered to be powerful tools for identifying cases of food fraud. In this review, the major DNA-based methods applicable for fish meat and product authentication and their commercial applications are discussed, the possibilities of detecting genetic modifications in fish are evaluated, and future trends are highlighted, emphasizing the need for comprehensive and regularly updated online database resources.

Bayesian Fusion Model Enhanced Codfish Classification Using Near Infrared and Raman Spectrum

In this study, a Bayesian-based decision fusion technique was developed for the first time to quickly and non-destructively identify codfish using near infrared (NIRS) and Raman spectroscopy (RS). NIRS and RS spectra from 320 codfish samples were collected, and separate partial least squares discriminant analysis (PLS-DA) models were developed to establish the relationship between the raw data and cod identity for each spectral technique. Three decision fusion methods: decision fusion, data layer or feature layer, were tested and compared. The decision fusion model based on the Bayesian algorithm (NIRS-RS-B) was developed on the optimal discrimination features of NIRS and RS data (NIRS-RS) extracted by the PLS-DA method whereas the other fusion models followed conventional, non-Bayesian approaches. The Bayesian model showed enhanced classification metrics (92% sensitivity, 98% specificity, 98% accuracy) that were significantly superior to those demonstrated by any of other two spectroscopic methods (NIRS, RS) and the two data fusion methods (data layer fused, NIRS-RS-D, or feature layer fused, NIRS-RS-F). This novel proposed approach can provide an alternative classification for codfish and potentially other food speciation cases.

The use of various statistical methods for authenticity and detection of adulteration in fish and seafood

Various methodologies including genetic analyses, morphometrics, proteomics, lipidomics, metabolomics, etc. are now used or being developed to authenticate fish and seafood. Such techniques usually lead to the generation of enormous amounts of data. The analysis and interpretation of this information can be particularly challenging. Statistical techniques are therefore commonly used to assist in analyzing these data, visualizing trends and differences and extracting conclusions. This review article aims at presenting and discussing statistical methods used in studies on fish and seafood authenticity and adulteration, allowing researchers to consider their options based on previous successes/failures but also offering some recommendations about the future of such techniques. Techniques such as PCA, AMOVA and FST statistics, that allow the differentiation of genetic groups, or techniques such as MANOVA that allow large data sets of morphometric characteristics or elemental differences to be analyzed are discussed. Furthermore, methods such as cluster analysis, DFA, CVA, CDA and heatmaps/Circos plots that allow samples to be differentiated based on their geographical origin are also reviewed and their advantages and disadvantages as found in past studies are given. Finally, mathematical simulations and modeling are presented in a detailed review of studies using them, together with their advantages and limitations.

Visual detection of rainbow trout (Oncorhynchus mykiss) and Atlantic salmon (Salmo salar) simultaneously by duplex loop-mediated isothermal amplification

Loop-mediated isothermal amplification (LAMP) is often confounded by the non-specific amplification arising from primer dimers, off-target priming, and other artifacts. Precipitation of the DNA produced during LAMP with the use of specific fluorescently labeled probe has proved the effectiveness in specific detection. Herein, two fluorophores (ROX and FAM) were attached to the primers S-LB-6 and R-FIP for Atlantic salmon and rainbow trout, respectively, which are self-quenched in unbound state and become de-quenched after binding to the dumbbell-shaped DNA specifically. The DNA precipitation and appearance of small sediment took 10 s of centrifugation at 1000 g, by adding polyethylenimine (PEI) 600. Each target species was specifically amplified with the predicted color of PEI-DNA sediment, namely red for Atlantic salmon, green for rainbow trout, and pale yellow for mixed species. The optimized duplex LAMP system has proved its specificity and can detect as little as 1 ng DNA in visual detection.

COIBar-RFLP Molecular Strategy Discriminates Species and Unveils Commercial Frauds in Fishery Products

The DNA analysis is the best approach to authenticate species in seafood products and to unveil frauds based on species substitution. In this study, a molecular strategy coupling Cytochrome Oxidase I (COI) DNA barcoding with the consolidated methodology of Restriction Fragment Length Polymorphisms (RFLPs), named COIBar-RFLP, was applied for searching pattern of restriction enzyme digestion, useful to discriminate seven different fish species (juveniles of Engraulis encrasicolus and Sardina pilchardus sold in Italy as “bianchetto” and Aphia minuta sold as “rossetto”; icefish Neosalanx tangkahkeii; European perch, Perca fluviatilis and the Nile Perch, Lates niloticus; striped catfish, Pangasianodon hypophthalmus). A total of 30 fresh and frozen samples were processed for DNA barcoding, analyzed against a barcode library of COI sequences retrieved from GenBank, and validated for COIBar–RFLP analysis. Cases of misdescription were detected: 3 samples labeled as “bianchetto” were substituted by N. tangkahkeii (2 samples) and A. minuta (1 sample); 3 samples labeled as “persico reale” (P. fluviatilis) were substituted by L. niloticus and P. hypophthalmus. All species were simultaneously discriminated through the restriction pattern obtained with MspI enzyme. The results highlighted that the COIBar-RFLP could be an effective tool to authenticate fish in seafood products by responding to the emerging interest in molecular identification technologies.

A fast HRMA tool to authenticate eight salmonid species in commercial food products

Atlantic and Pacific salmon are frequently consumed species with very different economic values: farmed Atlantic salmon is cheaper than wild-caught Pacific salmons. Species replacements occur with the high valued Pacific species (Oncorhynchus keta, O. gorbuscha, O. kisutch, O. nerka and O. tshawytscha) substituted by cheaper farmed Atlantic salmon (Salmo salar) and Atlantic salmon by rainbow trout (Oncorhynchus mykiss) and brown trout (Salmo trutta). Here we use High-Resolution Melting Analysis (HRMA) to identify eight salmonid species. We designed primers to generate short amplicons of 72 and 116 bp from the fish barcode genes CO1 and CYTB. The time of analysis was under 70 min, after DNA extraction. Food processing of Atlantic salmon (fresh, "Bellevue", "gravadlax", frozen and smoked) did not impact the HRMA profiles allowing reliable identification. A blind test was conducted by three different institutes, showing correct species identifications irrespective of the laboratory conducting the analysis. Finally, a total of 82 retail samples from three European countries were analyzed and a low substitution rate of 1.2% was found. The developed tool provides a quick way to investigate salmon fraud and contributes to safeguard consumers.

Amino Acid Profiling with Chemometric Analysis as a Feasible Tool for the Discrimination of Marine-Derived Peptide Powders

Marine-derived peptide powders have suffered from adulteration via the substitution of lower-price peptides or the addition of adulterants in the market. This study aims to establish an effective approach for the discrimination and detection of adulterants for four representative categories of marine-derived peptide powders, namely, oyster peptides, sea cucumber peptides, Antarctic krill peptides, and fish skin peptides, based on amino acid profiling alongside chemometric analysis. The principal component analysis and orthogonal partial least squares discriminant analysis results indicate that four categories of marine-derived peptides could be distinctly classified into four clusters and aggregated with the respective raw materials. Taurine, glycine, lysine, and protein contents were the major discriminants. A reliable classification model was constructed and validated by the prediction dataset, mixture sample dataset, and unclassified sample dataset with accuracy values of 100%, 100%, and 100%, respectively.