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

References

• Alamprese, C, and E. Casiraghi. 2015. Application of FT-NIR and FT-IR spectroscopy to fish fillet authentication. LWT - Food Science and Technology 63 (1):720–5. doi: 10.1016/j.lwt.2015.03.021.
   Web of Science ®Google Scholar
• An, Y., Y. L. Qian, A. Alcazar Magana, S. Xiong, and M. C. Qian. 2020. Comparative characterization of aroma compounds in silver carp (Hypophthalmichthys molitrix), pacific whiting (Merluccius productus), and Alaska pollock (Theragra chalcogramma) surimi by aroma extract dilution analysis, odor activity value, and aroma recombination studies. Journal of Agricultural and Food Chemistry 68 (38):10403–13. doi: 10.1021/acs.jafc.9b07621.
   PubMed Web of Science ®Google Scholar
• Anderson, K. A., K. A. Hobbie, and B. W. Smith. 2010. Chemical profiling with modeling differentiates wild and farm-raised salmon. Journal of Agricultural and Food Chemistry 58 (22):11768–74. doi: 10.1021/jf102046b.[PMC].[20973481.
   PubMed Web of Science ®Google Scholar
• Anderson, T. W., ed. 2003. Introduction. In An introduction of multivariate statistical analysis (3rd ed.), 1–5. New Jersey, Canada: John Wiley & Sons.
   Google Scholar
• Arnold, W. S., S. L. Walters, J. S. Fajans, S. C. Peters, and T. M. Bert. 2004. Influence of congeneric aquaculture on hard clam (Mercenaria spp.) population genetic structure. Aquaculture International 12 (2):139–60. doi: 10.1023/B:AQUI.0000032078.90352.03.
   Web of Science ®Google Scholar
• Azarian, C., S. Foster, F. Devloo‐Delva, and P. Feutry. 2021. Population differentiation from environmental DNA: investigating the potential of haplotype presence/absence‐based analysis of molecular variance. Environmental DNA 3 (3):541–52. doi: 10.1002/edn3.143.
   Google Scholar
• Balázs, G., J. Vörös, B. Lewarne, and G. Herczeg. 2020. A new non‑invasive in situ underwater DNA sampling method for estimating genetic diversity. Evolutionary Ecology 34 (4):633–44. doi: 10.1007/s10682-020-10053-1.
   Web of Science ®Google Scholar
• Behrmann, K., H. Rehbein, A. von Appen, and M. Fischer. 2015. Applying population genetics for authentication of marine fish: the case of Saithe (Pollachius virens). Journal of Agricultural and Food Chemistry 63 (3):802–9. doi: 10.1021/jf506201m.
   PubMed Web of Science ®Google Scholar
• Berejikian, B. A., D. M. Van Doornik, R. C. Endicott, T. L. Hoffnagle, E. P. Tezak, M. E. Moore, and J. Atkins. 2010. Mating success of alternative male phenotypes and evidence for frequency-dependent selection in Chinook salmon, Oncorhynchus tshawytscha. Canadian Journal of Fisheries and Aquatic Sciences 67 (12):1933–41. doi: 10.1139/F10-112.
   Web of Science ®Google Scholar
• Bernatchez, L., M. Wellenreuther, C. Araneda, D. T. Ashton, J. M. I. Barth, T. D. Beacham, G. E. Maes, J. T. Martinsohn, K. M. Miller, K. A. Naish, et al. 2017. Harnessing the power of genomics to secure the future of seafood. Trends in Ecology & Evolution 32 (9):665–80. doi: 10.1016/j.tree.2017.06.010.
   PubMed Web of Science ®Google Scholar
• Bhatia, G., N. Patterson, S. Sankararaman, and A. L. Price. 2013. Estimating and interpreting FST: the impact of rare variants. Genome Research 23 (9):1514–21. doi: 10.1101/gr.154831.113.
   PubMed Web of Science ®Google Scholar
• Bhattachan, P, and B. Dong. 2019. Multivariate analysis of genomic variables, effective population size, and mutation rate. BMC Research Notes 12 (1):1–6. doi: 10.1186/s13104-019-4097-3.
   PubMedGoogle Scholar
• Black, C., O. P. Chevallier, S. A. Haughey, J. Balog, S. Stead, S. D. Pringle, M. V. Riina, F. Martucci, P. L. Acutis, M. Morris, et al. 2017. A real time metabolomic profiling approach to detecting fish fraud using rapid evaporative ionisation mass spectrometry. Metabolomics 13 (12):1–13. doi: 10.1007/s11306-017-1291-y.
   PubMed Web of Science ®Google Scholar
• Bronte, C. R, and S. A. Moore. 2007. Morphological variation of Siscowet Lake trout in lake superior. Transactions of the American Fisheries Society 136 (2):509–17. doi: 10.1577/T06-098.1.
   Web of Science ®Google Scholar
• Caselle, J. E., S. L. Hamilton, D. M. Schroeder, M. S. Love, J. D. Standish, J. A. Rosales-Casián, and O. Sosa-Nishizaki. 2011. Geographic variation in density, demography, and life history traits of a harvested, sex-changing, temperate reef fish. Canadian Journal of Fisheries and Aquatic Sciences 68 (2):288–303. doi: 10.1139/F10-140.
   Web of Science ®Google Scholar
• Chanto-García, D. A., S. Saber, D. Macías, A. Sureda, J. Hernández-Urcera, and M. Cabanellas-Reboredo. 2022. Species-specific heavy metal concentrations of tuna species: the case of Thunnus alalunga and Katsuwonus pelamis in the Western Mediterranean. Environmental Science and Pollution Research International 29 (1):1278–88. doi: 10.1007/s11356-021-15700-w.
   PubMed Web of Science ®Google Scholar
• Chatterjee, N. S., O. P. Chevallier, E. Wielogorska, C. Black, and C. T. Elliott. 2019. Simultaneous authentication of species identity and geographical origin of shrimps: untargeted metabolomics to recurrent biomarker ions. Journal of Chromatography. A 1599:75–84. doi: 10.1016/j.chroma.2019.04.001.
   PubMed Web of Science ®Google Scholar
• Chicco, D, and G. Jurman. 2020. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21 (1):6–13. doi: 10.1186/s12864-019-6413-7.
   PubMed Web of Science ®Google Scholar
• Choi, E. H., M. Y. Yeo, G. Kim, B. Park, C. R. Shin, S. Y. Baek, and U. W. Hwang. 2021. Liolophura species discrimination with geographical distribution patterns and their divergence and expansion history on the northwestern Pacific coast. Scientific Reports 11 (1):17602–17. doi: 10.1038/s41598-021-96823-5.
   PubMed Web of Science ®Google Scholar
• Christiansen, H., F. M. Heindler, B. Hellemans, Q. Jossart, F. Pasotti, H. Robert, M. Verheye, B. Danis, M. Kochzius, F. Leliaert, et al. 2021. Facilitating population genomics of non-model organisms through optimized experimental design for reduced representation sequencing. BMC Genomics 22 (1):625. doi: 10.1186/s12864-021-07917-3.
   PubMed Web of Science ®Google Scholar
• Cooke, G. M., T. E. Schlub, W. B. Sherwin, and T. J. Ord. 2016. Understanding the spatial scale of genetic connectivity at sea: unique insights from a land fish and a meta-analysis. PLoS One 11 (5):e0150991–25. doi: 10.1371/journal.pone.0150991.
   PubMed Web of Science ®Google Scholar
• Davis, R. P., C. E. Boyd, R. Godumala, A. B. C. Mohan, A. Gonzalez, N. P. Duy, P. G. Sasmita J, N. Ahyani, O. Shatova, J. Wakefield, et al. 2022. Assessing the variability and discriminatory power of elemental fingerprints in whiteleg shrimp Litopenaeus vannamei from major shrimp production countries. Food Control. 133:108589–8. doi: 10.1016/j.foodcont.2021.108589.
   Web of Science ®Google Scholar
• Dodds, W. K, and M. R. Whiles, eds. 2020. Fish ecology, fisheries, and aquaculture. In Freshwater ecology (3rd ed.), 699–722. London, UK: Elsevier Inc.
   Google Scholar
• Duarte, B., I. A. Duarte, I. Caçador, P. Reis-Santos, R. P. Vasconcelos, C. Gameiro, S. E. Tanner, and V. F. Fonseca. 2022. Elemental fingerprinting of thornback ray (Raja clavata) muscle tissue as a tracer for provenance and food safety assessment. Food Control. 133:108592. doi: 10.1016/j.foodcont.2021.108592.
   Web of Science ®Google Scholar
• Dufour, É., J. P. Frencia, and E. Kane. 2003. Development of a rapid method based on front-face fluorescence spectroscopy for the monitoring of fish freshness. Food Research International 36 (5):415–23. doi: 10.1016/S0963-9969(02)00174-6.
   Web of Science ®Google Scholar
• Exadactylos, A. 2014. Nutrigenomics in aquaculture research. Fisheries & Aquaculture Journal 5 (2):e107. doi: 10.4172/2150-3508.10000e107.
   Google Scholar
• Exadaktylos, A., D. Vafidis, C. S. Tsigenopoulos, and G. A. Gkafas. 2019. High connectivity of the White Seabream Diplodus sargus, L. 1758) in the Aegean Sea, Eastern Mediterranean Basin. Animals 9 (979):1–8. doi: 10.3390/ani9110979.
   Google Scholar
• Fard, N. J. H., M. Ravanbakhsh, Z. Ramezani, M. Ahmadi, K. A. Angali, and A. Z. Javid. 2015. Determination of mercury and vanadium concentration in Johnius belangerii (C) fish in Musa estuary in Persian Gulf. Marine Pollution Bulletin 97 (1-2):499–505. doi: 10.1016/j.marpolbul.2015.05.036.
   PubMed Web of Science ®Google Scholar
• Fard, N. J. H., A. Z. Javid, M. Ravanbakhsh, Z. Ramezani, M. Ahmadi, K. A. Angali, and S. Ardeshirzadeh. 2017. Determination of nickel and thallium concentration in Cynoglossus arel fish in Musa estuary, Persian Gulf, Iran. Environmental Science and Pollution Research International 24 (3):2936–45. doi: 10.1007/s11356-016-8055-5.
   PubMed Web of Science ®Google Scholar
• Fiorino, G. M., I. Losito, E. De Angelis, M. Arlorio, A. F. Logrieco, and L. Monaci. 2019. Assessing fish authenticity by direct analysis in real time-high resolution mass spectrometry and multivariate analysis: discrimination between wildtype and farmed salmon. Food Research International (Ottawa, Ont.) 116:1258–65. doi: 10.1016/j.foodres.2018.10.013.
   PubMed Web of Science ®Google Scholar
• Franceschini, S., E. Gandola, M. Martinoli, L. Tancioni, and M. Scardi. 2018. Cascaded neural networks improving fish species prediction accuracy: the role of the biotic information. Scientific Reports 8 (1):1–12. doi: 10.1038/s41598-018-22761-4.
   PubMed Web of Science ®Google Scholar
• Frichot, E, and O. François. 2015. LEA: an R package for landscape and ecological association studies. Methods in Ecology and Evolution 6 (8):925–9. doi: 10.1111/2041-210X.12382.
   Web of Science ®Google Scholar
• Gain, C, and O. François. 2021. LEA 3: factor models in population genetics and ecological genomics with R. Molecular Ecology Resources 21 (8):2738–48. doi: 10.1111/1755-0998.13366.
   PubMed Web of Science ®Google Scholar
• Gamboa-Delgado, J., C. Molina-Poveda, D. E. Godínez-Siordia, D. Villarreal-Cavazos, D. Ricque-Marie, and L. E. Cruz-Suárez. 2014. Application of stable isotope analysis to differentiate shrimp extracted by industrial fishing or produced through aquaculture practices. Canadian Journal of Fisheries and Aquatic Sciences 71 (10):1520–8. doi: 10.1139/cjfas-2014-0005.
   Web of Science ®Google Scholar
• González, S., F. Álvarez-Valin, and J. E. Maldonado. 2002. Morphometric differentiation of endangered pampas deer (Ozotoceros bezoarticus), with description of new subspecies from Uruguay. Journal of Mammalogy 83 (4):1127–40. doi: 10.1644/1545-1542(2002)083 < 1127:MDOEPD>2.0.CO;2.
   Web of Science ®Google Scholar
• Gonzalez-Martinez, A., M. Lopez, H. M. Molero, J. Rodriguez, M. González, C. Barba, and A. García. 2020. Morphometric and meristic characterization of native chame fish (Dormitator latifrons) in Ecuador using multivariate analysis. Animals 10 (10):1805–16. doi: 10.3390/ani10101805.
   Web of Science ®Google Scholar
• Grazina, L., P. J. Rodrigues, G. Igrejas, M. A. Nunes, I. Mafra, M. Arlorio, M. Oliveira, and J. S. Amaral. 2020. Machine learning approaches applied to GC-FID fatty acid profiles to discriminate wild from farmed salmon. Foods 9 (11):1622–15. doi: 10.3390/foods9111622.
   PubMed Web of Science ®Google Scholar
• Guo, X., R. Cai, S. Wang, B. Tang, Y. Li, and W. Zhao. 2018. Non-destructive geographical traceability of sea cucumber (Apostichopus japonicus) using near infrared spectroscopy combined with chemometric methods. Royal Society Open Science 5 (1):170714–2. doi: http://dx.doi.org/10.1098/rsos.170714.
   PubMed Web of Science ®Google Scholar
• Han, C., S. Dong, L. Li, Q. Gao, and Y. Zhou. 2021. Evaluation of elemental analysis assisted by chemometrics for authenticating production methods and geographical origins of salmonids. Aquaculture 545:737210–8. doi: 10.1016/j.aquaculture.2021.737210.
   Web of Science ®Google Scholar
• Han, C., S. Dong, L. Li, F. Wei, Y. Zhou, and Q. Gao. 2020. The effect of the seasons on geographical traceability of salmonid based on multi-element analysis. Food Control. 109:106893–10. doi: 10.1016/j.foodcont.2019.106893.
   Web of Science ®Google Scholar
• Haniffa, M. A., M. Nagarajan, A. Gopalakrishnan, and K. K. Musammilu. 2007. Allozyme variation in a threatened freshwater fish, spotted murrel (Channa punctatus) in a South Indian river system. Biochemical Genetics 45 (3-4):363–74. doi: 10.1007/s10528-007-9080-3.
   PubMed Web of Science ®Google Scholar
• Holland, S. M. 2008. Non-metric multidimensional scaling (MDS), 1–7. Athens, GA: Department of Geology, University of Georgia. https://strata.uga.edu/software/pdf/mdsTutorial.pdf.
   Google Scholar
• Hu, W., X. X. Guo, X. C. Wang, Y. Zhao, S. Q. Sun, C. H. Xu, and Y. Liu. 2016. Rapid discrimination of different grades of white croaker surimi by tri-step infrared spectroscopy combined with soft independent modeling of class analogy (SIMCA). Food Analytical Methods 9 (4):831–9. doi: 10.1007/s12161-015-0258-8.
   Web of Science ®Google Scholar
• Janse, R. J., T. Hoekstra, K. J. Jager, C. Zoccali, G. Tripepi, F. W. Dekker, and M. van Diepen. 2021. Conducting correlation analysis: important limitations and pitfalls. Clinical Kidney Journal 14 (11):2332–7. doi: 10.1093/ckj/sfab085.
   PubMed Web of Science ®Google Scholar
• Johnson, R. A., and D. W. Wichern, eds. 2007. Aspects of multivariate analysis. In Applied multivariate statistical analysis, 1–48. Hoboken, NJ: Pearson Prentice Hall.
   Google Scholar
• Jones, R. C, and C. C. Clark. 1987. Impact of watershed urbanization on stream insect communities. Journal of the American Water Resources Association 23 (6):1047–55. doi: 10.1111/j.1752-1688.1987.tb00854.x.
   Google Scholar
• Karoui, R., A. Hassoun, and P. Ethuin. 2017. Front face fluorescence spectroscopy enables rapid differentiation of fresh and frozen-thawed sea bass (Dicentrarchus labrax) fillets. Journal of Food Engineering 202:89–98. doi: 10.1016/j.jfoodeng.2017.01.018.
   Web of Science ®Google Scholar
• Katano, O., K. Hosoya, K. Iguchi, M. Yamaguchi, Y. Aonuma, and S. Kitano. 2003. Species diversity and abundance of freshwater fishes in irrigation ditches around rice fields. Environmental Biology of Fishes 66 (2):107–21. doi: 10.1023/A:1023678401886.
   Web of Science ®Google Scholar
• Kotsanopoulos, K. V., A. Exadactylos, G. A. Gkafas, P. V. Martsikalis, F. F. Parlapani, I. S. Boziaris, and I. S. Arvanitoyannis. 2021. The use of molecular markers in the verification of fish and seafood authenticity and the detection of adulteration. Comprehensive Reviews in Food Science and Food Safety 20 (2):1584–654. doi: 10.1111/1541-4337.12719.
   PubMed Web of Science ®Google Scholar
• Kumar, B. V., A. Mahalanobis, and R. D. Juday. 2005. Correlation pattern recognition. Vancouver: Cambridge University Press.
   Google Scholar
• Lewandowski, D., R. M. Cooke, and R. Tebbens. 2007. Sample-based estimation of correlation ratio with polynomial approximation. ACM Transactions on Modeling and Computer Simulation 18 (1):1–16. doi: 10.1145/1315575.1315578.
   Web of Science ®Google Scholar
• Li, L., C. E. Boyd, and S. Dong. 2015. Chemical profiling with modeling differentiates Ictalurid catfish produced in fertilized and feeding ponds. Food Control. 50:18–22. doi: 10.1016/j.foodcont.2014.08.014.
   Web of Science ®Google Scholar
• Li, L., C. E. Boyd, and J. Odom. 2014. Identification of Pacific white shrimp (Litopenaeus vannamei) to rearing location using elemental profiling. Food Control. 45:70–5. doi: 10.1016/j.foodcont.2014.03.013.
   Web of Science ®Google Scholar
• Li, L., C. Han, S. Dong, and C. E. Boyd. 2019. Use of elemental profiling and isotopic signatures to differentiate Pacific white shrimp (Litopenaeus vannamei) from freshwater and seawater culture areas. Food Control. 95:249–56. doi: 10.1016/j.foodcont.2018.08.015.
   Web of Science ®Google Scholar
• Lishchenko, F, and B. Jones. 2021. Application of shape analyses to recording structures of marine organisms for stock discrimination and taxonomic purposes. Frontiers in Marine Science 8:1–26. doi: 10.3389/fmars.2021.667183.
   PubMed Web of Science ®Google Scholar
• Liu, X., J. S. Teixeira, S. Ner, K. V. Ma, N. Petronella, S. Banerjee, and J. Ronholm. 2020. Exploring the potential of the microbiome as a marker of the geographic origin of fresh seafood. Frontiers in Microbiology 11 (696):1–9. doi: 10.3389/fmicb.2020.00696.
   PubMedGoogle Scholar
• Liu, X., L. Zhang, L. You, J. Yu, M. Cong, Q. Wang, F. Li, L. Li, J. Zhao, C. Li, et al. 2011. Assessment of Clam Ruditapes philippinarum as heavy metal bioindicators using NMR based metabolomics. CLEAN - Soil, Air, Water 39 (8):759–66. doi: 10.1002/clen.201000410.
   Web of Science ®Google Scholar
• López, M. I., N. Colomer, I. Ruisánchez, and M. P. Callao. 2014. Validation of multivariate screening methodology. Case study: detection of food fraud. Analytica Chimica Acta 827:28–33. doi: 10.1016/j.aca.2014.04.019.
   PubMed Web of Science ®Google Scholar
• Lourie, S. A, and A. C. J. Vincent. 2004. Using biogeography to help set priorities in marine conservation. Conservation Biology 18 (4):1004–20. doi: https://www.jstor.org/stable/3589174.
   Web of Science ®Google Scholar
• Lu, C.-P., B. L. Smith, M. G. Hinton, and J. R. A. Bremer. 2016. Bayesian analyses of Pacific swordfish (Xiphias gladius L.) genetic differentiation using multilocus single nucleotide polymorphism (SNP) data. Journal of Experimental Marine Biology and Ecology 482:1–17. doi: 10.1016/j.jembe.2016.03.010.
   Web of Science ®Google Scholar
• Maluly, H. D. B., A. Porcari, I. Cunha, M. T. B. Pacheco, M. N. Eberlin, and R. M. Alberici. 2019. The impacts of the raising regime of Salmon species on their triacylglycerol composition revealed by easy ambient sonic-spray ionization mass spectrometry. Food Research International (Ottawa, Ont.) 120:19–25. doi: 10.1016/j.foodres.2019.01.066.
   PubMed Web of Science ®Google Scholar
• Marchetti, P., A. Mottola, R. Piredda, G. Ciccarese, and A. Di Pinto. 2020. Determining the authenticity of shark meat products by DNA sequencing. Foods 9 (9):1194. doi: 10.3390/foods9091194.
   PubMed Web of Science ®Google Scholar
• Martsikalis, P. V., M. Kavouras, G. A. Gkafas, A. P. Apostolidis, I. S. Boziaris, and A. Exadactylos. 2018. Morphological and free amino acid profile variability, as a tool for stock identification among farmed rainbow trout Oncorhynchus mykiss of different origin. Aquaculture Research 49 (2):621–30. doi: 10.1111/are.13491.
   Web of Science ®Google Scholar
• Massaro, A., R. Stella, A. Negro, M. Bragolusi, B. Miano, G. Arcangeli, G. Biancotto, R. Piro, and A. Tata. 2021. New strategies for the differentiation of fresh and frozen/thawed fish: a rapid and accurate non-targeted method by ambient mass spectrometry and data fusion (part A). Food Control. 130:108364. doi: 10.1016/j.foodcont.2021.108364.
   Web of Science ®Google Scholar
• McClelland, M. A, and G. G. Sass. 2012. Assessing fish collections from random and fixed site sampling methods on the Illinois River. Journal of Freshwater Ecology 27 (3):325–33. doi: 10.1080/02705060.2012.658213.
   Web of Science ®Google Scholar
• Miller, K., B. Alvarez, C. Battershill, P. Northcote, and H. Parthasarathy. 2001. Genetic, morphological, and chemical divergence in the sponge genus Latrunculia (Porifera: Demospongiae) from New Zealand. Marine Biology 139 (2):235–50. doi: 10.1007/s002270100574.
   Web of Science ®Google Scholar
• Mojekwu, T. O, and C. I. Anumudu. 2015. Advanced techniques for morphometric analysis in fish. Journal of Aquaculture Research and Development 6 (8):1–6. doi: 10.4172/2155-9546.1000354.
   Google Scholar
• Morgan-Richards, M., M. Bulgarella, L. Sivyer, E. J. Dowle, M. Hale, N. E. McKean, and S. A. Trewick. 2017. Explaining large mitochondrial sequence differences within a population sample. Royal Society Open Science 4 (11):170730–13. doi: 10.1098/rsos.170730.
   PubMed Web of Science ®Google Scholar
• Mounir, A., A. Ewague, M. Znari, and H. Elmghazli. 2019. Discrimination of the phenotypic sardine Sardina pilchardus stocks off the Moroccan Atlantic coast using a morphometric analysis. African Journal of Marine Science 41 (2):137–44. doi: 10.2989/1814232X.2019.1597765.
   Web of Science ®Google Scholar
• Naccarato, A., E. Furia, G. Sindona, and A. Tagarelli. 2016. Multivariate class modeling techniques applied to multielement analysis for the verification of the geographical origin of chili pepper. Food Chemistry 206:217–22. doi: 10.1016/j.foodchem.2016.03.072.
   PubMed Web of Science ®Google Scholar
• Nantón, A., A. Arias-Pérez, R. Freire, J. Fernández-Pérez, S. Nóvoa, and J. Méndez. 2017. Microsatellite variation in Donax trunculus from the Iberian Peninsula, with particular attention to Galician estuaries (NW Spain. )Estuarine, Coastal and Shelf Science 197:27–34. doi: 10.1016/j.ecss.2017.08.011.
   Web of Science ®Google Scholar
• Nikolaou, M., N. Neofitou, K. Skordas, I. Castritsi-Catharios, and L. Tziantziou. 2014. Fish farming and anti-fouling paints: a potential source of Cu and Zn in farmed fish. Aquaculture Environment Interactions 5 (2):163–71. doi: 10.3354/AEI00101.
   Web of Science ®Google Scholar
• Onogi, 2019. Comparison of F-tests for univariate and multivariate mixed-effect models in genome-wide association mapping. Frontiers in Genetics 10:1–8. doi: 10.3389/fgene.2019.00030.
   PubMed Web of Science ®Google Scholar
• Ortea, I, and J. M. Gallardo. 2015. Investigation of production method, geographical origin and species authentication in commercially relevant shrimps using stable isotope ratio and/or multi-element analyses combined with chemometrics: an exploratory analysis. Food Chemistry 170:145–53. doi: 10.1016/j.foodchem.2014.08.049.
   PubMed Web of Science ®Google Scholar
• Ouyang, X., J. Gao, M. Xie, B. Liu, L. Zhou, B. Chen, J. Jourdan, R. Riesch, and M. Plath. 2018. Natural and sexual selection drive multivariate phenotypic divergence along climatic gradients in an invasive fish. Scientific Reports 8 (1):11164–22. doi: 10.1038/s41598-018-29254-4.
   PubMedGoogle Scholar
• Palmer, M., G. X. Pons, and M. Linde. 2004. Discriminating between geographical groups of a Mediterranean commercial clam (Chamelea gallina (L.): Veneridae) by shape analysis. Fisheries Research 67 (1):93–8. doi: 10.1016/j.fishres.2003.07.006.
   Web of Science ®Google Scholar
• Picone, G., S. B. Engelsen, F. Savorani, S. Testi, A. Badiani, and F. Capozzi. 2011. Metabolomics as a powerful tool for molecular quality assessment of the fish Sparus aurata. Nutrients 3 (2):212–27. doi: 10.3390/nu3020212.
   PubMed Web of Science ®Google Scholar
• Pimentel, T., J. Marcelino, F. Ricardo, A. Soares, and R. Calado. 2017. Bacterial communities 16S rDNA fingerprinting as a potential tracing tool for cultured seabass Dicentrarchus labrax. Scientific Reports 7 (1):1–10. doi: 10.1038/s41598-017-11552-y.
   PubMedGoogle Scholar
• Poćwierz-Kotus, A., A. Kijewska, C. Petereit, R. Bernaś, B. Więcaszek, M. Arnyasi, S. Lien, M. P. Kent, and R. Wenne. 2015. Genetic differentiation of brackish water populations of cod Gadus morhua in the southern Baltic, inferred from genotyping using SNP-arrays. Marine Genomics 19:17–22. doi: 10.1016/j.margen.2014.05.010.
   PubMed Web of Science ®Google Scholar
• Pukk, L., R. Gross, M. Vetemaa, and A. Vasem.gi. 2016. Genetic discrimination of brackish and freshwater populations of Eurasian perch (Perca fluviatilis L.) in the Baltic Sea drainage: implications for fish forensics. Fisheries Research 183:155–64. doi: 10.1016/j.fishres.2016.05.027.
   Web of Science ®Google Scholar
• Raykov, T, and G. A. Marcoulides, eds. 2008. Introduction to multivariate statistics. In An introduction of applied multivariate analysis, 1–30. New York, NY: Routledge Taylor and Frances Group.
   Google Scholar
• Refsgaard, H. H. F., P. B. Brockhoff, and B. Jensen. 1998. Sensory and chemical changes in farmed atlantic salmon (Salmo salar) during frozen storage. Journal of Agricultural and Food Chemistry 46 (9):3473–9. doi: 10.1021/jf980309q.
   Web of Science ®Google Scholar
• Schönhuth, S., G. Luikart, and I. Doadrio. 2003. Effects of a founder event and supplementary introductions on genetic variation in a captive breeding population of the endangered Spanish killifish. Journal of Fish Biology 63 (6):1538–51. doi: 10.1111/j.1095-8649.2003.00265.x.
   Web of Science ®Google Scholar
• Shao, Y., J. Wang, Y. Qiao, Y. He, and W. Cao. 2007. Morphological variability between wild populations and inbred stocks of a Chinese minnow, Gobiocypris rarus. Zoological Science 24 (11):1094–102. doi: 10.2108/zsj.24.1094.
   PubMed Web of Science ®Google Scholar
• Shen, Q., G. Song, Q. Zhao, P. Wang, H. Yang, J. Xue, H. Wang, Y. Cui, and H. Wang. 2022. Detection of lipidomics characterization of tuna meat during different wet-aging stages using iKnife rapid evaporative ionization mass spectrometry. Food Research International 156:111307. doi: 10.1016/j.foodres.2022.111307.
   PubMed Web of Science ®Google Scholar
• Taboada, L., A. Sánchez, and C. G. Sotelo. 2017. A new real-time PCR method for rapid and specific detection of ling (Molvamolva). Food Chemistry 228:469–75. doi: 10.1016/j.foodchem.2017.01.117.
   PubMed Web of Science ®Google Scholar
• Thibert-Plante, X., K. Praebel, K. Østbye, K. K. Kahilainen, P.-A. Amundsen, and S. Gavrilets. 2020. Using mathematical modelling to investigate the adaptive divergence of whitefish in Fennoscandia. Scientific Reports 10 (1):7394–12. doi: 10.1038/s41598-020-63684-3.
   PubMed Web of Science ®Google Scholar
• Truonghuynh, H. T., B. Li, and G. K. Jaganathan. 2020. Differentiate the fatty acids profiles of large yellow croaker (Pseudosciaena crocea) fed with trash, commercial and formulated diets. International Journal of Fisheries and Aquatic Studies 8 (3):292–7.
   Google Scholar
• Uddin, M., E. Okazaki, S. Turza, Y. Yumiko, M. Tanaka, and Y. Fukuda. 2005. Non‐destructive visible/NIR spectroscopy for differentiation of fresh and frozen‐thawed fish. Journal of Food Science 70 (8):c506–10. doi: 10.1111/j.1365-2621.2005.tb11509.x.
   Web of Science ®Google Scholar
• Vajargah, M. F., M. H. Gerami, M. Sattari, and A. Hedayati. 2020. Morphological analysis of Alburnoides samiii from Toolkhone river, Guilan province, south Caspian Sea basin. Iranian Journal of Fisheries Sciences 19 (2):1015–23. doi: 10.22092/IJFS.2019.118130.
   Web of Science ®Google Scholar
• Velasco, A., G. Ramilo-Fernández, and C. G. Sotelo. 2020. A real-time PCR method for the authentication of common cuttlefish (Sepia officinalis) in food products. Foods 9 (3):286. doi: 10.3390/foods9030286.
   PubMed Web of Science ®Google Scholar
• Vella, A., N. Vella, F. S. Karakulak, I. Oray, S. Garcia-Tiscar, and R. de Stephanis. 2016. Population genetics of Atlantic bluefin tuna, Thunnus thynnus (Linnaeus, 1758), in the Mediterranean: implications for its conservation management. Journal of Applied Ichthyology 32 (3):523–31. doi: 10.1111/jai.13035.
   Web of Science ®Google Scholar
• Venables, W. N, and C. M. Dichmont. 2004. GLMs, GAMs and GLMMs: an overview of theory for applications in fisheries research. Fisheries Research 70 (2-3):319–37. doi: 10.1016/j.fishres.2004.08.011.
   Web of Science ®Google Scholar
• Wang, X, and A. Ma. 2020. Dynamic genetic analysis for body weight and main length ratio in turbot Scophthalmus maximus. Acta Oceanologica Sinica 39 (2):22–7. doi: 10.1007/s13131-020-1551-y.
   Web of Science ®Google Scholar
• Wang, Y. V., A. H. L. Wan, E.-J. Lock, N. Andersen, C. Winter-Schuh, and T. Larsen. 2018. Know your fish: a novel compound-specific isotope approach for tracing wild and farmed salmon. Food Chemistry 256:380–9. doi: 10.1016/j.foodchem.2018.02.095.
   PubMed Web of Science ®Google Scholar
• Wang, W. L., W. H. Chen, H. Y. Tian, and Y. Liu. 2018. Detection of frozen-thawed cycles for frozen tilapia (oreochromis) fillets using near infrared spectroscopy. Journal of Aquatic Food Product Technology 27 (5):609–18. doi: 10.1080/10498850.2018.1461156.
   Web of Science ®Google Scholar
• Wang, C., H. Bi, and J. Xie. 2020. Visualization of the distance among fishes by MALDI MS for rapid determination of the taxonomic status of fish fillets. Journal of Agricultural and Food Chemistry 68 (31):8438–46. doi: 10.1021/acs.jafc.0c01291.
   PubMed Web of Science ®Google Scholar
• Wold, S., K. Esbensen, and P. Geladi. 1987. Principal component analysis. Chemometrics and Intelligent Laboratory Systems 2 (1–3):37–52. doi: 10.1016/0169-7439(87)80084-9.
   Web of Science ®Google Scholar
• Wu, J., W. Wang, D. Deng, K. Zhang, S. Peng, X. Xu, Y. Zhang, and Z. Zhou. 2019. Genetic diversity and phylogeography of Daphnia similoides sinensis located in the middle and lower reaches of the Yangtze River. Ecology and Evolution 9 (8):4362–72. doi: 10.1002/ece3.4880.
   PubMed Web of Science ®Google Scholar
• Xu, T., J. Sun, J. Lv, H. K. Watanabe, T. Li, W. Zou, G. W. Rouse, S. Wang, P.-Y. Qian, Z. Bao, et al. 2017. Genome-wide discovery of single nucleotide polymorphisms (SNPs) and single nucleotide variants (SNVs) in deep-sea mussels: potential use in population genomics and cross-species application. Deep Sea Research Part II: Topical Studies in Oceanography 137:318–26. doi: 10.1016/j.dsr2.2016.03.011.
   Web of Science ®Google Scholar
• Yu, X., L. Li, H. Wang, G. Song, J. Wang, S. Li, Y. Wang, and Q. Shen. 2020. Lipidomics study of rainbow trout (Oncorhynchus mykiss) and salmons (Oncorhynchus tshawytscha and Salmo salar) using hydrophilic interaction chromatography and mass spectrometry. Lwt 121:108988–10. doi: 10.1016/j.lwt.2019.108988.
   Web of Science ®Google Scholar