Environmental DNA (eDNA) Metabarcoding in the Fish Market and Nearby Seafood Restaurants in Taiwan Reveals the Underestimation of Fish Species Diversity in Seafood

Seafood, especially in traditional food Taiwan, is rarely sourced from a fixed species and routinely from similar species depending on their availability. Hence, the species composition of seafood can be complicated. While a DNA-based approach has been routinely utilized for species identification, a large scale of seafood identification in fish markets and restaurants could be challenging (e.g., elevated cost and time-consuming only for a limited number of species identification). In the present study, we aimed to identify the majority of fish species potentially consumed in fish markets and nearby seafood restaurants using environmental DNA (eDNA) metabarcoding. Four eDNA samplings from a local fish market and nearby seafood restaurants were conducted using Sterivex cartridges. Nineteen universal primers previously validated for fish species identification were utilized to amplify the fragments of mitochondrial DNA (12S, COI, ND5) of species in eDNA samples and sequenced with NovaSeq 6000 sequencing. A total of 153 fish species have been identified based on 417 fish related operational taxonomic units (OTUs) generated from 50,534,995 reads. Principal Coordinate Analysis (PCoA) further showed the differences in fish species between the sampling times and sampling sites. Of these fish species, 22 chondrichthyan fish, 14 Anguilliformes species, and 15 Serranidae species were respectively associated with smoked sharks, braised moray eels, and grouper fish soups. To our best knowledge, this work represents the first study to demonstrate the feasibility of a large scale of seafood identification using eDNA metabarcoding approach. Our findings also imply the species diversity in traditional seafood might be seriously underestimated and crucial for the conservation and management of marine resources.

Performance Comparison of Convolutional Neural Network-based model using Gradient Descent Optimization algorithms for the Classification of Low Quality Underwater Images

Underwater imagery and analysis plays a major role in fisheries management and fisheries science helping developing efficient and automated tools for cumbersome tasks such as fish species identification, stock assessment and abundance estimation. Majority of the existing tools for analysis still leverage conventional statistical algorithms and handcrafted image processing techniques which demand human interventions and are inefficient and prone to human errors. Computer vision based automated algorithms need a better generalisation capability and should be made efficient to address the ambiguities present in the underwater scenarios, and can be achieved through learning based algorithms based on artificial neural networks. This paper research about utilising the Convolutional Neural Network (CNN) based models for under water image classification for fish species identification. This paper also analyses and evaluates the performance of the proposed CNN models with different optimizers such as the Stochastic Gradient Descent (SGD),Adagrad, RMSprop, Adadelta, Adam and Nadam on classifying ten classes of images from the Fish4Knowledge(F4K) database.

METHODS OF MOLECULAR DIAGNOSTICS FOR FISH SPECIES IDENTIFICATION

The growth in demand for fish products as a result of globalization of trade caused a risks and threats of selling poor-quality and falsified fish products. This has become a great problem both for supervising agencies and for consumers.Many countries have regulations on food labelling and safety. For example, in the Russian Federation, Republic of Belarus and Republic of Kazakhstan has been passed the Technical Regulation of the Customs Union TR CU022/2011 “Food products in part of their labeling” that aims to prevent misinformation of consumers to ensuring realization of consumer rights to reliable information about food products, and Technical Regulation TR EAEU040/2016 “On safety of fish and fish products” requires indication of the zoological name of the species of the aquatic biological resource or the object of aquaculture.Fish species identification is traditionally carried out based on external morphological traits. However, it becomes impossible to identify species by ichthyological traits upon fish cutting, if the head and fins are removed, and the body is cut on pieces (especially, in case of fillets) and even more so upon technological processing. In this case, objective analytical methods of species identification are used, which are based on ELISA or PCR. However, DNA‑based methods have several advantages compared to ELISA methods and complement traditional morphological identification methods. This paper gives a wide overview of the most recent and used methods of fish species identification based on DNA analysis such as single-strand conformation polymorphism (SSCP) analysis, species-specific PCR, real-time PCR, polymerase chain reaction-restriction fragment length polymorphism analysis (PCR-RFLP), DNA barcoding, Sanger sequencing and next-generation sequencing (NGS).

Analysis of color feature extraction techniques for Fish Species Identification

Color recognition is an important step for computer vision to be able to recognize objects in the most different environmental conditions. Classifying objects by color using computer vision is a good alternative for different color conditions such as the aquarium. In which it is possible to use resources of a smartphone with real-time image classification applications. This paper presents some experimental results regarding the use of five different feature extraction techniques to the problem of fish species identification. The feature extractors tested are the Bag of Visual Words (BoVW), the Bag of Colors (BoC), the Bag of Features and Colors (BoFC), the Bag of Colored Words (BoCW), and the histograms HSV and RGB color spaces. The experiments were performed using a dataset, which is also a contribution of this work, containing 1120 images from fishes of 28 different species. The feature extractors were tested under three different supervised learning setups based on Decision Trees, K-Nearest Neighbors, and Support Vector Machine. From the attribute extraction techniques described, the best performance was BoC using the Support Vector Machines as a classifier with an FMeasure of 0.90 and AUC of 0.983348 with a dictionary size of 2048.

A Rapid Method for the Identification of Fresh and Processed Pagellus erythrinus Species against Frauds

The commercialization of porgies or seabreams of the family Sparidae has greatly increased in the last decade, and some valuable species have become subject to seafood substitution. DNA regions currently used for fish species identification in fresh and processed products belong to the mitochondrial (mt) genes cytochrome b (Cytb), cytochrome c oxidase I (COI), 16S and 12S. However, these markers amplify for fragments with lower divergence within and between some species, failing to provide informative barcodes. We adopted comparative mitogenomics, through the analysis of complete mtDNA sequences, as a compatible approach toward studying new barcoding markers. The intent is to develop a specific and rapid assay for the identification of the common pandora Pagellus erythrinus, a sparid species frequently subject to fraudulent replacement. The genetic diversity analysis (Hamming distance, p-genetic distance, gene-by-gene sequence variability) between 16 sparid mtDNA genomes highlighted the discriminating potential of a 291 bp NAD2 gene fragment. A pair of species-specific primers were successfully designed and tested by end-point and real-time PCR, achieving amplification only in P. erythrinus among several fish species. The use of the NAD2 barcoding marker provides a rapid presence/absence method for the identification of P. erythrinus.