Valorisation of viscera from fish processing for food industry utilizations

Fish viscera is a valuable source of functional materials for the food industry, such as protein, oil, enzymes, protein hydrolysate, peptones, sterols, producing biodiesel, and other oleochemical industries. The major components of fish viscera are oil and protein, and their quantity depends on the fish habitats. Viscera oil from fish contains appreciable amounts of omega-3 fatty acids. The protein of fish viscera is a raw material for protein concentrates, hydrolysate, and bioactive peptides. All are valuable ingredients in food processing and product formulation. Most viscera are fish digestive tracts and organs responsible for producing enzymes, and several enzymes are found in high activity, such as lipases and proteases. The extraction of lipases and proteases from fish viscera and their utilizations have been intensively studied. Currently, the isolation of each major component from fish viscera is conducted separately. Therefore, it is challenging to obtain all valuable components from fish viscera to have a zero-waste process. This article reviews the separation of major components of fish viscera by conventional and emerging technology and the proposed simultaneous and integrative separation of all valuable major components.

Parasites prevalence which infecting freshwater fishes in Mulur Reservoir of Sukoharjo District, Indonesia

Iriansyah AH, Budiharjo A, Sugiyarto. 2020. Parasites prevalence infecting freshwater fishes in Mulur Reservoir of Sukoharjo District, Indonesia. Bonorowo Wetlands 10: 66-71. Mulur Reservoir is one of the natural fish habitats and consumption fish cultivation in Sukoharjo District, Indonesia. Excessive use of the reservoir area causes the decreasing of water quality which affects fish life sustainability, one of them can cause the fish susceptibility to infection by parasites. The purpose of this research was to identify the types of parasites which infect on consumption of fish in Mulur Reservoir and calculate the prevalence value. Samples were taken by purposive sampling for gourami (Osphronemus gouramy), tilapia (Oreochromis niloticus), betutu fish (Oxyeleotris marmorata), catfish (Clarias batrachus), and jambal (Pangasius djambal) 10 fishes for each type. The parts of fish infected by ectoparasites such as body mucus, fin mucus, head mucus, and gills were taken by scrapping and observed under a microscope with magnification between 100-400x. The results showed that 5 types of ectoparasites were Epistylis sp., Ichthyophthirius multifilis, Trichodina sp., Dactylogyrus sp., and Gyrodactylus sp. with an average prevalence of more than 50%. There are more ectoparasites in domesticated fish than in wild fish.

Weakly supervised underwater fish segmentation using affinity LCFCN

Estimating fish body measurements like length, width, and mass has received considerable research due to its potential in boosting productivity in marine and aquaculture applications. Some methods are based on manual collection of these measurements using tools like a ruler which is time consuming and labour intensive. Others rely on fully-supervised segmentation models to automatically acquire these measurements but require collecting per-pixel labels which are also time consuming. It can take up to 2 minutes per fish to acquire accurate segmentation labels. To address this problem, we propose a segmentation model that can efficiently train on images labeled with point-level supervision, where each fish is annotated with a single click. This labeling scheme takes an average of only 1 second per fish. Our model uses a fully convolutional neural network with one branch that outputs per-pixel scores and another that outputs an affinity matrix. These two outputs are aggregated using a random walk to get the final, refined per-pixel output. The whole model is trained end-to-end using the localization-based counting fully convolutional neural network (LCFCN) loss and thus we call our method Affinity-LCFCN (A-LCFCN). We conduct experiments on the DeepFish dataset, which contains several fish habitats from north-eastern Australia. The results show that A-LCFCN outperforms a fully-supervised segmentation model when the annotation budget is fixed. They also show that A-LCFCN achieves better segmentation results than LCFCN and a standard baseline.

Before and After: A Multiscale Remote Sensing Assessment of the Sinop Dam, Mato Grosso, Brazil

Hydroelectric dams are a major threat to rivers in the Amazon. They are known to decrease river connectivity, alter aquatic habitats, and emit greenhouse gases such as carbon dioxide and methane. Multiscale remotely sensed data can be used to assess and monitor hydroelectric dams over time. We analyzed the Sinop dam on the Teles Pires river from high spatial resolution satellite imagery to determine the extent of land cover inundated by its reservoir, and subsequent methane emissions from TROPOMI S-5P data. For two case study areas, we generated 3D reconstructions of important endemic fish habitats from unmanned aerial vehicle photographs. We found the reservoir flooded 189 km2 (low water) to 215 km2 (high water) beyond the extent of the Teles Pires river, with 13–30 m tall forest (131.4 Mg/ha average AGB) the predominant flooded class. We further found the reservoir to be a source of methane enhancement in the region. The 3D model showed the shallow habitat had high complexity important for ichthyofauna diversity. The distinctive habitats of rheophile fishes, and of the unique species assemblage found in the tributaries have been permanently modified following inundation. Lastly, we illustrate immersive visualization options for both the satellite imagery and 3D products.

Assessing Stream Thermal Heterogeneity and Cold-Water Patches from UAV-Based Imagery: A Matter of Classification Methods and Metrics

Understanding stream thermal heterogeneity patterns is crucial to assess and manage river resilience in light of climate change. The dual acquisition of high-resolution thermal infrared (TIR) and red–green–blue-band (RGB) imagery from unmanned aerial vehicles (UAVs) allows for the identification and characterization of thermally differentiated patches (e.g., cold-water patches—CWPs). However, a lack of harmonized CWP classification metrics (patch size and temperature thresholds) makes comparisons across studies almost impossible. Based on an existing dual UAV imagery dataset (River Ovens, Australia), we present a semi-automatic supervised approach to classify key riverscape habitats and associated thermal properties at a pixel-scale accuracy, based on spectral properties. We selected five morphologically representative reaches to (i) illustrate and test our combined classification and thermal heterogeneity assessment method, (ii) assess the changes in CWP numbers and distribution with different metric definitions, and (iii) model how climatic predictions will affect thermal habitat suitability and connectivity of a cold-adapted fish species. Our method was successfully tested, showing mean thermal differences between shaded and sun-exposed fluvial mesohabitats of up to 0.62 °C. CWP metric definitions substantially changed the number and distance between identified CWPs, and they were strongly dependent on reach morphology. Warmer scenarios illustrated a decrease in suitable fish habitats, but reach-scale morphological complexity helped sustain such habitats. Overall, this study demonstrates the importance of method and metric definitions to enable spatio-temporal comparisons between stream thermal heterogeneity studies.