Filipe Soares , Andreia Raposo , Rodrigo Mendes , Marina Azevedo , Jorge Dias , Ana Nobre , Luís E.C. Conceição , Tomé Silva
{"title":"ficoEst–一种估计养殖鱼类身体成分的工具","authors":"Filipe Soares , Andreia Raposo , Rodrigo Mendes , Marina Azevedo , Jorge Dias , Ana Nobre , Luís E.C. Conceição , Tomé Silva","doi":"10.1016/j.aquaeng.2023.102364","DOIUrl":null,"url":null,"abstract":"<div><p>ficoEst – Fish Composition Estimator is a public web tool to estimate the whole-body proximate composition of farmed fish (<span>https://webtools.sparos.pt/ficoest/</span><svg><path></path></svg><span>). The tool was designed for researchers in fish nutrition and fish farmers, and is available for six commercially relevant species: gilthead seabream (</span><span><em>Sparus aurata</em></span>), European seabass (<span><em>Dicentrarchus labrax</em></span>), meagre (<span><em>Argyrosomus regius</em></span>), rainbow trout (<em>Onchorhynchus mykiss</em>), Atlantic salmon (<span><em>Salmo salar</em></span><span>), and Nile tilapia (</span><span><em>Oreochromis niloticus</em></span>). ficoEst uses three different types of mathematical models (BC1, BC2, and BC3) to estimate the body composition of fish in terms of crude protein, crude lipids, water, ash, phosphorus, and energy. The models differ in the input data used to perform the estimates. BC1 models consider only body weight, BC2 models consider both body weight and water, and BC3 models consider body weight, water, and ash as inputs. The model evaluation results demonstrate that considering water and ash as additional inputs to body weight (BC3 models) significantly improves the accuracy in predicting some body composition components, such as crude lipids (e.g., up to 67.9 % and 28.1 % more accurate, compared to BC1 and BC2 models, respectively, depending on the species considered). ficoEst can be used as a complementary tool to analytical methods to obtain additional information about fish body composition. As a public web tool, ficoEst has the potential to be a valuable resource for researchers and fish farmers interested in estimating the body composition of farmed fish.</p></div>","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"103 ","pages":"Article 102364"},"PeriodicalIF":3.6000,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ficoEst – a tool to estimate the body composition of farmed fish\",\"authors\":\"Filipe Soares , Andreia Raposo , Rodrigo Mendes , Marina Azevedo , Jorge Dias , Ana Nobre , Luís E.C. Conceição , Tomé Silva\",\"doi\":\"10.1016/j.aquaeng.2023.102364\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>ficoEst – Fish Composition Estimator is a public web tool to estimate the whole-body proximate composition of farmed fish (<span>https://webtools.sparos.pt/ficoest/</span><svg><path></path></svg><span>). The tool was designed for researchers in fish nutrition and fish farmers, and is available for six commercially relevant species: gilthead seabream (</span><span><em>Sparus aurata</em></span>), European seabass (<span><em>Dicentrarchus labrax</em></span>), meagre (<span><em>Argyrosomus regius</em></span>), rainbow trout (<em>Onchorhynchus mykiss</em>), Atlantic salmon (<span><em>Salmo salar</em></span><span>), and Nile tilapia (</span><span><em>Oreochromis niloticus</em></span>). ficoEst uses three different types of mathematical models (BC1, BC2, and BC3) to estimate the body composition of fish in terms of crude protein, crude lipids, water, ash, phosphorus, and energy. The models differ in the input data used to perform the estimates. BC1 models consider only body weight, BC2 models consider both body weight and water, and BC3 models consider body weight, water, and ash as inputs. The model evaluation results demonstrate that considering water and ash as additional inputs to body weight (BC3 models) significantly improves the accuracy in predicting some body composition components, such as crude lipids (e.g., up to 67.9 % and 28.1 % more accurate, compared to BC1 and BC2 models, respectively, depending on the species considered). ficoEst can be used as a complementary tool to analytical methods to obtain additional information about fish body composition. As a public web tool, ficoEst has the potential to be a valuable resource for researchers and fish farmers interested in estimating the body composition of farmed fish.</p></div>\",\"PeriodicalId\":8120,\"journal\":{\"name\":\"Aquacultural Engineering\",\"volume\":\"103 \",\"pages\":\"Article 102364\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2023-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Aquacultural Engineering\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0144860923000511\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AGRICULTURAL ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Aquacultural Engineering","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0144860923000511","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
ficoEst – a tool to estimate the body composition of farmed fish
ficoEst – Fish Composition Estimator is a public web tool to estimate the whole-body proximate composition of farmed fish (https://webtools.sparos.pt/ficoest/). The tool was designed for researchers in fish nutrition and fish farmers, and is available for six commercially relevant species: gilthead seabream (Sparus aurata), European seabass (Dicentrarchus labrax), meagre (Argyrosomus regius), rainbow trout (Onchorhynchus mykiss), Atlantic salmon (Salmo salar), and Nile tilapia (Oreochromis niloticus). ficoEst uses three different types of mathematical models (BC1, BC2, and BC3) to estimate the body composition of fish in terms of crude protein, crude lipids, water, ash, phosphorus, and energy. The models differ in the input data used to perform the estimates. BC1 models consider only body weight, BC2 models consider both body weight and water, and BC3 models consider body weight, water, and ash as inputs. The model evaluation results demonstrate that considering water and ash as additional inputs to body weight (BC3 models) significantly improves the accuracy in predicting some body composition components, such as crude lipids (e.g., up to 67.9 % and 28.1 % more accurate, compared to BC1 and BC2 models, respectively, depending on the species considered). ficoEst can be used as a complementary tool to analytical methods to obtain additional information about fish body composition. As a public web tool, ficoEst has the potential to be a valuable resource for researchers and fish farmers interested in estimating the body composition of farmed fish.
期刊介绍:
Aquacultural Engineering is concerned with the design and development of effective aquacultural systems for marine and freshwater facilities. The journal aims to apply the knowledge gained from basic research which potentially can be translated into commercial operations.
Problems of scale-up and application of research data involve many parameters, both physical and biological, making it difficult to anticipate the interaction between the unit processes and the cultured animals. Aquacultural Engineering aims to develop this bioengineering interface for aquaculture and welcomes contributions in the following areas:
– Engineering and design of aquaculture facilities
– Engineering-based research studies
– Construction experience and techniques
– In-service experience, commissioning, operation
– Materials selection and their uses
– Quantification of biological data and constraints