Abstract Aquaculture requires the monitoring of several parameters simultaneously such as temperature, salinity, ammonia, potential of hydrogen, nitrogen dioxide, bromine, among others to preserve the well-being of the fish, optimize production and meet the requirements of environmental ethics. To contribute to the fulfillment of these requirements, we propose here to use a data analysis technique called principal component analysis (PCA). This data processing technique is a mathematical procedure that performs a transformation on the data to convert possibly correlated parameters into a new set of parameters called principal components. Once the PCA performs an exploratory analysis on the data, we can use it to reveal the internal structure of the aquaculture data, to explain not only the variance in the data, but also the correlation between them. The choice of PCA in this study was based on the fact that it is a multivariate statistical technique of data that is very well known and widely applied in several scientific fields. Furthermore, the PCA allows to reduce the dimensionality of datasets, increasing their interpretability, but at the same time minimizing the loss of information. Results obtained with the use of PCA on the data related to the weaning and pre-fattening phases of Senegalese sole (species Solea senegalensis) collected during the years 2018 and 2019, respectively, show the feasibility of this proposal in the simultaneous monitoring of aquaculture parameters and take future critical conclusions to predict events in the aquaculture data analysis. Furthermore, there are no records to date of studies in the aquaculture industry in terms of data processing and analysis in detail. Currently, the scientific literature in the area of aquaculture exhibits a consistent body of knowledge based on the characterization of the physiology and environment of fish, with research on instrumentation still scarce. In this perspective, this study not only represents a substantial novelty for the field of aquaculture for performing the data processing directed at the aquaculture industry for the first time, but it will also allow the development of a new branch of research focused specifically in a support tool for aquaculture recirculating systems-based industry.

中文翻译：

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基于多变量统计分析的循环水养殖系统监测快速决策工具

摘要 水产养殖需要同时监测多个参数，如温度、盐度、氨、氢势、二氧化氮、溴等，以保护鱼类的健康、优化生产并满足环境伦理的要求。为了有助于满足这些要求，我们在此建议使用称为主成分分析 (PCA) 的数据分析技术。这种数据处理技术是一种数学过程，它对数据执行转换，将可能相关的参数转换为一组称为主成分的新参数。一旦PCA对数据​​进行探索性分析，我们就可以用它来揭示水产养殖数据的内部结构，不仅解释数据的差异，还有它们之间的相关性。在本研究中选择 PCA 是基于这样一个事实，即它是一种众所周知并广泛应用于多个科学领域的多元数据统计技术。此外，PCA 允许减少数据集的维数，增加其可解释性，但同时最大限度地减少信息丢失。分别在 2018 年和 2019 年期间收集的与塞内加尔鳎（Solea senegalensis 种）断奶和育肥前阶段相关的数据使用 PCA 获得的结果表明该建议在同时监测水产养殖参数方面的可行性并在未来的关键结论中预测水产养殖数据分析中的事件。此外，迄今为止，在数据处理和分析方面没有详细记录水产养殖业的研究。目前，水产养殖领域的科学文献展示了基于鱼类生理和环境特征的一致知识体系，而对仪器的研究仍然很少。从这个角度来看，这项研究不仅代表了水产养殖领域首次针对水产养殖业进行数据处理的实质性创新，而且还将允许开发一个新的研究分支，专门针对支持水产养殖再循环系统工业的工具。水产养殖领域的科学文献展示了基于鱼类生理学和环境特征的一致知识体系，但对仪器的研究仍然很少。从这个角度来看，这项研究不仅代表了水产养殖领域首次针对水产养殖业进行数据处理的实质性创新，而且还将允许开发一个新的研究分支，专门针对支持水产养殖再循环系统工业的工具。水产养殖领域的科学文献展示了基于鱼类生理学和环境特征的一致知识体系，但对仪器的研究仍然很少。从这个角度来看，这项研究不仅代表了水产养殖领域首次针对水产养殖业进行数据处理的实质性创新，而且还将允许开发一个新的研究分支，专门针对支持水产养殖再循环系统工业的工具。