Department of Foood Science, Food Engineering, Biotechnology and Aquaculture, Faculty of Food Science and Engineering, University "Dunărea de Jos" of Galați, 800008 Galați, Romania.
The Fish Culture Research and Development Station of Nucet, 137335 Dâmbovița-Nucet, Romania.
Molecules. 2020 Oct 14;25(20):4696. doi: 10.3390/molecules25204696.
Metals are considered to be one of the most hazardous substances due to their potential for accumulation, magnification, persistence, and wide distribution in water, sediments, and aquatic organisms. Demersal fish species, such as turbot (), are accepted by the scientific communities as suitable bioindicators of heavy metal pollution in the aquatic environment. The present study uses a machine learning approach, which is based on multiple linear and non-linear models, in order to effectively estimate the concentrations of heavy metals in both turbot muscle and liver tissues. For multiple linear regression (MLR) models, the stepwise method was used, while non-linear models were developed by applying random forest (RF) algorithm. The models were based on data that were provided from scientific literature, attributed to 11 heavy metals (As, Ca, Cd, Cu, Fe, K, Mg, Mn, Na, Ni, Zn) from both muscle and liver tissues of turbot exemplars. Significant MLR models were recorded for Ca, Fe, Mg, and Na in muscle tissue and K, Cu, Zn, and Na in turbot liver tissue. The non-linear tree-based RF prediction models (over 70% prediction accuracy) were identified for As, Cd, Cu, K, Mg, and Zn in muscle tissue and As, Ca, Cd, Mg, and Fe in turbot liver tissue. Both machine learning MLR and non-linear tree-based RF prediction models were identified to be suitable for predicting the heavy metal concentration from both turbot muscle and liver tissues. The models can be used for improving the knowledge and economic efficiency of linked heavy metals food safety and environment pollution studies.
金属被认为是最危险的物质之一，因为它们具有在水、沉积物和水生生物中积累、放大、持久和广泛分布的潜力。底栖鱼类，如大菱鲆（），被科学界接受为水生环境中重金属污染的合适生物标志物。本研究采用基于多元线性和非线性模型的机器学习方法，有效估计大菱鲆肌肉和肝脏组织中重金属的浓度。对于多元线性回归（MLR）模型，使用了逐步法，而非线性模型则通过应用随机森林（RF）算法开发。模型基于科学文献提供的数据，涉及 11 种重金属（As、Ca、Cd、Cu、Fe、K、Mg、Mn、Na、Ni、Zn），来自大菱鲆肌肉和肝脏组织的样本。在肌肉组织中，Ca、Fe、Mg 和 Na 以及在大菱鲆肝脏组织中 K、Cu、Zn 和 Na 记录了显著的 MLR 模型。在肌肉组织中对 As、Cd、Cu、K、Mg 和 Zn 以及在大菱鲆肝脏组织中对 As、Ca、Cd、Mg 和 Fe 分别识别出了非线性基于树的 RF 预测模型（预测准确率超过 70%）。多元线性回归（MLR）和非线性基于树的 RF 预测模型均被认为适合预测大菱鲆肌肉和肝脏组织中的重金属浓度。这些模型可用于提高与重金属食品安全和环境污染相关的研究的知识和经济效益。
