一种基于机器学习的海水汞检测方法

F. Piccialli, F. Giampaolo, Vincenzo Schiano Di Cola, Federico Gatta, Diletta Chiaro, E. Prezioso, Stefano Izzo, S. Cuomo
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引用次数: 0

摘要

由于移动设备的广泛使用,过去必须在专门指定和装备齐全的实验室进行的分析,需要很长的处理时间,现在可以在室外实时进行。例如,在海洋科学中,开发一种可移动的紧凑型系统,用于现场检测海水中的重金属污染,至少在两个方面对科学家和社会有帮助:1)减少与这些实验相关的时间和成本;Ii)室外分析策略的实施,最终可嵌入到硬件实验室系统中。本文属于机器学习(ML)在跨学科领域应用的实用模式挖掘的背景下:从井板图像开始,我们提供了一个新的概念验证(PoC)基于机器学习的框架,以协助科学家对海水样本进行日常研究,提出了一个系统,该系统首先自动识别多井中的井,然后预测每个井的荧光程度,从而显示重金属的可能存在。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A machine learning-based approach for mercury detection in marine waters
Thanks to the widespread use of mobile devices, analyses that in the past had to be carried out in specifically designated and equipped laboratories and which required long processing times, may now take place outdoor and in real time. In the marine science, for example, the development of a mobile and compact system for the on-site detection of heavy metals contamination in seawater would be helpful for scientists and society in at least two ways: i) reduction of time and costs associated with these experiments; ii) the implementation of a strategy for outdoor analysis, eventually embeddable in a lab-on-hardware system. This paper falls within the context of machine learning (ML) for utility pattern mining applied on interdisciplinary domains: starting from wellplates images, we provide a novel proof-of-concept (PoC) machine learning-based framework to assist scientists in their daily research on seawater samples, proposing a system which automatically recognise wells in a multiwell firstly and then predicts the degree of fluorescence in each of them, thus showing possible presence of heavy metals.
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