Qian Hanyu, McLamore Eric, Bliznyuk Nikolay
Department of Agricultural and Biological Engineering, University of Florida, Gainesville, Florida 32611, United States.
Department of Agricultural Sciences, College of Agriculture, Forestry and Life Sciences, Clemson University, Clemson, South Carolina 29634, United States.
ACS Omega. 2023 Sep 10;8(37):34171-34179. doi: 10.1021/acsomega.3c05797. eCollection 2023 Sep 19.
Reuse of alternative water sources for irrigation (e.g., untreated surface water) is a sustainable approach that has the potential to reduce water gaps, while increasing food production. However, when growing fresh produce, this practice increases the risk of bacterial contamination. Thus, rapid and accurate identification of pathogenic organisms such as Shiga-toxin producing (STEC) is crucial for resource management when using alternative water(s). Although many biosensors exist for monitoring pathogens in food systems, there is an urgent need for data analysis methodologies that can be applied to accurately predict bacteria concentrations in complex matrices such as untreated surface water. In this work, we applied an impedimetric electrochemical aptasensor based on gold interdigitated electrodes for measuring in surface water for hydroponic lettuce irrigation. We developed a statistical machine-learning (SML) framework for assessing different existing SML methods to predict the concentration. In this study, three classes of statistical models were evaluated for optimizing prediction accuracy. The SML framework developed here facilitates selection of the most appropriate analytical approach for a given application. In the case of prediction in untreated surface water, selection of the optimum SML technique led to a reduction of test set RMSE by at least 20% when compared with the classic analytical technique. The statistical framework and code (open source) include a portfolio of SML models, an approach which can be used by other researchers using electrochemical biosensors to measure pathogens in hydroponic irrigation water for rapid decision support.
将替代性水源（如未经处理的地表水）用于灌溉是一种可持续的方法，它有可能缩小水资源缺口，同时提高粮食产量。然而，在种植新鲜农产品时，这种做法会增加细菌污染的风险。因此，快速准确地鉴定诸如产志贺毒素大肠杆菌（STEC）等致病生物对于使用替代水源时的资源管理至关重要。尽管存在许多用于监测食品系统中病原体的生物传感器，但迫切需要能够应用于准确预测复杂基质（如未经处理的地表水）中细菌浓度的数据分析方法。在这项工作中，我们应用了一种基于金叉指电极的阻抗式电化学适体传感器来测量用于水培生菜灌溉的地表水中的（相关物质）。我们开发了一个统计机器学习（SML）框架，用于评估不同的现有SML方法以预测（相关物质）浓度。在本研究中，评估了三类统计模型以优化预测准确性。这里开发的SML框架有助于为给定应用选择最合适的分析方法。在未经处理的地表水中进行（相关物质）预测的情况下，与经典分析技术相比，选择最佳SML技术可使测试集均方根误差至少降低20%。该统计框架和代码（开源）包括一系列SML模型，其他研究人员可以使用这种方法，利用电化学生物传感器测量水培灌溉水中的病原体，以提供快速决策支持。
