从环境水域中计算弯曲杆菌可能很困难,因为它的浓度很低,这仍然会带来巨大的健康风险。分光光度法是一种常用的快速检测水样中的水性污染物的方法,但是它还没有用于病原体检测,这通常是通过费力且耗时的培养或qPCR最可能的数字枚举方法来完成的(即,MPN-PCR方法)。在这项研究中,我们提出了一种新的方法,MPN-Spectro-ML,这可以提供弯曲杆菌检测的快速证据,因此,水浓度。在最初的孵化之后,样品用分光光度计分析,光谱数据用于训练三个机器学习(ML)模型(即,支持向量机-SVM,逻辑回归-LR,和随机森林RF)。训练的模型用于预测富集水样中弯曲杆菌的存在,并估计最可能的数量(MPN)。超过100个雨水,河,收集了来自农村和城市流域的小溪样品(包括淡水和微咸水),以测试MPN-Spectro-ML方法在各种情况下的准确性,并与以前标准化的MPN-PCR方法进行了比较。阳性和阴性对照样品之间存在光谱差异,阳性样品在540-542nm和575-576nm之间具有两个独特的吸收峰。Further,三种ML模型具有相似的性能,与平均预测精度(ACC)和假阴性率为0.763和13.8%的测试场景无关,分别。然而,新方法预测的弯曲杆菌MPN与传统的MPN-PCR方法不同,城市集水区数据集的最大纳什-萨克利夫系数为0.44。然而,基于这两种方法的MPN值仍然具有可比性,考虑与MPN估计相关的置信区间和较大的不确定性。该研究揭示了这种新颖方法的潜力,可以为环境水体中弯曲杆菌的存在和水平提供临时证据。这个,反过来,减少从风险检测到管理的时间,以促进公共卫生。
Enumeration of
Campylobacter from environmental waters can be difficult due to its low concentrations, which can still pose a significant health risk. Spectrophotometry is an approach commonly used for fast detection of water-borne pollutants in water samples, but it has not been used for pathogen detection, which is commonly done through a laborious and time-consuming culture or qPCR Most Probable Number enumeration methods (i.e., MPN-PCR approaches). In this study, we proposed a new method, MPN-Spectro-ML, that can provide rapid evidence of
Campylobacter detection and, hence, water concentrations. After an initial incubation, the samples were analysed using a spectrophotometer, and the spectrum data were used to train three machine learning (ML) models (i.e., supported vector machine - SVM, logistic regression-LR, and random forest-RF). The trained models were used to predict the presence of
Campylobacter in the enriched water samples and estimate the most probable number (MPN). Over 100 stormwater, river, and creek samples (including both fresh and brackish water) from rural and urban catchments were collected to test the accuracy of the MPN-Spectro-ML method under various scenarios and compared to a previously standardised MPN-PCR method. Differences in the spectrum were found between positive and negative control samples, with two distinctive absorbance peaks between 540-542nm and 575-576nm for positive samples. Further, the three ML models had similar performance irrespective of the scenario tested with average prediction accuracy (ACC) and false negative rates at 0.763 and 13.8%, respectively. However, the predicted MPN of
Campylobacter from the new method varied from the traditional MPN-PCR method, with a maximum Nash-Sutcliffe coefficient of 0.44 for the urban catchment dataset. Nevertheless, the MPN values based on these two methods were still comparable, considering the confidence intervals and large uncertainties associated with MPN estimation. The study reveals the potential of this novel approach for providing interim evidence of the presence and levels of
Campylobacter within environmental water bodies. This, in turn, decreases the time from risk detection to management for the benefit of public health.