Water Research Centre (WRC), School of Civil and Environmental Engineering, UNSW Sydney, Sydney, New South Wales, Australia.
Department of Civil Engineering, Environmental and Public Health Microbiology Laboratory (EPHM Lab), Monash University, Melbourne, Victoria, Australia.
PLoS One. 2024 Sep 6;19(9):e0307572. doi: 10.1371/journal.pone.0307572. eCollection 2024.
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.
由于其低浓度，从环境水中枚举弯曲杆菌可能很困难，但仍会构成重大健康风险。分光光度法是一种常用于快速检测水样中水污染的方法，但尚未用于病原体检测，通常通过繁琐且耗时的培养或 qPCR 最可能数（即 MPN-PCR）方法进行。在这项研究中，我们提出了一种新方法，MPN-Spectro-ML，可以快速提供弯曲杆菌检测和水浓度的证据。经过初步孵育，用分光光度计分析样品，并用光谱数据训练三个机器学习（ML）模型（即支持向量机-SVM、逻辑回归-LR 和随机森林-RF）。训练后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数（MPN）。从农村和城市集水区收集了 100 多个雨水、河流和小溪水样（包括淡水和咸水），以在各种情况下测试 MPN-Spectro-ML 方法的准确性，并与以前标准化的 MPN-PCR 方法进行比较。在正、阴性对照样品之间发现了光谱差异，阳性样品在 540-542nm 和 575-576nm 之间有两个明显的吸光度峰。此外，无论测试场景如何，三个 ML 模型的性能都相似，平均预测准确率（ACC）和假阴性率分别为 0.763%和 13.8%。然而，新方法预测的弯曲杆菌 MPN 值与传统的 MPN-PCR 方法不同，城市集水区数据集的纳什-苏特克利夫系数最大为 0.44。然而，考虑到 MPN 估计的置信区间和较大的不确定性，这两种方法的 MPN 值仍然具有可比性。该研究揭示了这种新方法在提供环境水体中弯曲杆菌存在和水平的临时证据方面的潜力。这反过来又减少了从风险检测到管理的时间，有利于公众健康。
