Correlation results.

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.

环境水体中弯曲杆菌（Campylobacter）的计数工作极具挑战性，因其浓度极低，但即便如此仍可构成显著健康风险。分光光度法（Spectrophotometry）是水样中水生污染物快速检测的常用手段，但尚未应用于病原体检测；当前病原体检测通常需借助耗时费力的培养法或实时定量PCR（qPCR）最大可能数（Most Probable Number, MPN）计数法（即MPN-PCR法）。本研究提出一种新型方法MPN-Spectro-ML，可快速实现弯曲杆菌检测并定量水体中的菌浓度。经初始富集培养后，采用分光光度计对样本进行分析，并利用光谱数据训练三种机器学习（Machine Learning, ML）模型：支持向量机（Support Vector Machine, SVM）、逻辑回归（Logistic Regression, LR）以及随机森林（Random Forest, RF）。经训练的模型可用于预测富集后水样中弯曲杆菌的存在情况，并估算其最大可能数（MPN）。本研究收集了来自城乡集水区的100余份雨水、河流及溪流水样（涵盖淡水与咸淡水水体），在多种场景下验证MPN-Spectro-ML法的检测精度，并与已标准化的MPN-PCR法进行对比。研究发现阳性对照与阴性对照样本的光谱存在显著差异：阳性样本在540~542nm及575~576nm波段存在两处特征吸收峰。此外，无论测试场景如何，三种机器学习模型的性能均较为接近，平均预测准确率（Accuracy, ACC）达0.763，假阴性率仅为13.8%。不过，新型方法预测的弯曲杆菌MPN值与传统MPN-PCR法存在一定偏差：城市集水区数据集的纳什-萨特克利夫系数（Nash-Sutcliffe coefficient）最高为0.44。但考虑到MPN估算本身存在置信区间与较大不确定性，两种方法得到的MPN值仍具有可比性。本研究证实了该新型方法的应用潜力，可快速为环境水体中弯曲杆菌的存在及丰度提供阶段性检测依据。此举可缩短从风险检出到风险管控的时长，助力公共卫生保障。