Ensuring food safety through rapid and accurate detection of pathogenic bacteria in food products is a critical challenge in the food supply chain. In this study, a non-specific optical sensor array was proposed for the identification of multiple pathogenic bacteria in contaminated milk samples. Fluorescence-labeled single-stranded DNA was efficiently quenched by two-dimensional nanoparticles and subsequently recovered by foreign biomolecules. The recovered fluorescence generated a unique fingerprint for each bacterial species, enabling the sensor array to identify eight bacteria (pathogenic and spoilage) within a few hours. Four traditional machine learning models and two artificial neural networks were applied for classification. The neural network showed a 93.8 % accuracy with a 30-min incubation. Extending the incubation to 120 min increased the accuracy of the multiplayer perceptron to 98.4 %. This sensor array is a novel, low-cost, and high-accuracy approach for the identification of multiple bacteria, providing an alternative to plate counting and ELISA methods.

中文翻译：

---

机器学习支持的单链 DNA 传感器阵列，用于识别牛奶中的多种食源性致病菌和腐败细菌


通过快速准确地检测食品中的病原菌来确保食品安全是食品供应链中的一项重大挑战。在本研究中，提出了一种非特异性光学传感器阵列，用于鉴定受污染牛奶样品中的多种致病菌。荧光标记的单链 DNA 被二维纳米颗粒有效淬灭，随后被外源生物分子回收。回收的荧光为每种细菌种类生成了唯一的指纹图谱，使传感器阵列能够在几个小时内识别出八种细菌（致病性和腐败性）。应用了 4 个传统机器学习模型和 2 个人工神经网络进行分类。神经网络在 30 分钟的孵育中显示出 93.8% 的准确率。将孵化时间延长到 120 分钟，将多人感知器的准确率提高到 98.4%。这种传感器阵列是一种新型、低成本和高精度的多种细菌鉴定方法，是平板计数和 ELISA 方法的替代方案。