Asif Sohaib
Xiangya School of Public Health, Central South University, Changsha, China.
School of Computer Science and Engineering, Central South University, Changsha, China.
Comput Biol Chem. 2024 Dec;113:108238. doi: 10.1016/j.compbiolchem.2024.108238. Epub 2024 Oct 9.
The detection of foodborne bacteria is critical in ensuring both consumer safety and food safety. If these pathogens are not properly identified, it can lead to dangerous cross-contamination. One of the most common methods for classifying bacteria is through the examination of Hyperspectral microscope imaging (HMI). A widely used technique for measuring microbial growth is microscopic cell counting. HMI is a laborious and expensive process, producing voluminous data and needing specialized equipment, which might not be widely available. Machine learning (ML) methods are now frequently utilized to automatically interpret data from hyperspectral microscopy. The objective of our study is to devise a technique that employs deep transfer learning to address the challenge of limited data and utilizes four base classifiers - InceptionResNetV2, MobileNet, ResNet101V2, and Xception - to create an ensemble-based classification model for distinguishing live and dead bacterial cells of six pathogenic strains. In order to determine the optimal weights for the base classifiers, a Powell's optimization method was utilized in conjunction with a weighted average ensemble (WAVE) technique. We carried out an extensive experimental study to verify the efficiency of our proposed ensemble model on live and dead cell images of six different foodborne bacteria. In order to gain a better understanding of the regions, we performed a Grad-CAM analysis to explain the predictions made by our model. Through a series of experiments, our proposed framework has proven its capacity to effectively and precisely detect numerous bacterial pathogens. Specifically, it achieved a perfect identification rate of 100 % for Escherichia coli (EC), Listeria innocua (LI), and Salmonella Enteritidis (SE), while achieving rates of 96.30 % for Salmonella Typhimurium (ST), 87.13 % for Staphylococcus aureus (SA), and 94.12 % for Salmonella Heidelberg (SH). As a result, it can be considered as an effective tool for the identification of foodborne pathogens, due to its high level of efficiency.
食源细菌的检测对于确保消费者安全和食品安全至关重要。如果这些病原体没有得到正确识别，可能会导致危险的交叉污染。对细菌进行分类的最常见方法之一是通过高光谱显微镜成像（HMI）检查。一种广泛使用的测量微生物生长的技术是显微镜细胞计数。HMI是一个费力且昂贵的过程，会产生大量数据且需要专门设备，而这些设备可能并不广泛可用。机器学习（ML）方法现在经常被用于自动解释高光谱显微镜的数据。我们研究的目的是设计一种技术，该技术采用深度迁移学习来应对数据有限的挑战，并利用四个基础分类器——InceptionResNetV2、MobileNet、ResNet101V2和Xception——创建一个基于集成的分类模型，用于区分六种致病菌株的活细菌细胞和死细菌细胞。为了确定基础分类器的最佳权重，结合加权平均集成（WAVE）技术使用了鲍威尔优化方法。我们进行了广泛的实验研究，以验证我们提出的集成模型对六种不同食源细菌的活细胞和死细胞图像的有效性。为了更好地理解这些区域，我们进行了Grad-CAM分析来解释我们模型所做的预测。通过一系列实验，我们提出的框架已证明其能够有效且精确地检测多种细菌病原体。具体而言，它对大肠杆菌（EC）、无害李斯特菌（LI）和肠炎沙门氏菌（SE）的识别率达到了100%，对鼠伤寒沙门氏菌（ST）的识别率为96.30%，对金黄色葡萄球菌（SA）的识别率为87.13%，对海德堡沙门氏菌（SH）的识别率为94.12%。因此，由于其高效率，它可被视为一种识别食源性病原体有效的工具。
