College of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu 210031, China; United States Department of Agriculture, Agricultural Research Service, U.S. National Poultry Research Center, Athens, GA 30605, USA.
United States Department of Agriculture, Agricultural Research Service, U.S. National Poultry Research Center, Athens, GA 30605, USA.
Spectrochim Acta A Mol Biomol Spectrosc. 2020 Jan 5;224:117386. doi: 10.1016/j.saa.2019.117386. Epub 2019 Jul 16.
Non-O157 Shiga toxin-producing Escherichia coli (STEC) serogroups such as O26, O45, O103, O111, O121 and O145 often cause illness to people in the United States and the conventional identification of these "Big-Six" are complex. The label-free hyperspectral microscope imaging (HMI) method, which provides spectral "fingerprints" information of bacterial cells, was employed to classify serogroups at the cellular level. In spectral analysis, principal component analysis (PCA) method and stacked auto-encoder (SAE) method were conducted to extract principal spectral features for classification task. Based on these features, multiple classifiers including linear discriminant analysis (LDA), support vector machine (SVM) and soft-max regression (SR) methods were evaluated. Different sizes of datasets were also tested in search for the suitable classification models. Among the results, SAE-based classification models performed better than PCA-based models, achieving classification accuracy of SAE-LDA (93.5%), SAE-SVM (94.9%) and SAE-SR (94.6%), respectively. In contrast, classification results of PCA-based methods such as PCA-LDA, PCA-SVM and PCA-SR were only 75.5%, 85.7% and 77.1%, respectively. The results also suggested the increasing number of training samples have positive effects on classification models. Taking advantage of increasing dataset, the SAE-SR classification model finally performed better than others with average accuracy of 94.9% in classifying STEC serogroups. Specifically, O103 serogroup was classified with the highest accuracy of 97.4%, followed by O111 (96.5%), O26 (95.3%), O121 (95%), O145 (92.9%) and O45 (92.4%), respectively. Thus, the HMI technology coupled with SAE-SR classification model has the potential for "Big-Six" identification.
非 O157 型志贺毒素产生大肠杆菌（STEC）血清群，如 O26、O45、O103、O111、O121 和 O145，经常导致美国人生病，而这些“六大”血清群的常规鉴定较为复杂。无标记高光谱显微镜成像（HMI）方法可提供细菌细胞的光谱“指纹”信息，用于在细胞水平上对血清群进行分类。在光谱分析中，主成分分析（PCA）方法和堆叠自动编码器（SAE）方法被用来提取主要的光谱特征用于分类任务。基于这些特征，包括线性判别分析（LDA）、支持向量机（SVM）和软最大回归（SR）在内的多种分类器进行了评估。同时，还测试了不同大小的数据集，以寻找合适的分类模型。结果表明，基于 SAE 的分类模型比基于 PCA 的模型表现更好，SAE-LDA（93.5%）、SAE-SVM（94.9%）和 SAE-SR（94.6%）的分类准确率分别达到了 93.5%、94.9%和 94.6%。相比之下，基于 PCA 的方法（如 PCA-LDA、PCA-SVM 和 PCA-SR）的分类结果仅为 75.5%、85.7%和 77.1%。结果还表明，增加训练样本数量对分类模型有积极影响。利用增加的数据集，SAE-SR 分类模型最终表现优于其他模型，在 STEC 血清群分类中的平均准确率达到 94.9%。具体而言，O103 血清群的分类准确率最高，为 97.4%，其次是 O111（96.5%）、O26（95.3%）、O121（95%）、O145（92.9%）和 O45（92.4%）。因此，HMI 技术结合 SAE-SR 分类模型有望用于“六大”血清群的鉴定。
