Ten E. coli strains and eight Shigella strains were used in this experiment. Two of the laboratory strains E. coli BL21 (DE3) and BW25113 was purchased from Tiangen Biotech Co. Ltd (Beijing, China), the remaining eight E. coli strains and eight Shigella spp., strains (S. dysenteriae: 2 strains, S. boydii: 2 strains, S. flexneri: 2 strains, and S. sonnei: 2 strains) were directly isolated from clinical samples and confirmed via both biochemical tests and mass spectrometry in Xuzhou Infectious Diseases Hospital affiliated to Xuzhou Medical University. All the strains were stored in liquid Luria Broth (LB) culture with 25% glycerol stock at −80°C. During the study, all the strains were recovered from the −80°C freezer by streaking on LB agar plate and incubating at 37°C overnight before experimental analysis.

The preparation procedures for silver nanoparticles (AgNPs) have been well documents in previous studies (Tang et al., 2021; Liu et al., 2022; Tang et al., 2022; Wang et al., 2022a,b). Therefore, we only briefly stated the preparation steps in terms of the key reactions. In particular, 33.72 mg of silver nitrate (AgNO₃) was added to a triangular flask with 200 mL of deionized distilled water (ddH₂O) with stirring and heating until boiling. 8 mL of sodium citrate (Na₃C₆H₅O₇) was then added with stirring and heating at 650 r/min for 40 min. Stop heating and keep stirring until the solution cools down to room temperature, which was then filled up to 200 mL with ddH₂O. 1 mL of the prepared solution was transferred to a clean Eppendorf (EP) tube for 7-min centrifugation at 7000 r/min. Discard the supernatant and resuspend the pellet with 100 μL of ddH₂O, which was the AgNP substrate that were stored without light at room temperature for long-term use.

For each bacterial species (10 E. coli strains and eight Shigella spp., strains) used in this study, after recovering via cultivating on LB agar plate overnight, a single colony was selected and inoculated into 15 μL phosphate buffer saline (PBS) and well mixed via vortexing, which was then well mixed with 15 μL negatively-charged AgNPs substrate solution. The well-mixed suspension was dropped onto the surface of silicon wafer and to form a circular spot of suitable size, which was dried naturally before SERS spectroscopy. The hand-held Raman spectrometer Anton Paar™ Cora100 (Anton Paar Shanghai Trading Co., Ltd., China.) was used for sample analysis and Raman spectral detection by using the following settings of parameters: (1) excitation wavelength: 784.56 nm; (2) excitation power: 25 mW; (3) spectral resolution: 1 nm; (4) spectral wave number resolution: 10 cm⁻¹; (5) detection spectral range: 400–2,300 cm⁻¹. All the SERS spectra were calibrated using the Raman peak at 520 cm⁻¹ as the reference peak and the dark current was deducted during integration time. For each bacterial species, a total of 200 spectra were collected by randomly selecting detecting sites in each of the dried sample spots. Therefore, a total of 800 spectra for Shigella spp., and 800 SERS spectra of E. coli were used for further analysis in this study,

In order to explore the characteristic features of the average Raman spectra for Shigella spp., and E. coli, we performed deconvolution operations on each average Raman spectrum. In specificity, baseline correction was first performed on the average spectrum with the parameter set to type = Polynom and degree = 4, which aims to highlight the representative characteristic peaks so that finer differences can be found by means of deconvolution. The function fit peaks pro in Origin (version 2019b 32bit) was applied for peak fitting, which used Voigt function maximum value and half width to obtain the Doppler and Lorentz lines to solve the deconvolution sub-bands of spectra. Before fitting, appropriate values for the Gaussian width and Lorentzian width parameters were selected to avoid fitting failures. In addition, barcodes are an efficient mean for electronic record-keeping, which enables rapid identification via software (Pezzotti et al., 2021). Therefore, in this study, in order to improve the accessibility of spectral data, we designed barcodes for the deconvoluted Raman spectra in terms of different variants and their subtypes. The barcodes were generated through assigning each band a line with width equal to 1/30 of the width of the sub-band and a distance from the successive line proportional to the band area, which provided necessary flexibility and rapidity for Raman spectroscopy methods.

In order to investigate the inherent differences among SERS spectra of E. coli and Shigella spp., T-Distributed Stochastic Neighbor Embedding (T-SNE) algorithm was used for dimensionality reduction and clustering visualization of bacterial spectral data. Set n_components = 2 and learning_rate = 10 for the TSNE function in the scikit-learn package (version 0.21.3) to analyze the spectral difference between Shigella spp., and E. coli spectra. For data visualization, we chose the first two feature dimensions after TSNE dimensionality reduction as the coordinate axises, and displayed the categories of Shigella spp., and E. coli in the form of scatter plots.

To evaluate the capacities of machine learning algorithms on the differentiation of Shigella spp., and E. coli, quantitative metrics were used to measure the performance of these algorithms. Accuracy (ACC), as the most basic evaluation index in spectral signal identification, is often used to describe the correct prediction of the overall result. In this study, to avoid the data imbalance issue caused by the random splitting of data, we used Precision (P) and Recall (R) to obtain objective model evaluations. Precision represents the probability of a sample that is actually positive among all samples that are predicted to be positive, while Recall represents the probability of a sample that is predicted to be positive among all samples that are actually positive. During the data analysis process, the average parameters of Precision and Recall were set to Micro and Macro, respectively. As Precision and Recall are mutually influencing indicators, F1-Score as the harmonic average of the two indicators, were introduced to provide a comprehensive evaluation, and the average parameter was weighted. In addition, in order to avoid the occurrence of overfitting of the model during training, we used the five-fold cross-validation method with cv. = 5 in the cross_val_score function, which divided the training set data into 5 equal-sized subsets and tested the models consecutively. That is, during the training process, a set of data subsets were selected as the validation set, and the remaining 4 sets were used as the training set, and 5 models were trained to correspond to 5 accuracy rates, respectively, while the average accuracy rate was calculated as the final training effect. In addition, ROC curves and confusion matrix were also drawn to measure the performance of the models. In specificity, we evaluated machine learning models by using roc_curve to draw the ROC curve. In addition, the area under the curve (AUC) value calculated by the roc_auc_score function was also used as a metric to eliminate the imbalance of sample categories. In order to more intuitively observe the performance of the model on the test set data, we visualized the prediction results of the optimal model CNN via confusion matrix via confusi_matrix function in the form of a 2*2 matrix.

It was worth noting that, in this study, we also used the Grad-CAM algorithm to explain how the deep learning model extracted the SERS spectral features and the decision-making process of the convolutional layer on the SERS spectral signal. In the specific calculation process, the tf.GradientTape method was conducted to calculate the gradient vector of the last convolutional layer, and the channel mean was calculated using the tf.reduce_mean method. The Grad-CAM of the target bacterial species was generated according to the gradient vector and the channel mean. In addition to obtaining the weight distribution of the model for the spectral data, we also investigated how the model classified the spectral data of Shigella spp., and E. coil. Therefore, we input the abstract features extracted from the last layer of the convolution layer (num_layer = 5) into TSNE for classification, which further proved the feasibility of the CNN model. During the classification, the TSNE parameters were set to n_components = 2, init = ‘pca’, and random_state = 0. A brief flow chat was given to show the logic of the SERS spectral collection and computational analysis behind the discrimination of Shigella spp., and E. coli (Figure 1).

Both bacterial morphological characteristics and physiological states have different molecular basis, while SERS spectra can reflect these molecular differences and are able to transform these chemical and structural information into SERS signal intensities at different Raman shifts (Lu et al., 2020). As for bacterial cells belonging to the same species, they are similar to each other in terms of morphology and physiology, while those from different species are quite dissimilar (Lu et al., 2020). Therefore, different bacterial species could be discriminated based on the analysis of their SERS spectra. In this study, we collected SERS spectra for Shigella spp., and Escherichia coli, respectively. Average Raman spectra with shaded standard error (SE) bands for the two species were calculated to quantitatively display the general trend of SERS spectral repeats and also reflect the data variance among the spectra (Liu et al., 2022), according to which, SERS spectra were well repeated for Shigella spp., and E. coli, and the reproducibility of SERS spectra varied in acceptable ranges (Figures 2A,B). In this study, deconvoluted SERS spectral band components that were directly linked to molecular structures and chemical components were also generated, which aimed to evaluate SERS spectral characteristics efficiently (Pezzotti et al., 2022). Based on the deconvoluted SERS spectra, two-dimensional (2D) Raman barcoding bands were then generated for Shigella spp., and E. coli at the upper right corners of Figures 2C,D. In particular, the deconvolution spectra consisted of a series of Vogit sub-bands, each representing a spectral characteristic peak. It could be seen that the method fts the important characteristic peaks of Raman spectra well and could eliminate the interference of other artifactual peaks. In addition, Shigella spp., and E. coil Raman barcodes also contained subtle details of the structure of internal metabolites, amplifying the differences among the two bacterial genera. Taken together, the Raman barcodes generated through deconvolution analysis greatly supported the electronic storage of SERS spectra, converted molecular characteristics into information, and improved the recognition of bacterial species.

As previously mentioned, due to the compositional and physiological differences between bacterial species, SERS spectra contains unique characteristic peaks corresponding to different molecular structures and components (Tang et al., 2021). In this study, under the signal measurement of SERS spectra at the wavelength of 785 nm, characteristic peaks for E. coli and Shigella spp., were computationally identified, respectively. All the characteristic peaks were annotated with a black arrow in Figure 2, while band assignments of characteristic peaks corresponding to potential molecular structure and components were given in details in Table 1. The unique characteristic peaks to both bacteria revealed apparent differences between E. coli and shigella spp. We can see that there are many different peaks between the two bacterial genera. In specificity, as for E. coli, the unique characteristic peaks identified in its average SERS spectrum include C=O, C-N and ring deformation at 794 cm⁻¹ (Kubryk et al., 2016), Ribose vibration, one of the distinct RNA modes at 910 cm⁻¹ (Szekeres and Kneipp, 2019), v(C-C) at 1034 cm⁻¹ (Athamneh et al., 2014), Amine III of proteins at 1248 cm⁻¹ (Bashir et al., 2021), (C-N) stretch at 1320 cm⁻¹ (Zeiri et al., 2004), and Stretch C=C in the quinoid ring at 1414 cm⁻¹ (Laska and Widlarz, 2005). On the other hand, for Shigella spp., their unique characteristic peaks include C-C chain stretch of cell wall lipids at 1096 cm⁻¹ (Zheng et al., 2018), tryptophan at 1332 cm⁻¹ (Xie et al., 2013), and Ring stretching (Adenine, guanine) at 1594 cm⁻¹ (Demirel et al., 2009).

Clustering analysis is able to find commonalities between data elements, therefore dividing SERS spectra into different parts (also known as clusters) so that spectra in the same part are similar to each other while spectra in different parts are dissimilar to each other, leading to the differentiation of bacterial species (Talabis et al., 2015). In this study, we applied the two algorithms, TSNE and OPLS-DA, for the clustering analysis of Shigella spp., and E. coli SERS spectra. TSNE is considered as one of the best high-dimensionality data reduction method and clustering visualization tool, though it has obvious disadvantages of large memory consumption and long running time (Linderman and Steinerberger, 2019; Pezzotti et al., 2019). During the application of TSNE method in SERS spectra, the result showed that two bacterial genera could be well separated into two groups but the classification boundary is not clear due to the mixture of certain number of E. coli SERS spectra (Figure 3A). As for OPLS-DA, it is a multivariate statistical method for supervised data clustering, which provides insights into the divisions of data groups based on high-dimensional spectral measurements (Worley and Powers, 2016). When using OPLS-DA method for analyzing the same SERS spectral dataset, it was found that Shigella spp., and E. coli spectra were well clustered into two groups with smaller intra-genus variations and the boundary between the two genera was clear (Figure 3B). In addition, unlike TSNE, OPLS-DA gave a quantitative measurement of the clustering result, which showed that R2X (cum) = 0.964, R2Y (cum) = 0.806 and Q2 (cum) = 0.799. The larger the three parameters are, the better the classification performance of the model is, and the difference between R2X and R2Y is generally no more than 0.3 (Abdel-Ilah et al., 2017).

Although clustering analysis could differentiate SERS spectra of Shigella spp., and E. coli into different groups, it is difficult to know the accuracy and robustness of the analysis. Therefore, supervised machine learning analysis was used for predicting the identity of a particular SERS spectra. In particular, supervised machine learning algorithms are able to learn the patterns in the input variable X and the construct mapping functions Y = f(X) in order to achieve the purpose of using unknown input data X to accurately predict its output Y (Tang et al., 2021). In this study, we compared the predictive capacities of three representative machine learning algorithms, CNN, SVM, and RF. The results were shown in Table 2, according to which, all the prediction parameters for the deep learning model CNN reached to values of 99.64%, indicating that CNN model worked the best in capturing the features of the SERS spectral signals and was able to predict Shigella spp., and E. coli with no mistake. As for the two classic machine learning algorithms, it was found that both SVM and RF achieved very high accuracy and robustness, which was very close to the performance of CNN, confirming that SVM and RF were also suitable in predicting SERS spectra of Shigella spp., and E. coli.

ROC curve aims to compare the sensitivity and specificity across a range of values for the predictive ability of supervised machine learning methods, and AUC means overall accuracies in distinguishing data samples (Florkowski, 2008). As for confusion matrix, it is a table that shows the classification results based on the true class and predicted class (Tang et al., 2021). Therefore, in this study, we plotted both ROC curve and confusion matrix for the optimal prediction model CNN. As for the ROC curve, the x-axis represents the specificity (false positive rate, FPR) while the y-axis represents the sensitivity (true positive rate, TPR). According to Figure 4A, it could be seen that the AUC value was 1.00, which indicated that the CNN model had very high specificity and sensitivity. The confusion matrix showed the specific performance of the model on the test set data (Figure 4B), It could be seen that the prediction accuracy of CNN models was 100% for Shigella spp., and 99% for E. coli, while 1% of the E. coli spectra was misidentified as Shigella spp., indicating that these spectra could be accurately assigned to the correct species with very low error rate.

In order to observe the distributions of the CNN model in the SERS spectra of different bacterial genera, we used the Grad-CAM algorithm to analyze the full spectral data. It could be seen that the heatmap distribution of the raw spectra data was determined by the intensity of characteristic peaks, that is, high intensity region appeared in red and low intensity region appeared in white (Figure 5A). As for the spectral data output by the CNN model, they were not classified according to the characteristic peaks as previously inferred. Instead, according to the results, there were obvious differences in the weight distribution between the spectra of the two bacterial genera (Figure 5B). In order to better interpret the result, we arbitrarily divided the heatmap into three regions. In the first region of the spectra, the CNN model assigned more attention in the range of 756-754 cm⁻¹ in E. coil, while more attention was distributed in the range of 688-721 cm⁻¹ for the SERS spectra of Shigella spp. In the second region, where the spectral intensity was intermediate, the model did not pay much attention to E. coli in this area, while for Shigella spp., the attention of CNN model mainly focused on around the characteristic peak at 994–1000 cm⁻¹. In the third region of the spectra, the model focused on the Raman shifts at 1484–1542 cm⁻¹ for E. coil, but for Shigella, the model assigned more weights on the end of the spectrum from 1764 cm⁻¹ to 1776 cm⁻¹. To further demonstrate how the CNN model classified Shigella spp., and E. coil, we used TSNE to show the extraction results of spectral features by the model. It could be seen that the two bacterial groups were divided into two well-separated clusters, and according the label display, it can be judged that the model can distinguish the two bacteria well (Figure 5C). In sum, Grad-CAM analysis confirmed the reliability of the CNN model, indicating that it had the ability to accurately identify Shigella spp., from E. coil.