Rapid discrimination and ratio quantification of mixed antibiotics in aqueous solution through integrative analysis of SERS spectra via CNN combined with NN-EN model

Graphical abstract

Highlights

• •Deconvolution of Raman spectra resolves the pure antibiotic spectrum in the SERS spectrum of the mixture.
• •The Shapley Additive exPlanations (SHAP) explanatory algorithm reveals discriminative ways within the model.
• •Convolutional Neural Network (CNN) combined with Non-Negative Elastic Net (NN-EN) provides quantitative identification of ratio of mixed antibiotics.

Abstract

Introduction

Abusing antibiotic residues in the natural environment has become a severe public health and ecological environmental problem. The side effects of its biochemical and physiological consequences are severe. To avoid antibiotic contamination in water, implementing universal and rapid antibiotic residue detection technology is critical to maintaining antibiotic safety in aquatic environments. Surface-enhanced Raman spectroscopy (SERS) provides a powerful tool for identifying small molecular components with high sensitivity and selectivity. However, it remains a challenge to identify pure antibiotics from SERS spectra due to coexisting components in the mixture.

Objectives

In this study, an intelligent analysis model for the SERS spectrum based on a deep learning algorithm was proposed for rapid identification of the antibiotic components in the mixture and quantitative determination of the ratios of these components.

Methods

We established a water environment system containing three antibiotic residues of ciprofloxacin, doxycycline, and levofloxacin. To facilitate qualitative and quantitative analysis of the SERS spectra antibiotic mixture datasets, we developed a computational framework integrating a convolutional neural network (CNN) and a non-negative elastic network (NN-EN) method.

Results

The experimental results demonstrate that the CNN model has a recognition accuracy of 98.68%, and the interpretation analysis of Shapley Additive exPlanations (SHAP) shows that our model can specifically focus on the characteristic peak distribution. In contrast, the NN-EN model can accurately quantify each component's ratio in the mixture.

Conclusion

Integrating the SERS technique assisted by the CNN combined with the NN-EN model exhibits great potential for rapid identification and high-precision quantification of antibiotic residues in aquatic environments.

Introduction

Since Dr. Fleming first discovered the antibiotic properties of penicillin in 1928, various antibiotics have been used to prevent and treat human disease [1]. Antibiotics, as a family of secondary metabolites or synthetic analogs, not only interfere with cell development but also kill or inhibit microorganisms at low concentrations [2]. This makes antibiotics widely used in aquaculture, poultry production, and agricultural development to increase economic productivity [1]. However, antibiotics are released into the water environment during the manufacturing process through urban, agricultural, and industrial routes, and antibiotic transportation and dissemination are completed in the water environment [1]. As a result, antibiotic residues are often found in everyday items, such as meat products, drinking water, and dairy products. Accumulation in the human body through the food chain results in significant changes in bacterial resistance, causing a decline in drug treatment efficacy and considerable harm to human health [1], [3].

The detection of antibiotics is an important step in antibiotic research. Currently, scientists have developed a large number of analytical methods, such as chromatography, high-performance liquid chromatography (HPLC), mass spectrometry (MS), and capillary electrophoresis (CE) etc., for the qualitative and quantitative determination of antibiotics in food and water environments. However, expensive equipment and cumbersome operations limit this method's rapid on-site analytical application [4]. The microbial screening assay based on the bacterial growth inhibition-dependent antibiotic concentration method is cheap and easy to operate, but it takes a long time and has poor stability [5]. The enzyme-linked immunosorbent assay (ELISA) is a viable alternative for high-throughput antibiotic screening. Still, the process entails repetitive separation and washing steps, requiring skilled laboratory operators [6]. Therefore, developing a fast and convenient detection method for identifying and quantifying unknown trace antibiotics in the water environment is significant.

Optical technologies, including visible-light/near-infrared spectroscopy, hyperspectral imaging, and Raman spectroscopy, have great worth in public health safety and quality inspections. These techniques have numerous advantages, such as simple operation, low cost, rapid results, etc [7], [8]. In optical technology, Raman spectroscopy has the advantage of being insensitive to water, which can be used for qualitative and quantitative identification of the excess chemical substances in the water environment [9]. However, the Raman spectra of biological samples are often overwhelmed by a robust fluorescent background, hindering the detection of low concentrations of chemicals [7]. Surface-enhanced Raman spectroscopy (SERS) has been applied to qualitatively characterize interactions between molecules and metal nanomaterials with fingerprint spectral resolution and selective single-molecule level sensitivity, effectively enhancing Raman signal strength by combining with portable or handheld Raman spectroscopy instruments [10]. It has been used quantitatively with enormous potential [11], [12]. However, due to complex background interference, most studies are aimed at the analysis methods of a single class of antibiotics in the sample [13], [14], making the identification and quantification of multiple antibiotics in the mixed SERS spectrum still challenging.

Traditional Raman spectral data analysis methods are iterative techniques based on criteria such as Euclidean distance, correlation coefficient, etc., to identify the maximum similarity between unknown and known spectra [15]. As a powerful data analysis method, machine learning performs well in mining local features of signal data and extracting global features. It can identify and classify feature components submerged in large and complex data [16]. Several supervised and unsupervised algorithms have been used in conjunction with Raman spectroscopy, including principal component analysis (PCA) [17], orthogonal least squares discriminant analysis (OPLS-DA) [18], random forest (RF) [18], support vector machines (SVM) [19] , and deep learning methods [20]. As an essential branch of machine learning, deep learning can extract relevant information about labeled data from complex data sets containing non-linear classifiable data, which is more common than traditional methods [21]. Integrating SERS with deep learning significantly enhances its efficiency and accuracy in handling complex factors and large datasets [22], [23]. This synergy finds crucial applications in biomedical science [24], [25], [26], environmental protection [27], and food safety [28], offering improved analysis and detection capabilities. One of the common deep learning algorithms is the convolutional neural network (CNN), which was developed for the multi-classification task of analyzing pure compounds and binary and ternary mixtures and achieved satisfactory performance [29]. Additionally, it is effectively applied in the real-time detection of trace substances [30]. Facing the traditional deep learning method, which is limited to the classification of pure compounds, Mozaffari developed two 1-D CNN models, RaMixNet I and II, through data enhancement to realize the analysis of unknown test mixtures. The experimental results revealed that the classification accuracy of RaMixNet I and II was 100% for the analysis of unknown test mixtures. Simultaneously, the RaMixNet II model may achieve a regression accuracy of 88% for the quantification of each component [31]. Kunxia et al. developed a 1D CNN-based deep learning approach to distinguish three similar antibiotics, sulfadiazine, sulfamerazine, and sulfamethoxazole, within 0.005 to 10.00 μg/mL concentrations. This model, when compared to traditional machine learning methods such as PCA and Distributed Stochastic Neighbor Embedding, achieved a 100% accuracy rate. Additionally, the quantitative analysis model established using the 1-D CNN was successfully applied to the quantitative analysis of these three sulfonamide drugs [27].

This study proposed an intelligent analysis model for the SERS spectrum based on a deep learning algorithm, which could effectively identify the Raman spectra of 7 different antibiotic combinations (ACC=98.68%, 5-Fold Cross Validation=98.81%). Moreover, the explanatory analysis of Shapley Additive exPlanations (SHAP) showed that the CNN model could effectively assign the feature weights of the model's attention according to the feature peak distribution, indicating that the CNN model has good robustness. To verify the feasibility of this method, positive and negative samples of pure antibiotics were generated from a limited number of antibiotic Raman spectra for model training using the data enhancement method, which was employed to construct a spectral discrimination model for unknown mixtures. The findings of comparing the ratio of real samples to the predicted ratio of the model reveal that the CNN model, combined with the non-negative elastic net (NN-EN) algorithm, identifies components and proportions in the Raman spectra of all mixed antibiotics.

Methods and Materials

Chemicals and Instruments

Silver nitrate (AgNO₃), sodium citrate (Na₃C₆H₅O₇), and sulfosalicylic acid were all purchased from China National Pharmaceutical Group Co., Ltd. (Beijing, China). At the same time, ciprofloxacin, doxycycline, and levofloxacin were purchased from Sangon Biotech Co., Ltd. (Shanghai, China). Each material was utilized exactly as received, with no further purification required. All the magnetic stirring bars and glassware were soaked in aqua regia for 10 hours while triple-rinsed with ultrapure water from a Milli Q-Plus system (Millipore, Bedford, MA, USA).

Preparation of silver nanoparticles as SERS substrate

The methods to synthesize silver nanoparticles (AgNPs) have been used in our previous work [32], [33]. In particular, 200 mL of deionized distilled water and 33.72 mg of silver nitrate (AgNO₃) were added to a triangular flask while stirred and heated to boiling. Next, 8 mL of sodium citrate (Na₃C₆H₅O₇) was added and heated for 40 minutes at 650 rpm. After that, stop heating and continue stirring until the solution cools to room temperature. The prepared solution of 1 mL was transferred to a clean Eppendorf (EP) tube for 7 minutes of centrifugation at 7000 rpm, and then the supernatant was removed, and the pellet was resuspended with 100 μL of water, which was stored at room temperature for long-term use. The structures of AgNPs were characterized via UV-visible (UV-Vis) spectroscopy and transmission electron microscopy (TEM). The UV-Vis absorption spectrum showed that the absorption peak of AgNPs was located at 426 nm (Figure 1A), confirming the successful synthesis of AgNPs. Complementarily, the TEM image of the AgNPs is shown in Figure 1B. The results indicated that the as-prepared AgNPs were mono-dispersal, and the average diameters were 40 to 45 nm.

Figure 1.

(A) UV-vis spectrum of the AgNPs. The inset shows a photograph of the AgNP colloid. (B) TEM images of the AgNPs.

Sample Preparation and Raman spectroscopy measurements

The original powders of ciprofloxacin, doxycycline, and levofloxacin were weighed and dissolved in deionized water to prepare a 10⁻⁴ M stock solution. After that, further dilutions were conducted to prepare antibiotic solutions with concentrations of 10⁻⁵ M, 10⁻⁶ M, 10⁻⁷ M, and 10⁻⁸ M as standard solutions. Furthermore, the mixed solution of these antibiotics, ciprofloxacin, doxycycline, and levofloxacin, with a final concentration of 10⁻⁸ M, was prepared. All samples were sonicated for 10 minutes in a different ratio to determine homogeneity. In Raman spectroscopy experiments, 15 μL of each antibiotic solution at varied concentrations was mixed with 15 μL of the AgNPs solution and dropped onto a silicon wafer to form circular spots. Considering that the droplet deposition of AgNPs on silicon wafers may lead to the coffee ring effect, the 30 μL spots were dried in a safety cabinet in a controlled environment before detection. In particular, the droplet evaporated on the silicon wafer in a sealed Petri dish with 1 mL of distilled water to generate a high humidity level. The sealed Petri dish was placed into an incubator with a temperature set to 30 ˚C until the droplet was dried. The high-humidity environment slowed droplet evaporation, allowing the solvent to evaporate at a similar rate from the contact line to the droplet centers, hence preventing the formation of a coffee-ring structure. For further reference, please see the previously published article [34]. A portable Anton Paar Cora100^TM Raman spectrometer (Anton Paar Shanghai Trading Co., Ltd., China) was used to measure the Raman spectra of the samples at an excitation wavelength of 785 nm. This wavelength was strategically chosen for its ability to minimize fluorescence interference, a common issue with shorter wavelengths [35]. Additionally, the 785 nm wavelength falls within the near-infrared region, effectively balancing the need for efficient Raman scattering and reduced fluorescence, enhancing the sensitivity and efficiency of detection [36], [37]. In all experiments, the laser power was set to 25 mW, its spectral resolution was 1 nm, the spectral wave number resolution was 10 cm⁻¹, and its ability to detect the spectral range was 400-2300 cm⁻¹. For the calibration of all SERS spectra, the Raman peak at 520 cm⁻¹ was used as the reference peak, and the dark current was removed using integration time. In this work, a sample was dropped onto silicon water with 6 replicates, forming 6 individual spots. 100 spectra were randomly collected from each dried spot, with each spectrum undergoing an acquisition time of 3–4 seconds. However, due to equipment delays and spectrum generation in the handheld Raman spectrometer, the actual time for obtaining the raw spectral data varies. Taken together, 600 SERS spectra for each sample were generated for further analysis (Supplementary Table S1).

Spectral Deconvolution and Characteristic Peaks

The characteristic peaks of the Raman spectrum contain all the information about serum molecular vibration [38]. To fully explore the biological significance of spectral characteristic peaks, we use the Vogit function in Origin software to extract relevant peak information. Vogit is the convolution form of Lorentzian and Gaussian functions. It can effectively mine the same and different characteristic peaks in the Raman spectral signals of pure antibiotics and antibiotic mixtures. Specifically, in the Voigt linear function, the Gaussian and Lorentzian width shared values of all characteristic peaks were set to 1, then converged until fitting. According to the FitPeakCurve results, spectral deconvolution bands were drawn. The characteristic peaks corresponding to pure antibiotics in the two-way and three-way mixtures were distinguished by different colors. The molecular components corresponding to the characteristic peaks were obtained by searching the research literature of others.

Clustering Analysis of SERS spectra of Antibiotics

As a pattern recognition method, this study uses a clustering method to discover potential rules among antibiotic SERS data. It divides antibiotic combination data into clusters according to their similarity and dissimilarity. We chose the unsupervised learning algorithm TSNE and the supervised learning algorithm OPLS-DA for comparative analysis and evaluated the impact of spectral preprocessing on the clustering results. Specifically, the maximum and minimum normalization functions in the commercial analysis software Unscrambler X (Version 10.4 64bit, CAMO, Norway) were used to process the raw spectral data, and the intensity range of each SERS spectrum was controlled within the [0, 1] range inside. The preprocessed spectral and raw data are inputted into the TSNE and OPLS-DA algorithms for analysis. TSNE calls the TSNE function in the sci-kit learn (version 0.21.3) data analysis library, with the n_components parameter set to 2 for data fitting. OPLS-DA is implemented by multivariate statistical analysis software SIMCA (version 13.0, 32-bit) and uses three clustering indicators (R2X, R2Y, and Q2) to evaluate the results, where the difference between R2X and Q2 is within 0.3 indicates that the clustering results are better [18].

Traditional Machine Learning Analysis

To differentiate and predict the SERS signals of various antibiotics and determine the most effective identification model, this study employed five commonly used machine learning algorithms: Extreme Gradient Boosting (XGBoost), Linear Discriminant Analysis (LDA), Decision Trees (DT), Random Forest (RF), and Support Vector Machine (SVM), alongside the deep learning algorithm Convolutional Neural Network (CNN) for SERS spectroscopy analysis. Before performing ML analysis, we utilized the train_test_split function to partition the dataset into training, testing, and validation sets in a 6:2:2 ratio. The testing dataset, independent of model training, was solely used to evaluate the performance of each trained model. Before applying different machine learning algorithms to identify spectral data, we optimized the hyperparameters of each model using the GridSearchCV function. By setting predefined ranges for hyperparameters, we trained each model to obtain the best parameter combinations (Supplementary Table S2). Following hyperparameter optimization, we conducted five-fold cross-validation using the cv function, with parameters set to 5, to assess the robustness of the machine learning models.

Deep Learning Model Architecture

As a fast and accurate identification model, the convolutional neural network (CNN) is generally used to segment two-dimensional images [39]. This study follows the basic architecture of CNN and replaces each 2D layer in the CNN model with its corresponding layer in 1D space. During the analysis process, the network uses the input Raman spectrum as x to accurately predict y, identifying the category related to the spectrum. The goal is to construct a predicted value ý = f(x), where ý is the estimate of the ground truth output y. The CNN network structure mainly comprises an input, convolution, pooling, and fully connected layer. Before model training, use the train_test_split function to divide all spectral data into a training set, validation set, and test set in a ratio of 6:2:2, and use the Label Encoder function and to_categorical method in the Scikit-Learn package (version 0.21.3) to convert the sample labels in the dataset to label-encoded form. When the model is trained, the input layer receives 80% of the normalized data from the original data and uses 20% for model verification. The input data shape format is (636, 1). The convolutional layer extracts feature from spectral data through Conv1D and set different Kernel sizes to capture more detailed features. For each convolutional layer in the network, the Relu nonlinear function is used to improve the nonlinear fitting ability of the network as a whole. Pooling layer as a method of nonlinear down-sampling, which can reduce the representation size, the number of parameters, and the amount of computation. The pooling size and the stride we used were both 2. Before inputting the spectral signal into the fully connected layer, we use the flattened layer to lift and flatten the data into a 1D vector and then input it into the fully connected layer to calculate the probability distribution. This study only uses one fully connected layer, using the Softmax function as the activation function to predict the output, and the unit size is set to 7. The categorical cross-entropy loss function is used to judge the degree of difference between the predicted result and the real one. The adaptive moment estimation (Adam) function is used as an optimizer to train the CNN model due to its low operating memory and high computational efficiency. The Adam optimizers are set to learning_rate = 0.0001.

Training and Evaluation of Model

The dataset used for model performance evaluation has been normalized to improve model accuracy and fitting speed. The accuracy and loss curves of the training and verification under each Epoch are recorded during the training process to determine whether the model is overfitting. In real-world applications, users may only be interested in the correct predictions of the model samples in the measurements. However, a single value is insufficient to ensure the model's performance. Therefore, to make the deep learning model in this study reliable, we included various statistical measures in the sci-kit learn data analysis library, including accuracy, precision, recall, F1-score, and 5-fold cross-validation. To further observe the model's performance in each antibiotic category, use the confusion matrix function confusion_matrix to calculate the model's accuracy corresponding to each category. To provide a quantitative evaluation, use the ROC curve to test the model's diagnostic ability and calculate the area's AUC value under the curve according to the roc_curve and roc_auc_score methods. In addition, the explanatory analysis of the black-box properties of deep learning algorithms is often an unavoidable step in determining whether the model can be applied to real-world use, for which the shapDeepExplainer function in the explainable machine learning library SHapley Additive exPlanations (SHAP) is used to explore. The role of different Raman shifts in predicting antibiotic classes, SHAP was proposed by Lundberg and Lee to explain the importance of the model's output [40]. Unlike the original features, SHAP replaces each feature x_i with a binary variable:

$$\mathrm{g}\left(z^{\prime }\right)={\varphi }_0+\sum _{i=1}^M{\varphi }_i{z}_i^{\prime }$$

Where $z^{\prime }\in${0, 1}^M, $z^{\prime }$=1 if a feature is observed; otherwise, it is equivalent to 0. Also, M represents the number of input features. The SHAP value provides a way to estimate the contribution of each feature based on game theory and local interpretation. The formula is as follows:

$${\varphi }_i=\sum _{S\subseteq N\{i\}}\frac{\left|S\right|!\left(N-\left|S\right|-1\right)!}{n!}[v\left(S\cup \left\{i\right\}\right)-v(s)]$$

In explaining how much the model pays attention to the characteristics of Raman signals, we also analyze the overall classification process of the data within the model. Input the features extracted by the last convolutional layer (num_layer=5) into the TSNE function for visualization. The parameters are set to n_components=2, init='pca', random_state=0. Record the classification accuracy of models at different Epoch stages, observe the degree of dispersion between different types of sample points, and stop the training when the sample points are clustered within the group and dispersed between the groups.

Data Augmentation Impact and Pure Antibiotic Model Generation

Deep learning models provide a rapid way to detect Raman spectra of different antibiotic combinations in water environments. Still, it remains challenging to identify the proportion of pure antibiotics in a mixture. In the task of proportional identification, the identification model must be able to determine not only the type of mixture but also the composition arrangement in the mixture to achieve proportional prediction with a small error. This makes the amount of original collected spectral data insufficient to meet the training of high-accuracy models, so the data augmentation method is used to increase the sample size and improve the model's generalization ability. Specifically, different pure spectra are added in a random ratio for each input antibiotic Raman spectrum, and random Gaussian noise is added to obtain an augmented Raman spectrum dataset for each pure antibiotic. Augmented spectral bands include positive and negative spectra. The positive sample is a random combination of the spectra of selected antibiotics and other interfering antibiotics, and the proportion of selected compounds in each augmented spectrum is set to be not less than 10%. The negative spectrum is the random superimposition of SERS signals outside the selected antibiotics. During data augmentation, the maximum number of components is set to 2, and the noise rate is set to 0.5%. To avoid the bias between the augmented spectrum and the real sample, we calculated the Pearson correlation coefficient between the generated spectrum and the original spectrum to measure the reliability of the generated data. For each pure antibiotic, 15,000 positive and 15,000 negative spectra were generated for CNN model training, and three binary classification models for pure antibiotics were obtained (Supplementary Table S1).

Antibiotic Ratio Prediction by using non-negative elastic net

For a given mixture spectrum, the CNN model structure can be combined with the structure mentioned above to predict the composition of the mixture. The spectra of the mixture are parsed and read into the network model, and then all the pure spectrum-trained CNN models in the spectrum database are loaded. All the loaded models predicted the mixture spectrum to determine its components. A model's prediction result was the probability of a specific component. Setting a judgment threshold of 0.5 to judge whether a particular component exists in the compound when the result exceeds the threshold indicates that the antibiotic exists in the mixture. The model analysis results' false positive and negative rates evaluate the threshold setting comprehensively. After the judgment result is obtained, the proportional prediction work is performed. Here, we compare the results between the original data and the data preprocessed by Savitzky-Golay (SG) smoothing and adaptive iteratively reweighted penalized least squares (airPLS) baseline correction. All data are identified by Elastic Net (EN), a linear regression model that uses L1 and L2 norms as prior regular terms and achieves a relatively stable judgment result by comprehensively weighing the two algorithms of Lasso regression and Ridge regression [41]. Since the antibiotic concentration in the mixture should be non-negative (NN), setting a non-negative limit on the model output makes the results more reasonable.

Result.

SERS Reproducibility and Uniformity

The SERS spectra of Ciprofloxacin, Doxycycline, and Levofloxacin in the concentration range of 10⁻⁴∼10⁻⁸M in aqueous solution are shown in Figures 2A-C, where remarkably enhanced concentration-dependent Raman signals were observed. Moreover, calibration curves were generated for the characteristic Raman peaks of Ciprofloxacin at 1388cm⁻¹, Doxycycline at 1272 cm^-1, and Levofloxacin at 1400 cm⁻¹ plotting the intensity-concentration relationship, as presented in Figures 2D–F have a good linear relationship (R² > 0.95). In this study, a detailed analysis was performed to quantify the impact of changes in different signal acquisition points on the SERS signal to evaluate the reproducibility of the SERS signal further, and each sample in the figure represents the average of three spectra. The standard deviation (RSD) of the three antibiotics in the important characteristic peaks was within the acceptable range (Figure 2G–I)⁴², indicating that the SERS spectra collected in this study have a good presence.

Figure 2.

(A–C) Illustrates the results of the quantitative detection of ciprofloxacin, doxycycline, and levofloxacin in water at different concentrations ranging from 10^-6 M to 10^-9 M. Panels (D-F) depict the linear correlation between the concentrations of ciprofloxacin, doxycycline and levofloxacin in water and SERS intensity at significant Raman peaks. The histograms in panels (G-I) illustrate the distribution of the SERS intensity for major peaks of ciprofloxacin, doxycycline, and levofloxacin acquired from random spots on the SERS substrate.

Deconvoluted SERS Spectra and Characteristic Peaks

Spectral deconvolution realizes the deconstruction of the spectral signal of the mixture. The deconvolution bands show that the characteristic peaks of each pure antibiotic are separated (Figure 3). The corresponding pure antibiotics in different mixture spectra are shown in different colors. These three antibiotics share the bands 1556cm⁻¹ and 1620cm⁻¹, which contribute to the stretching of the C=O function group [43] and the C=C stretching vibration of aromatic rings [44]. In addition to these two characteristic peaks, the two-way combination of antibiotics ciprofloxacin and doxycycline exhibits a common characteristic peak at 1272 cm⁻¹, where CH2 rocking vibrations are observed [45]. The characteristic peak shared by ciprofloxacin and levofloxacin at 1460 cm⁻¹ corresponds to δ (CH₂) saturated lipids [46]. In addition to the common characteristic peaks, the unique peaks corresponding to each pure antibiotic are the key to distinguishing the SERS spectra of different antibiotics. The ciprofloxacin spectrum exhibited four primary characteristic peaks at 748 cm^-1, 784 cm⁻¹, 1322 cm⁻¹ and 1388 cm⁻¹. Specifically, the 748cm⁻¹ peak in ciprofloxacin was attributed to the methylene rocking modes⁴⁷. In contrast, the 784 cm⁻¹ peak as the main vibrational frequency corresponds to the symmetric vibration of the C–F bond [48], the peak at 1322 cm⁻¹ is assigned to ring breathing of stretching modes C_6a-C₇, C₉-C₁₀ [49], and symmetric and asymmetric C=O stretch was observed at a wavelength of 1388 cm⁻¹ [50]. The doxycycline contains two unique peaks: 1314 cm⁻¹ is assigned to C=C stretching [51], and the peak position at 1442 cm⁻¹ is attributed to the C=C stretch of the benzene ring [52]. On the other hand, the levofloxacin spectrum also demonstrated two primary characteristic peaks at 1340 cm⁻¹ and 1400 cm⁻¹. The 1340 cm⁻¹ unique peak corresponds to the D-band of carbons [53], while the 1400 cm⁻¹ peak can be assigned to the stretching of the pyridine ring [54].

Figure 3.

Deconvoluted Raman Gaussian-Lorentzian bands. The Raman shift and biological significance of each characteristic peak are shown.

Cluster Analysis of SERS Spectra of Different Antibiotics Combination

Two clustering algorithms, TSNE and OPLS-DA, were used to determine the spectral differences between different antibiotic combinations preliminarily. In the unprocessed original SERS spectrum data set, the TSNE clustering results showed (Figure 4A) that there was overlap within the same antibiotic spectrum group, and the distribution of sample points was relatively discrete. However, OPLS-DA (Figure 4B) can divide different types of samples, and the evaluation index score R2X=0.979, Q2=0.759, indicating that OPLS-DA can identify the potential rules of different types of SERS spectra to a certain extent. After the spectral data were “maximum and minimum normalized”, the degree of dispersion between different types of samples became higher, and the differences within the group decreased. Within the TSNE feature coordinate system, different antibiotic combinations are clustered into different clusters (Figure 4C). OPLS-DA provides quantitative evaluation indicators for the normalized spectral data (Figure 4D), R2X=0.978, Q2=0.836. The clustering performance has improved, but the overlap of some sample points is still inevitable. Therefore, we need to explore advanced chemometric analysis methods further to identify different antibiotic SERS spectra accurately.

Figure 4.

Schematic illustration of the clustering results of pure antibiotics and mixture used in this study by TSNE and OPLS-DA. (A) TSNE cluster analysis of raw data, (B) OPLS-DA cluster analysis of raw data, (C) TSNE cluster analysis of normalized data, (D) OPLS-DA cluster analysis of normalized data.

Model Evaluation and Interpretation Analysis

In this study, we constructed and compared the predictive capabilities of CNN with five traditional machine-learning algorithms, as depicted in Table 1. The results revealed that the deep learning model CNN achieved the highest predictive accuracy (98.68%), indicating its superior effectiveness in capturing the features of SERS spectroscopic signals, thereby enabling the effective identification of antibiotics in water environments. For the five traditional machine learning algorithms, it was observed that all algorithms exhibited high accuracy and robustness, with recognition accuracies exceeding 95%. This affirmed the applicability of traditional machine learning algorithms in predicting SERS spectra of antibiotics in water. Notably, the XGBoost algorithm demonstrated the highest utilization of computational resources, with a fitting time of 2.165 seconds. On the other hand, the Decision Trees (DT) algorithm exhibited the shortest reasonable time, at only 0.006 seconds, suggesting its capability for rapid differentiation of SERS signals of different antibiotics. However, despite its swift processing time, the DT algorithm recorded the lowest recognition accuracy, at 96.13%.

Table 1.

Comparison of CNN performance with five conventional machine learning algorithms for predicting SERS spectra of pure antibiotics and mixtures.

Algorithm	Accuracy	Precision	Recall	F1-score	5-Fold	Time
CNN	98.68%	100%	97.92%	98.94%	98.81%	0.385s
SVM	98.58%	98.58%	98.34%	98.58%	98.14%	0.057s
LDA	98.06%	98.06%	97.35%	98.06%	94.86%	0.182s
RF	97.44%	97.44%	96.69%	97.29%	94.54%	0.237s
XGBoost	96.15%	96.15%	93.98%	96.31%	95.18%	2.165s
DT	96.13%	96.13%	92.58%	96.17%	92.94%	0.006s

To better describe the model performance, we recorded the learning and loss curves during CNN training (Figure 5A). It can be seen that the accuracy and loss value of the model are close to each other in the training set and verification set, and the performance improves with the increase of Epoch. When the number of Epoch increases to 20, the model reaches the highest performance (ACC=99.01%) and tends to be more stable. To verify the model's performance with unknown data, we use 20% of the independent test data set as the input of the model file obtained from training. The results of different performance evaluation indicators show that the model has an accuracy rate of 98.68% in the test set, and the score of 5-fold validation is 98.81% (Figure 5B), indicating that the model has good robustness. In addition to various evaluation indicators, the ROC curve shows the trade-off between sensitivity and specificity of the deep learning algorithm. The curve is near the upper left corner, indicating the classifier performs better. In this study, the area AUC under the ROC curve of the CNN model is 0.9895, indicating the model has very high recognition performance (Figure 5C). The confusion matrix was used to condense the classification accuracy of the model. Our method had an average accuracy of 98.71% (Figure 5D), and only 5% and 4% misidentifications occurred in the spectral identifications of Ciprofloxacin and three antibiotic mixtures. In addition, SHAP analysis was used to analyze spectral data to understand how the model identified samples with different antibiotic combinations. Figure 5E depicts the SHAP importance feature summary map, ranking features according to the importance of the Raman shift. The X-axis is the influence distribution of the Raman shift on the model output, and the Y-axis represents the important Raman shift. Each point in the figure represents a sample on the test set. The color indicates the size of the feature value (red corresponds to a high value, blue corresponds to a low value), among which 788cm⁻¹ is the most important feature. The characteristic peak is the spectral characteristic peak corresponding to 784cm⁻¹ in spectral deconvolution, generated by the symmetric vibration of the C–F bond [48]. It is worth noting that when drawing the heat map of the distribution of important feature peaks (Figure 5F), it is found that the important features are mainly distributed in the three regions of 750-788cm⁻¹, 1207-1312 cm⁻¹ and 1624-1634 cm⁻¹, which is consistent with the distribution of characteristic peaks of the antibiotic Raman spectral. It shows that the characteristic peaks of different antibiotics were the basis for the model to identify different types of antibiotics.

Figure 5.

Deep learning model performance evaluation and comparisons in the testing dataset. (A) Training curve, (B) Evaluation index score, (C) ROC Curve, (D) Confusion matrix, (E) Distribution of the SHAP values for the significant characteristic peaks, the X axis represents the SHAP value, and the Y axis represents the Raman shift, (F) Heat map of significant characteristic peaks.

Regarding how the CNN model extracts the Raman spectral features of antibiotics and the classification process, we visualize and analyze the feature information output by the convolutional layer through T-SNE (Figure 6). The results found that with the increase in the number of model Epochs, the model's classification accuracy gradually increased, and the boundaries of the clusters of different sample points became clear. When Epoch=20, the model's classification accuracy reached its highest, ACC=98.18%. This is consistent with the model learning curve. Our proposed CNN algorithm can efficiently identify trace antibiotics in the water environment.

Figure 6.

Schematic illustration of how the number of iterations (epoch) influenced the classification effects of deep learning models. As the convolutional layers and the number of iterations continued to increase, the data in different categories changed from the original mixed state to the gradual separation. After the epoch reached 20, the model's recognition of the different antibiotic combination groups gradually became stable.

Ratio prediction dataset description and augmentation

To obtain a high-accuracy model to identify the proportion of pure antibiotics in mixed samples and reduce the chance of overfitting during training, we used data augmentation techniques to obtain sufficient samples to train the model. We calculated the Pearson correlation coefficient between the original and generated spectrum to avoid the deviation between the enhanced and original data distribution. Remarkably, the correlation of the generated spectra of the three pure antibiotics was more than 90% (Figure 7A, B, and C), indicating that the pure antibiotic is still the main component in the generated mixed signal. In addition, we mixed three pure antibiotics in varying proportions, resulting in nine combinations of two-way mixtures (Figure 7D, E, and F) and eleven combinations of three-way mixtures (Figure 7G). Mainly, Raman spectra of various antibiotic proportions show that when the concentration of a specific antibiotic increases, the distinctive peaks associated with that antibiotic become more apparent.

Figure 7.

The presentation of interpretive data in (A, B, and C) ciprofloxacin, doxycycline, and levofloxacin data augmentation (D) Specific ratios of the mixture of ciprofloxacin and doxycycline, (E) Specific ratios of the mix of ciprofloxacin and levofloxacin, (F) Specific ratios of the mixture of doxycycline and levofloxacin, (G) Specific ratios of the mixture of ciprofloxacin, doxycycline and levofloxacin.

Deep Learning Model of Antibiotic Composition

A single model that can quantify any two-way mixture without prior knowledge of its composition can address the practical problem of antibiotic mixing. This study uses three pure antibiotic models to predict the existence of three different antibiotics in the solution. Each model, dedicated to a specific antibiotic, analyzes solution samples and produces a probability output. If this probability exceeds 0.5, the corresponding antibiotic is considered present. After determining which antibiotics are current, NN-EN is used to predict the proportion of these antibiotics. In Raman spectroscopy analysis, the spectral characteristics of each compound are related to its concentration [55]. The NN-EN model integrates L1 and L2 regularization for regression analysis of spectral data, enabling the identification and estimation of different compounds' concentrations in a mixture [56]. L1 regularization introduces sparsity, pinpointing the most significant contributors to the mixture. Meanwhile, L2 regularization manages inter-feature correlations, ensuring stability in analyzing highly correlated spectral data [57]. In addition, we combine different spectral preprocessing methods with NN-EN to achieve the best identification results. The results demonstrate that our method can achieve excellent results in most identification tasks of mixed samples of two antibiotics without data preprocessing. However, in other cases, baseline correction (BC) can make a substantial difference in the performance of our model. Furthermore, when the ratio of the two antibiotics was 1:9 or 9:1 (ciprofloxacin with doxycycline, ciprofloxacin with levofloxacin, and doxycycline with levofloxacin), the model performance was improved by 1%-2% (Figure 8A, B, C). Rapid recognition can also be achieved when the two-way model is extended to three-way and other complex mixtures. In this study, eleven different mixtures of three antibiotics were analyzed to validate our method's general applicability further. The result showed that our model can still obtain good results in the original data. When the identification task becomes complicated, BC has outstanding performance in improving the model's performance, improving the model's recognition accuracy by 0.1%-1% in the identification process of six mixed samples. However, when faced with the identification task of a certain pure antibiotic only accounting for 10% of the mixed samples, the model will have an identification deviation of 3%–5% (Figure 8D). Furthermore, we computed the Root Mean Squared Error (RMSE) value for each group, which provides an interpretable measure of the average error magnitude⁵⁸. A smaller RMSE indicates better model predictions. The results indicate that in the majority of cases, the model predictions have a small error with the ground truth values. Detailed information on the prediction of mixture sample proportions and RMSE values is provided in Supplementary Table S3–6. These findings suggest that the deep learning model can generalize and distinguish the SERS bands of unknown antibiotics.

Figure 8.

Deep learning results for two-way and three-way antibiotic composition (A) ciprofloxacin and doxycycline mixture prediction, (B) Prediction of the ciprofloxacin and levofloxacin mixture ratio, (C) Prediction of doxycycline and levofloxacin mixture ratio, the X-axis represents the type of two-way mixture sample, and the Y-axis represents the prediction ratio value of two-way mixture (D) Prediction of ciprofloxacin, doxycycline and levofloxacin mixtures, The vertices of the triangle correspond to three antibiotics, and the axis values represent the proportions of each group. The position of a point is determined by the respective proportions of the three antibiotics in the mixture, forming the corresponding coordinates. Shapes of the same color represent one experimental group, with circles indicating actual proportions and triangles representing values predicted by the model.

Discussion

Due to the inevitable use of antibiotics in aquaculture, agricultural development, and daily life, their residues may accumulate in the human body through the food chain, causing great harm to human health [58]. Hence, identifying and quantifying antibiotics in water environments is increasingly important, and several studies have been published [10], [11]. However, many studies ignore the multivariate changes in the composition of antibiotics in the natural water environment [16], [21], which makes it difficult to determine the composition of the water body based on the antibiotic spectrum. In this work, we combined SERS with advanced deep learning technology to analyze Raman spectra of antibiotics with different concentration gradients and proportions and conducted a pilot study. Our analysis revealed that the convolutional neural network (CNN) achieved 98.68% accuracy in different antibiotic mixture recognition tasks. Furthermore, when combined with NN-EN, this technique could quantitatively detect different proportions of pure antibiotics in the mixture spectrum with high recognition accuracy. In this regard, we anticipate that the recognition range of our model can be evaluated and improved with the increase in antibiotic species. This method can be effectively extended to identify the antibiotic composition of any given mixture.

Specifically, this study used the concentration gradient dilution method to prepare antibiotic solutions of different concentrations and mix them with the same amount of negatively charged nano silver substrate to achieve direct labeling detection at 10⁻⁷ M concentration (Figure 2A, B, C). A comprehensive statistical analysis was performed to quantify the change in SERS signal strength during point-to-point measurements at different points to assess the uniformity of SERS substrates [59]. The results revealed a good linear relationship between the significantly enhanced Raman signal intensity and the concentration of the three antibiotics in the concentration range of 10⁻⁴-10⁻⁸M (Figure 2D, E, F). In addition, the calculated RSD values for these antibiotics are well within acceptable limits, indicating that our SERS substrates have good reproducibility in practical and biological contexts (Figure 2G, H, and I) [42]. However, due to the interference of coexisting components in mixed samples of antibiotics, noise, baseline, and systematic differences between Raman spectrometers, it is challenging for Raman spectroscopy to identify components in antibiotic mixtures. To overcome this limitation, we first adopt a spectral deconvolution method based on the vibration of fine molecules, which is successfully used to refine and distinguish the differences between spectra [20], [60]. The Raman spectra of the three pure antibiotics in this study are composed of multiple common and unique characteristic peaks, and the structural differences of the characteristic peaks between different spectra may be used as biomarkers for sample identification [61]. The characteristic peak of ciprofloxacin at 748 cm⁻¹ corresponds to methylene rocking modes [47], while the characteristic peak of doxycycline at 1314 cm⁻¹ is assigned to C=C stretching [51]. These characteristic peaks can provide the basis for the subsequent decision-making process of artificial intelligence algorithms.

The traditional chemometric methods have poor generalization properties [62]. Previous studies have shown that unsupervised clustering can be used to explore association rules preceding Raman spectroscopy [[20], [33]] . At the same time, supervised learning can be employed to develop an intelligent discriminant model for the prediction of spectral data [63], [64]. Here, we first use the unsupervised clustering algorithm t-Distributed Stochastic Neighbor Embedding (TSNE) and the supervised algorithm Orthogonal Partial least squares analysis (OPLS-DA) to understand and simulate the changes between and within different antibiotic clusters before and after normalization. Our findings indicate that the clustering results of TSNE and OPLS-DA before normalization are unsatisfactory, and the differences between groups are minor. In contrast, the normalized SERS data showed major inter-group differences and minor intra-group differences in both algorithms, and the scores of R2X, R2Y, and Q2 were satisfactory. We harnessed the clustering patterns developed within these mixtures to facilitate the classification and prediction of SERS spectra related to antibiotics to construct a robust deep-learning model. This model can effectively discern intricate antibiotic components within authentic water environments.

There are few methods to apply deep learning algorithms to Raman spectral datasets of antibiotics in water environments, and most of them are only for the classification of single antibiotics or a group of pure antibiotics [14]. A recent study showed promising results in a quantitative model of multiple chemical mixtures [21. However, this study only explored the differences between different combinations of compounds, and the content of each component still needs to be explored. In this study, deep learning was coupled with the simulation process to discriminate between the combination of antibiotics and the content of pure antibiotics. Through the training curve (Figure 5A), activation function, cross-entropy loss function, and various evaluation indicators (Figure 5B–D), the structure of the deep learning model is effectively verified and optimized, and the update of the model parameters is guided. The interpretability of the proposed model was evaluated by introducing Shapley Additive exPlanations (SHAP) (Figure 5E, F) to reveal the wavenumber bands activated when identifying the Raman spectra of target antibiotics. We observed that the model was able to specifically identify Raman characteristic peaks unique to different antibiotics, which were mainly distributed in three regions 750-788cm⁻¹，1207-1312cm⁻¹ and 1624-1634cm⁻¹, indicating that the proposed model can detect a pattern of differences between each antibiotic. These patterns can have the same effect as the characteristic peaks of Raman spectra observed by experts and have the reference potential for analyzing Raman spectra of antibiotics at the molecular level. In addition, the internal feature extraction process of deep learning and the spatial distribution of sample points can represent the performance of model pattern recognition [65]. The results before and after feature extraction showed that with the increase in iterations of the model, the distribution of spectral sample points of different antibiotic combinations gradually became discrete from the original aggregation, and the relationship between the groups was close (Figure 6).

Although the CNN model has achieved excellent performance in mixture identification, it is often insufficient for daily production safety to identify the mixture category alone, and the quantitative detection of pure antibiotic components in the mixture is an unavoidable problem⁶⁸. Data augmentation technology was used to solve the problem of a limited number of Raman spectra during training, verification, and testing to obtain a rapid identification method with controllable quality. Considering that the augmented spectrum has a strong correlation with the original spectrum (Figure 7A-C), the diversity of the original spectrum is increased by adding different proportions of spectral data and noise to the original spectrum without destroying the resolved Raman peak (Figure 7D-G). Through randomly discarding spectral intensity, the discriminant Raman features of some wave numbers can be partially destroyed, forcing the model to learn features from other wave numbers [66], thus improving the robustness of the model. The augmented data was used as CNN's input matrix, and the NN-EN algorithm was embedded to analyze the Raman spectrum of the mixture to realize the quantitative identification of pure antibiotics in the mixture samples. The algorithm didn’t need specific training of different mixture models, and only pure antibiotic data fitting can ensure the stability of the model. It can also prevent false negatives in the identification results due to the quadratic part of the penalty [67]. The results show that the CNN model combined with NN-EN can accurately identify the ingredients in the mixture (Figure 8). The systematic shift of Raman characteristic peaks is unavoidable in collecting Raman spectra of binary and ternary mixtures. Furthermore, after data normalization, the baseline and noise level of antibiotic samples with low-concentration components may be enhanced [68]. These factors may be why we predicted unstable performance at low concentrations of a pure antibiotic during the experiment (Figure 8). However, NN-EN can estimate their relative concentrations with a small error for pure sample identification in all cases. It shows that our method is accurate and universal for the component identification of various scenes. Taken together, the applicability of our approach in quality control and assurance could provide a rapid way to measure domestic water use, particularly in areas where antibiotics are overused. On a more fundamental level, the model could also be used to unify quality standards in the daily water industry and provide a reliable way to label products, assisting in public health safety and security.

Conclusion

In this study, we proposed a novel method for rapid and accurate detection of antibiotic residues in water using surface-enhanced Raman spectroscopy (SERS) and deep learning algorithms. By analyzing the average SERS spectra and deconvolution features of different antibiotic combinations, SERS spectra can reflect subtle differences between different antibiotic combinations. This was further confirmed by cluster analysis using OPLS-DA and TSNE. To facilitate qualitative and quantitative analysis of SERS spectra of antibiotic mixture datasets, this study effectively distinguished seven antibiotic combinations using a CNN model with an identification accuracy of up to 98.68%. In addition, a data augmentation method was used to generate positive and negative samples of pure antibiotics from a limited number of antibiotic Raman spectra. This method was used to construct a spectral discrimination model for unknown mixtures. Finally, the combination of the CNN model and non-negative elastic net (NN-EN) algorithm successfully predicted the components and proportions of all mixed antibiotic Raman spectra. This study has great potential for rapid identification and high-precision quantification of antibiotic residues in aquatic environments, which is significant for public health and environmental protection.

CRediT authorship contribution statement

Quan Yuan: Formal analysis, Software, Investigation, Writing - Original Draft. Lin-Fei Yao: Formal analysis, Software, Investigation, Writing - Original Draft. Jia-Wei Tang: Formal analysis, Software, Writing - Review & Editing. Zhang-Wen Ma: Investigation, Validation, Data Curation. Jing-Yi Mou: Investigation, Validation, Data Curation. Xin-Ru Wen: Validation, Data Curation. Muhammad Usman: Conceptualization, Methodology, Supervision. Xiang Wu: Conceptualization, Methodology, Resources. Liang Wang: Conceptualization, Methodology, Investigation, Writing - Original Draft, Writing - Review & Editing, Resources, Supervision, Project administration, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

The following are the Supplementary data to this article: