Machine Learning-Assisted Raman Spectroscopy and SERS for Bacterial Pathogen Detection: Clinical, Food Safety, and Environmental Applications
Abstract
:1. Introduction
2. Raman and SERS: Fundamentals and Signal Enhancement
3. ML Techniques for Raman Spectroscopy: Traditional ML, CNNs, and Other Deep Learning Techniques
3.1. Unsupervised Machine Learning
3.2. Supervised Machine Learning
3.2.1. Traditional Machine Learning
3.2.2. Deep Learning (DL)
Convolutional Neural Networks (CNNs)
Other Deep Learning (ODL)
4. Three Case Studies Based on Modern Trends in ML-Enabled Raman Detection of Bacterial Pathogens
4.1. Case Study I: Decoding Bacterial Identity with CNNs
4.1.1. CNNs: Addressing Clinical Complexity in Bacterial Detection
4.1.2. RamanNet and Data Augmentation: Balancing Accuracy and Efficiency in Bacterial Detection
4.1.3. A Comparative Analysis of CNN-Based Raman Approaches for Bacterial Diagnostics
4.2. Case Study II: Enhancing Bacterial Detection by SERS
4.2.1. SERS: Unlocking Precision in Clinical Diagnostics
4.2.2. SERS: A Transformative Toolkit for Microbiology
4.2.3. Comparison of SERS-ML Approaches
4.3. Case Study III: Tackling Data Challenges and Expanding Raman Spectroscopy with ODL Techniques
4.3.1. Expanding Raman’s Reach with Limited Data
4.3.2. Beyond Bacteria: ODL Tackles Diverse Raman Applications
4.3.3. A Comparative Analysis of ODL Techniques for Raman-Based Bacterial Analysis
5. Limitations and Future Directions
- Data challenges: The development of robust, accurate models often depends on substantial, well-curated, and harmonized datasets. Initiatives for multi-institutional data sharing through accessible repositories with standardized metadata are essential to address smaller dataset limitations. Exploration of techniques like transfer learning and data augmentation also holds promise.
- Standardization: The lack of standardized protocols for sample preparation, spectral acquisition, and data analysis hinders reproducibility and clinical translation. Establishing best practices and guidelines will ensure reliable results across different laboratories and applications.
- Limited focus on quantification: Our review reveals a predominant focus on classification tasks in pathogen detection, highlighting an opportunity for further research into regression-based approaches for quantifying bacterial load. The study by Yan et al., as highlighted in Case Study II, demonstrates the potential of machine learning to accurately predict bacterial concentration using SERS, underscoring a promising avenue for future exploration.
- Explainable AI: While certain deep learning models deliver exceptional results, achieving a clear understanding of their decision-making processes remains a challenge. Developing explainable AI techniques, such as Grad-CAM, is vital for building trust in ML–Raman solutions, especially within the clinical context.
- Open questions: The case studies examined highlight exciting open questions for future research. These include the development of ODL architectures specifically tailored for Raman spectroscopy, computationally efficient models for real-time applications, and the pursuit of spectral biomarkers for early disease detection.
- Multimodal analysis: Integrating Raman spectroscopy with complementary techniques like microfluidics and mass spectrometry paves the way for comprehensive analysis. This offers richer insights into bacterial phenotypes, antibiotic resistance mechanisms, and single-cell dynamics—areas crucial for combating the AMR crisis.
- Harnessing GenAI’s potential: The integration of cutting-edge generative AI (GenAI) models holds significant promise for Raman spectroscopy and microbiology. These models can further enhance data generation, aid in spectral interpretation, and potentially uncover novel biological insights.
- Cross-field collaboration: Fostering interdisciplinary collaboration between experts in Raman spectroscopy, machine learning, and microbiology is paramount. By combining diverse knowledge and expertise, researchers can develop innovative ML–Raman solutions tailored to address specific biological challenges and clinical needs.
6. Conclusions
- Rapid point-of-care diagnostics: Clinicians, equipped with portable, ML-powered Raman devices, can swiftly identify the cause of an infection and determine the most effective antibiotic, preventing needless delays and improving patient outcomes.
- Precision-guided food safety: Rapid ML-SERS-based tests screen food production lines for harmful bacteria, overcoming adaptability challenges to safeguard consumers and prevent costly outbreaks.
- Decoding the microbial world: Researchers harness the power of ML and Raman to unlock insights into complex bacterial communities, unraveling the mysteries of microbial ecosystems and their impact on the environment and human health.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
Appendix A. Technical Details of SERS Enhancement Mechanisms
Appendix B
Sample Type | Specific Bacteria | Algorithm Category | Accuracy Metric | Input (No. of Spectra) | Raman Parameters | Ref. |
---|---|---|---|---|---|---|
Pure bacterial cultures | E. coli (2 strains), Shigella spp. (8 strains) | CNN | 99.64% | 1600 | Excitation: 784.56 nm, 25 mW grating: 600 L/mm, spectral range: 400–2300 cm−1, exposure = 60 s, objective = 100× | [87] |
Clinical isolates + AgNPs | 30 species, 9 genera | CNN | CNN: 99.80% (genus), 98.37% (species) | 17,149 | Excitation: 785 nm, 20 mW, spectral range: 65–2800 cm−1, exposure = 5 s | [96] |
Pure cultures | 30 isolates, 15 species | CNN | 84.7 ± 0.3% (isolate), 97 ± 0.3% (treatment ID) | ≈60,000 | N/A | [100] |
K. pneumoniae clinical isolates | K. pneumoniae (71 strains) | CNN | >94% for antibiotic resistance genes | 7455 | Excitation: 785 nm, 150 mW, grating: 1200 L/mm, spectral range: 390.79–1552.14 cm−1, objective = 50× | [101] |
N/A | N/A | CNN | 86.7% (isolate), 92.7% (MRSA/MSSA) | Paper 10 data | N/A | [89] |
Genomic DNA isolates | Brucella spp., Bacillus spp. | CNN | CNN: 96.33% | 843 | Excitation: 785 nm, 30 mW, grating: 600 L/mm, spectral range: 600–1700 cm−1, exposure = 60 s, objective = 100× | [85] |
Pure bacterial cultures + AuNPs | S. Enteritidis, S. Typhimurium, S. Paratyphi | CNN | 97% | 1854 | Excitation: 785 nm, 5 mW, spectral range: 550–1676 cm−1, exposure = 2 s | [62] |
Individual microbes and cells on Teflon | 12 species (Gram +ve and −ve) + fungi | CNN | 95–100% | ≈6000 per organism | Excitation: 532 nm, 20 mW | [102] |
Bacteria, archaea, yeast under various conditions | 14 species | CNN | 95.64 ± 5.46% | >4200 (train) 1400 (test) | Excitation: 785 nm, <16 mW, grating: 600 L/mm, spectral range: 600–1800 cm−1, exposure = 60–90 s, objective = 100× | [103] |
Lab-prepared isolates | S. aureus (MRSA/MSSA pair) + yeast | CNN | 82% (isolate), 97% (treatment), 89% (MRSA/MSSA) | 72,000 | Excitation: 633 nm, 13.17 mW, grating: 300 L/mm, spectral range: 381.98–1792.4 cm−1, background: poly fit (5) | [18] |
Mixed bacterial cultures | E. coli, S. aureus, S. typhimurium | Traditional ML | ANN: R2 > 0.95, RMSE < 0.06 | N/A | N/A | [84] |
Bacterial cultures | 6 distinct species | Traditional ML | >98% | 100 per species | Excitation: 532 nm, 0.3–0.4 mW, spectral range: 400–1800 cm−1, objective = 20× | [60] |
Clinical isolates, some cultured | 12 species (Gram +ve/−ve) + 2 fungi | Traditional ML | RF: 90.73% (species ID), 99.92% (antibiotic resistance) | >300 per species (train), 80 per species (test) | Integration time = 60–90 s | [91] |
Clinical isolates on aluminum | 9 species (Gram +ve/−ve) | Traditional ML | Simple filter: 92% (1 s/cell), DAE: 84% (0.1 s/cell) | ≈11,141 | Excitation: 532 nm, 7 mW, grating: 1200 L/mm, spectral range: 280–2186 cm−1, exposure = 0.01, 0.1, 1, 10, or 15 s, objective = 100× | [137] |
Clinical isolates + AgNPs | 117 S. aureus strains | Traditional ML | DBSCAN: 0.9733, Rand index, CNN: 98.21% Accuracy | 2752 | Excitation: 785 nm, spectral range: 519.56–1800.8 cm−1, exposure = 20 s | [90] |
Bacterial cultures on silver-coated slides | 30 species, 7 genera | Traditional ML | 86.23 ± 0.92% (all, single model); 87.1–95.8% (hierarchical) | 15,890 | Excitation: 532 nm, 5 mW, grating: 300 L/mm, spectral range: 400–1800 cm−1, objective = 20× | [19] |
Single prokaryotic cells | 3 bacteria, 3 archaea | Traditional ML | >98% | 40 per species | N/A | [86] |
Pure cultures on silicon wafer | E. coli ATCC 8739 | Traditional ML | N/A | N/A | Excitation: 532 nm, 8 mW, spectral range: 650–3300 cm−1, exposure = 0.033 s, background: poly fit (6) | [138] |
Bacterial isolates + AgNPs | S. aureus (MRSA/MSSA), L. pneumophila | Traditional ML | 97.8 ± 0.63% (kNN) | 230 | Excitation: 785 nm, 3 mW, spectral range: 550–1700 cm−1, exposure = 1 s, objective = 50× | [114] |
Milk, beef | E. coli O157:H7 (and others) | Traditional ML | Limit of detection: 6.94 × 101 CFU/mL, Recovery 86–128% | 2700 | Excitation: 633 nm, grating: 300 L/mm, exposure = 2 s, objective = 50× | [52] |
Heat-inactivated bacterial cells | B. mallei, B. pseudomallei, other Burkholderia spp. | Traditional ML | 95.5% sensitivity (core group), 83.4% sensitivity (others) | ≈ 200 per strain | Excitation: 532 nm, 7 mW, grating: 920 L/mm, spectral range: 15–3275 cm−1, exposure = 5 s, objective = 100× | [139] |
Bacteria from blood cultures | 8 common species | Other deep learning | 99.3% Gram type, 97.56% species, 98.5% MRSA/MSSA | 11,774 | Excitation: 632.8 nm, objective = 20× | [59] |
Clinical isolates + AgNO3 | A. xylosoxidans, B. cepacian, C. indologenes, + 12 others | Other deep learning | CNN: 99.86% | ≈6950 | Excitation: 785 nm, 20 mW, spectral range: 519.56–1800.81 cm−1, exposure = 5 s | [115] |
Extracellular vesicles (EVs) | 6 bacterial species | Other deep learning | >96% (Gram/species), 93% (strain), 87% (physiological) | 4335 | Excitation: 532 nm, 5 mW, grating: 300 L/mm, spectral range: 800–1800 & 2700–3200 cm−1, exposure = 9 s, objective = 100× | [98] |
Clinical isolates | ESKAPE pathogens | Other deep learning | 99.99% (training), 98.66% (validation) | >160 per species | Excitation: 633 nm, grating: 1200 L/mm, spectral range: 600–1700 cm−1, exposure = 20 s, objective = 100× | [129] |
Single bacterial cells (deep-sea) | 5 deep-sea strains | Other deep learning | 99.8 ± 0.2% | Initial: 300 per strain (augmented) | Excitation: 785 nm, grating: 1800 L/mm | [122] |
Partially covered CaF2 surfaces | 15 bacterial/non-bacterial classes, incl. MR/MS | Other deep learning | 96% (15 classes), 95.6% (MR/MS) | 5200 per species | Excitation: 785 nm, 60 mW, grating: 950 L/mm, spectral range: 700–1600 cm−1 | [130] |
N/A | N/A | Other deep learning | 86.3% (species-level), 97.84% (empiric treatment), 95% (antibiotic resistance) | Paper 10 data | N/A | [131] |
Clinical isolates + AgNPs | S. aureus (19 MRSA, 1 MSSA) | Other deep learning | 97.66% accuracy, 99.2% specificity, 96.1% sensitivity | ≈1699 per isolate | Excitation: 785 nm, 3 mW, grating: 1200 L/mm, spectral range: 550–1700 cm−1, exposure = 1 s, objective: 100× | [108] |
Tomato plant leaves | C. michiganensis subsp. michiganensis | Other deep learning | PCA + MLP: 99% Acc, 95% Spec, PCA + LDA: 97% Acc, 88% Spec | 177 (infected), 120 (healthy) | Excitation: 785 nm, 20 mW, grating: 1200 L/mm, spectral range: 800–1800 cm−1 exposure = 10 s, objective = 20× | [126] |
Pure bacterial cultures (intestinal pathogens) | 8 strains from Urechis unicinctus | Other deep learning | 94% isolation-level accuracy | 150 per strain | Excitation: 785 nm, grating: 600 L/mm, spectral range: 600–1800 cm−1, exposure = 60 s, objective = 100× | [132] |
Pure bacterial cultures | S. hominis, V. alginolyticus, B. licheniformis | Other deep learning | N/A | 100 per strain | Grating: 1200 L/mm, exposure = 60 s, objective = 100× | [88] |
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Techniques | Strengths | Weaknesses | Ideal Applications | Key References for Pathogen Detection |
---|---|---|---|---|
Unsupervised ML | ||||
PCA, K-means, hierarchical clustering, DBSCAN, etc. 1 | (1) No need for labeled data. (2) Automatic identification of groups or clusters. (3) Useful for exploratory data analysis. | (1) Limited ability to handle complex data structures. (2) Sensitive to initialization and parameter settings. (3) Difficult to interpret clusters in high-dimensional spaces. | (1) Preliminary analysis for bacterial identification. (2) Exploratory data analysis. (3) Clustering of bacterial spectra. | (1) PCA and hierarchical clustering for discriminating Raman DNA signatures of B. anthracis from B. cereus and B. thuringiensis [85]. |
Supervised ML | ||||
Traditional methods: SVM, RF, DT, KNN, ensemble methods, etc. 2 | (1) Effective for smaller datasets. (2) Computationally efficient. (3) Interpretable models. | (1) Limited ability to capture complex non-linear relationships. (2) Performance depends on feature selection and data quality. (3) Potential overfitting or underfitting issues. | (1) Rapid clinical diagnostics. (2) Analysis of mixed bacterial samples. (3) Bacterial identification and discrimination. | (1) RF for accurate classification of bacteria and archaea, identifying key biomolecules [86]. (2) RF for analysis of bacterial extracellular matrices (ECMs) [60]. |
CNN 3 | (1) Handles complex spectral data. (2) Automatic feature extraction. (3) Effective for classification tasks. | (1) Requires large, labeled datasets. (2) Computationally intensive. (3) Black-box models (low interpretability). | (1) Clinical diagnostics (rapid, complex samples). (2) Precise identification (antibiotic resistance). (3) Single-cell analysis and microbial ecology research. | (1) CNN for identifying bacterial isolates and predicting antibiotic treatments [18]. (2) CNN to distinguish between closely related Shigella spp. and E. coli strains [87]. |
ODL methods: ViT, aNN, ResNet, GANs, etc. 4 | (1) Effective for complex data and limited datasets. (2) Robust to complex samples and real-world noise. (3) Can handle sequential data and long-range dependencies. | (1) Computationally intensive. (2) Needs careful validation on diverse datasets. (3) Requires domain expertise to select the right ODL method. | (1) Analysis of complex clinical samples. (2) Analysis of rare/hard-to-culture samples. (3) Microbial ecology/strain-level analysis. | (1) ViT for rapid antibiotic resistance classification in clinical settings [59]. (2) GANs to enhance datasets for rare deep-sea bacteria analysis [88]. |
Challenge Addressed | Accuracy Metric | Key Insight | SERS (Y/N) | Ref. |
---|---|---|---|---|
Differentiating closely related pathogens | 99.64% | SERS + CNN for Shigella/E. coli differentiation | Y | [87] |
Clinical application, complex samples | CNN: 99.80% (genus), 98.37% (species) | SERS + CNN for clinical pathogens | Y | [96] |
CNN complexity, computational resources | 84.7 ± 0.3% (isolate), 97 ± 0.3% (treatment ID) | RamanNet: simplified CNN | N | [100] |
Urgent diagnostics for resistant/hypervirulent strains | >94% for antibiotic resistance genes | Raman–CNN for K. pneumoniae diagnostics | N | [101] |
Accuracy limitations of existing methods | 86.7% (isolate), 92.7% (MRSA/MSSA) | Multiscale DL for ID | N | [89] |
Identifying closely related pathogens beyond whole-cell analysis | CNN: 96.33% | DNA-based Raman + CNN | N | [85] |
Limited spectral variation between serovars | 97% | SERS + multiscale CNN for Salmonella serovars | Y | [62] |
Microbial contamination, complex matrices | 95–100% | CNN for diverse bacterial ID | N | [102] |
Microbial complexity, single-cell analysis | 95.64 ± 5.46% | Single-cell ID with Raman + ConvNet | N | [103] |
Subtle spectral differences, antibiotic resistance | 82% (isolate), 97% (treatment), 89% (MRSA/MSSA) | Successful MRSA/MSSA distinction | N | [18] |
Challenge Addressed | Sample Type | Key Insight | Algorithm Category | SERS Substrate | Input (No. of Spectra) | Ref. |
---|---|---|---|---|---|---|
Differentiating closely related pathogens | Pure bacterial cultures | SERS + CNN for Shigella/E. coli differentiation | CNN | AgNPs | 1600 | [87] |
Clinical application, complex samples | Clinical isolates | SERS + CNN for clinical pathogens | CNN | AgNPs | 17,149 | [96] |
Limited spectral variation between serovars | Pure bacterial cultures | SERS + multiscale CNN for Salmonella serovars | CNN | AuNPs | 1854 | [62] |
Analysis of mixed bacterial samples | Mixed bacterial cultures | SERS + ANN for mixed bacteria analysis | Traditional ML | Au@Ag@SiO2 | N/A | [84] |
Point-of-need ID, alternative taxonomy, complex ECMs | Bacterial cultures | SERS-based chemotaxonomy | Traditional ML | Ag Nanocubes | 100/species | [60] |
Algorithm selection, real-world complexity | Clinical isolates | Algorithms classify Staphylococcus via SERS | Traditional ML | AgNPs | 2752 | [90] |
Visual analysis limitations, rapid diagnostics | Bacterial isolates | SERS + ML reveals MRSA/MSSA biomarkers | Traditional ML | AgNPs | 230 | [114] |
Food safety, early detection, quantitative analysis | Milk, beef | SERS-LFA + XGBR for E. coli detection | Traditional ML | AuDTNB@Ag | 2700 | [52] |
Clinical sample complexity, antibiotic resistance, limited data | Bacteria from blood cultures | ViT for SERS, clinical focus | ODL | AgNPs | 11,774 | [59] |
Spectral consistency, clinical complexity | Clinical isolates | SERS + ML for pathogen classification | ODL | AgNO3 | ≈6950 | [115] |
Subtle differences, need for rapid methods | Clinical isolates | SAE-DNN for MRSA/MSSA in SERS | ODL | AgNPs | ≈1699/ isolate | [108] |
ODL Technique | Challenge Solved | Key Insight | Clinical Potential? | Ref. |
---|---|---|---|---|
Vision transformer (ViT) | Accurately identifies bacteria and antibiotic resistance in complex blood cultures | ViT-based SERS accurately determines antibiotic resistance and classifies clinical pathogens | Y | [59] |
CNN, recurrent neural network (RNN) variants | Variability and noise in clinical samples | SERS with deep learning enables robust classification of clinical bacterial isolates | Y | [115] |
Attention neural network (aNN) | Analyzing complex biological samples (bacterial EVs) | Raman with aNN identifies EVs, discovers biomarkers, and reveals EV biogenesis insights | Y | [98] |
Residual network (ResNet) | Limited datasets and complexity of clinical samples | ResNet deep learning accurately classifies ESKAPE pathogens in clinical samples | Y | [129] |
Progressive growing GAN (PGGAN) + ResNet | Limited and noisy spectral data (marine environment) | Raman, PGGANs, and ResNet accurately classify marine pathogens, overcoming data limitations | Promising | [122] |
Spectral transformer (ST) | Computational efficiency, handling sample variability, antibiotic resistance detection | Spectral transformers offer comparable accuracy with faster training and superior handling of sample variation for antibiotic resistance studies | Y | [130] |
U-Net | Information loss during deep learning training, classification of antibiotic resistance | U-Net improves Raman-based antibiotic resistance classification by reducing information loss | Y | [131] |
Stacked autoencoder–deep neural network (SAE-DNN) | Detecting subtle spectral differences for antibiotic resistance determination | SERS with SAE-DNNs accurately distinguishes antibiotic resistance profiles (MRSA vs. MSSA) | Y | [108] |
Multilayer perceptron (MLP) | Early disease detection (presymptomatic) in plants | Raman spectroscopy with MLP enables early plant disease detection prior to visible symptoms | Promising | [126] |
Long short-term memory (LSTM) | Accurate bacteria classification at the strain level, analysis of complex spectral data | LSTM deep learning differentiates bacterial strains and extracts subtle spectral information | Promising | [132] |
Generative adversarial network (GAN) | Limited and/or noisy spectral data | Raman with GANs and deep learning accurately classifies pathogens despite spectral challenges | Promising | [88] |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Rahman, M.H.-U.; Sikder, R.; Tripathi, M.; Zahan, M.; Ye, T.; Gnimpieba Z., E.; Jasthi, B.K.; Dalton, A.B.; Gadhamshetty, V. Machine Learning-Assisted Raman Spectroscopy and SERS for Bacterial Pathogen Detection: Clinical, Food Safety, and Environmental Applications. Chemosensors 2024, 12, 140. https://doi.org/10.3390/chemosensors12070140
Rahman MH-U, Sikder R, Tripathi M, Zahan M, Ye T, Gnimpieba Z. E, Jasthi BK, Dalton AB, Gadhamshetty V. Machine Learning-Assisted Raman Spectroscopy and SERS for Bacterial Pathogen Detection: Clinical, Food Safety, and Environmental Applications. Chemosensors. 2024; 12(7):140. https://doi.org/10.3390/chemosensors12070140
Chicago/Turabian StyleRahman, Md Hasan-Ur, Rabbi Sikder, Manoj Tripathi, Mahzuzah Zahan, Tao Ye, Etienne Gnimpieba Z., Bharat K. Jasthi, Alan B. Dalton, and Venkataramana Gadhamshetty. 2024. "Machine Learning-Assisted Raman Spectroscopy and SERS for Bacterial Pathogen Detection: Clinical, Food Safety, and Environmental Applications" Chemosensors 12, no. 7: 140. https://doi.org/10.3390/chemosensors12070140