Enhanced Data Augmentation using GANs for Raman Spectra Classification

Frischia, Stefano Di; Giammatteo, Paolo; Angelini, F.; Spizzichino, Valeria; Santis, Elena De; Pomante, Luigi

doi:10.1109/bigdata50022.2020.9377977

Cited by 6 publications

(7 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a solution to the problem, a hierarchical pipeline of data enhancement steps reinforced with the GAN method was presented by Frischia et al. [ 204 ] The data augmentation pipeline began with a natural individual Raman spectrum. It progressed to include several signal processing procedures or algorithms to artificially increase the number of spectra in the original dataset, such as by adding white Gaussian noise to the original signal, applying baseline removal algorithms, noise reduction filtering, clustering, shifting, and merging the data.…”

Section: Conclusion; Challenges and Perspectives For Ml‐assisted Anal...mentioning

confidence: 99%

Recent Progresses in Machine Learning Assisted Raman Spectroscopy

Jiang

et al. 2023

Advanced Optical Materials

View full text Add to dashboard Cite

With the development of Raman spectroscopy and the expansion of its application domains, conventional methods for spectral data analysis have manifested many limitations. Exploring new approaches to facilitate Raman spectroscopy and analysis has become an area of intensifying focus for research. It has been demonstrated that machine learning techniques can more efficiently extract valuable information from spectral data, creating unprecedented opportunities for analytical science. This paper outlines traditional and more recently developed statistical methods that are commonly used in machine learning (ML) and ML‐algorithms for different Raman spectroscopy‐based classification and recognition applications. The methods include Principal Component Analysis, K‐Nearest Neighbor, Random Forest, and Support Vector Machine, as well as neural network‐based deep learning algorithms such as Artificial Neural Networks, Convolutional Neural Networks, etc. The bulk of the review is dedicated to the research advances in machine learning applied to Raman spectroscopy from several fields, including material science, biomedical applications, food science, and others, which reached impressive levels of analytical accuracy. The combination of Raman spectroscopy and machine learning offers unprecedented opportunities to achieve high throughput and fast identification in many of these application fields. The limitations of current studies are also discussed and perspectives on future research are provided.

show abstract

Section: Conclusion; Challenges and Perspectives For Ml‐assisted Anal...mentioning

confidence: 99%

Recent Progresses in Machine Learning Assisted Raman Spectroscopy

Jiang

et al. 2023

Advanced Optical Materials

View full text Add to dashboard Cite

show abstract

“…Horgan et al presented a comprehensive framework for higher-throughput molecular imaging via DL-enabled Raman spectroscopy trained on a large dataset of over 1.5 million Raman spectra [14]. Frischia et al proposed a pipeline for augmenting data using GAN reinforcement [15], and Ma et al demonstrated a spectral recovery conditional GAN to reduce the data acquisition time [16]. The above studies used DL for applications using Raman spectrum data of non-hazardous substances, not of fatal chemical agents.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, the above studies trained their models using a very large number of Raman spectrum data. Specifically, the network model proposed in [15] aimed for data augmentation of the Raman spectrum, but training using thousands of Raman spectra should be required to perform data augmentation. However, in the military field, many tasks frequently require the recognition of rare or never before seen samples [18].…”

Section: Related Workmentioning

confidence: 99%

Distributed Raman Spectrum Data Augmentation System Using Federated Learning with Deep Generative Models

Kim

Lee

2022

Sensors

View full text Add to dashboard Cite

Chemical agents are one of the major threats to soldiers in modern warfare, so it is so important to detect chemical agents rapidly and accurately on battlefields. Raman spectroscopy-based detectors are widely used but have many limitations. The Raman spectrum changes unpredictably due to various environmental factors, and it is hard for detectors to make appropriate judgments about new chemical substances without prior information. Thus, the existing detectors with inflexible techniques based on determined rules cannot deal with such problems flexibly and reactively. Artificial intelligence (AI)-based detection techniques can be good alternatives to the existing techniques for chemical agent detection. To build AI-based detection systems, sufficient amounts of data for training are required, but it is not easy to produce and handle fatal chemical agents, which causes difficulty in securing data in advance. To overcome the limitations, in this paper, we propose the distributed Raman spectrum data augmentation system that leverages federated learning (FL) with deep generative models, such as generative adversarial network (GAN) and autoencoder. Furthermore, the proposed system utilizes various additional techniques in combination to generate a large number of Raman spectrum data with reality along with diversity. We implemented the proposed system and conducted diverse experiments to evaluate the system. The evaluation results validated that the proposed system can train the models more quickly through cooperation among decentralized troops without exchanging raw data and generate realistic Raman spectrum data well. Moreover, we confirmed that the classification model on the proposed system performed learning much faster and outperformed the existing systems.

show abstract

“…Spectroscopic techniques have been widely used for different purposes in various domains such as petrochemical [41,42], medical, pharmaceutical, and biological [43,44,45], food and agricultural [46,47,48,49], engineering [50] and material and geologic [51,52] analysis to monitor reactions and conditions of a final product.…”

Section: Related Workmentioning

confidence: 99%

Explainable Predictive Modeling for Limited Spectral Data

Akulich¹,

Anahideh²,

Sheyyab³

et al. 2022

Preprint

View full text Add to dashboard Cite

Feature selection of high-dimensional labeled data with limited observations is critical for making powerful predictive modeling accessible, scalable, and interpretable for domain experts. Spectroscopy data, which records the interaction between matter and electromagnetic radiation, particularly holds a lot of information in a single sample. Since acquiring such high-dimensional data is a complex task, it is crucial to exploit the best analytical tools to extract necessary information. In this paper, we investigate the most commonly used feature selection techniques and introduce applying recent explainable AI techniques to interpret the prediction outcomes of high-dimensional and limited spectral data. Interpretation of the prediction outcome is beneficial for the domain experts as it ensures the transparency and faithfulness of the ML models to the domain knowledge. Due to the instrument resolution limitations, pinpointing important regions of the spectroscopy data creates a pathway to optimize the data collection process through the miniaturization of the spectrometer device. Reducing the device size and power and therefore cost is a requirement for the real-world deployment of such a sensor-to-prediction system as a whole. Furthermore, we consider a wide range of machine learning models that have been proven to be successful for the prediction of the Cetane Number of fuels. We specifically design three different scenarios to ensure that the evaluation of ML models is robust for the real-time practice of the developed methodologies and to uncover the hidden effect of noise sources on the final outcome. The evaluation is performed for both the full model and reduced models using different feature selection techniques on a real dataset. Finally, we propose a correctness metric for the feature selection techniques to assess the conformance of the selected subset

show abstract

Enhanced Data Augmentation using GANs for Raman Spectra Classification

Cited by 6 publications

References 22 publications

Recent Progresses in Machine Learning Assisted Raman Spectroscopy

Recent Progresses in Machine Learning Assisted Raman Spectroscopy

Distributed Raman Spectrum Data Augmentation System Using Federated Learning with Deep Generative Models

Explainable Predictive Modeling for Limited Spectral Data

Contact Info

Product

Resources

About