Raman spectroscopy-based adversarial network combined with SVM for detection of foodborne pathogenic bacteria

Du, Yuwan; Han, Dianpeng; Li, Sha; Sun, Xuan; Ning, Baoan; Han, Tao; Wang, Jiang; Gao, Zhixian

doi:10.1016/j.talanta.2021.122901

Cited by 38 publications

(21 citation statements)

References 35 publications

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“…Considering the structure–spectrum bidirectional relationship, it will in principle also be possible to make use of well-developed ML strategies of language translation (e.g., seq2seq, attention, transformers). Studies − applying generative adversarial networks to extract descriptors for the Raman spectrum predictions are also known, and same NN structure can potentially be used in the aspect of molecular representations. Besides, ΔML (an algorithm that learns from the corrections from low-level theory to high-level theory), which has been applied in other fields of computational chemistry (e.g., force field development), can also be utilized for achieving high accuracy. − …”

Section: Discussionmentioning

confidence: 99%

A Concise Review on Recent Developments of Machine Learning for the Prediction of Vibrational Spectra

2022

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Machine learning has become more and more popular in computational chemistry, as well as in the important field of spectroscopy. In this concise review, we walk the reader through a short summary of machine learning algorithms and a comprehensive discussion on the connection between machine learning methods and vibrational spectroscopy, particularly for the case of infrared and Raman spectroscopy. We also briefly discuss state-of-the-art molecular representations which serve as meaningful inputs for machine learning to predict vibrational spectra. In addition, this review provides an overview of the transferability and best practices of machine learning in the prediction of vibrational spectra as well as possible future research directions.

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Section: Discussionmentioning

confidence: 99%

A Concise Review on Recent Developments of Machine Learning for the Prediction of Vibrational Spectra

2022

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“…Du et al proposed a method based on Raman spectroscopy combined with generative adversarial network and multiclass SVM to classify foodborne pathogenic bacteria. Better classification results are obtained by optimizing the parameters of the multiclass SVM [52] . Shu et al developed a deep learning guided fiberoptic Raman diagnostic platform to assess its ability of real-time in vivo nasopharyngeal carcinoma diagnosis and post-treatment follow-up patients.…”

Section: Related Workmentioning

confidence: 99%

Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19

Zhao

Zhai

Shao

et al. 2023

Computer Methods and Programs in Biomedicine

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“…GANs aim to generate fake data by training a pair of competing networks, the generator and the discriminator. GANs are used in a variety of fields, such as image synthesis, semantic image editing, style transfer, image super-resolution, and classification [24,25]. In addition, the system utilizes the DAE to alleviate the excessive noise included in the data generated by the GAN.…”

Section: Related Workmentioning

confidence: 99%

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

Kim

Lee

2022

Sensors

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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.

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Raman spectroscopy-based adversarial network combined with SVM for detection of foodborne pathogenic bacteria

Cited by 38 publications

References 35 publications

A Concise Review on Recent Developments of Machine Learning for the Prediction of Vibrational Spectra

A Concise Review on Recent Developments of Machine Learning for the Prediction of Vibrational Spectra

Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19

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

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