Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

Zhou, Hong; Xu, Liangge; Ren, Zhihao; Zhu, Jiaqi; Lee, Chengkuo

doi:10.1039/d2na00608a

Cited by 61 publications

(27 citation statements)

References 302 publications

(412 reference statements)

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“…To achieve this objective, chemometric analysis and machine learning techniques emerge as potent tools, which can efficiently handle these spectra and uncover valuable insights for SERS detection or spectral analysis. [236][237][238][239][240] Chemometric analyses, such as principal component analysis (PCA), partial least squares (PLS), and cluster analysis, are widely used for SERS spectra due to itheir ability to reduce data dimensionality and uncover hidden patterns. These techniques are particularly useful when dealing with large datasets or when the relationship between the spectra and analyte properties is not well understood.…”

Section: Chemometric and Machine Learning Analysis For Sers Spectramentioning

confidence: 99%

Unveiling practical considerations for reliable and standardized SERS measurements: lessons from a comprehensive review of oblique angle deposition-fabricated silver nanorod array substrates

Zhao,

Kumar,

Yang

2024

Chem. Soc. Rev.

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Section: Chemometric and Machine Learning Analysis For Sers Spectramentioning

confidence: 99%

Unveiling practical considerations for reliable and standardized SERS measurements: lessons from a comprehensive review of oblique angle deposition-fabricated silver nanorod array substrates

Zhao,

Kumar,

Yang

2024

Chem. Soc. Rev.

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“…CNNs are a kind of neural networks which employ filters and pooled layers in the architecture and often used if the size of the data set is large enough and if images are involved in the modeling [ 366 ]. Specifically, in the field of biophotonics, machine learning models using SERS can be efficiently classified into three domains: identification, classification, and quantification, with interests such as disease and molecular diagnosis [ 367 , 368 ]; microorganism classification, identification, etc. [ 369 , 370 , 371 , 372 ]; and cancer diagnosis [ 373 ], as shown in Figure 7 .…”

Section: Machine Learning In Sers-based Biosensingmentioning

confidence: 99%

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

2023

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Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS’s full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

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“…Rather than relying on human-crafted features, machine learning utilizes its unique learning capability to identify key features from the data set, building datadriven models. [282][283][284][285][286] In recent years, deep learning specifically has garnered much attention and transformed the field of data science. [287,288] The advancements in machine learning have broadened the scope of data-driven sensing applications.…”

Section: Waveguide-based Bio/chemical Sensormentioning

confidence: 99%

Recent Progress in Silicon‐Based Photonic Integrated Circuits and Emerging Applications

Xiao,

Liu,

et al. 2023

Advanced Optical Materials

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In recent years, with the further ministration of the semiconductor device in integrated circuits, power consumption and data transmission bandwidth have become insurmountable obstacles. As an integrated technology, photonic integrated circuits (PICs) have a promising potential in the post‐Moore era with more advantages in data processing, communication, and diversified sensing applications for their ultra‐high process speed and low power consumption. Silicon photonics is believed to be an encouraging solution to realize PICs because of the mature CMOS process. The past decades have witnessed a huge growth in silicon PICs. However, there is still a demand for the development of silicon PICs to enable powerful chip‐scale systems and new functionalities. In this paper, a review of the photonic components, functional blocks, and emerging applications for PICs is offered. The common photonic components are classified into several sections, including on‐chip light sources, fiber‐to‐chip couplers, photonic resonators, waveguide‐based sensors, on‐chip photodetectors, and modulators. The functional blocks of the PICs mentioned in this review are photonic memories and photonic neural networks. Finally, the paper concludes with emerging applications for further study.

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Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

Abstract: The world today is witnessing the significant role and huge demand for molecular detection and screening in healthcare and medical diagnosis, especially during the outbreak of COVID-19. Surface-enhanced spectroscopy techniques,...

Cited by 61 publications

References 302 publications

Unveiling practical considerations for reliable and standardized SERS measurements: lessons from a comprehensive review of oblique angle deposition-fabricated silver nanorod array substrates

Unveiling practical considerations for reliable and standardized SERS measurements: lessons from a comprehensive review of oblique angle deposition-fabricated silver nanorod array substrates

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Recent Progress in Silicon‐Based Photonic Integrated Circuits and Emerging Applications

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