Collaborative Low-Rank Matrix Approximation-Assisted Fast Hyperspectral Raman Imaging and Tip-Enhanced Raman Spectroscopic Imaging

He, Hao; Cao, Maofeng; Yue, Xiaxia; Xu, Mengxi; Wang, Lei; Ren, Bin

doi:10.1021/acs.analchem.1c02071

Cited by 10 publications

(7 citation statements)

References 21 publications

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“…A typical example is shown in Figure S7 for the TERS imaging of defective sites on a bimetallic sample. 30 In comparison with the raw images (Figure S7c,d), the contour of large defects in TERS image can more precisely reflect the original topographic image of the defect in the denoised images (Figure S7e,f). In addition, some tiny defects originally submerged in the noise can now be clearly identified.…”

Section: Applicable Scenarios Of S2s 221 Improved Spatial Resolution ...mentioning

confidence: 93%

“…At spectral SNR as low as 1.12 (Figure g), the originally unidentified boundaries in the three separate monolayer regions (bottom right) can still be seen after denoising (Figure k). Notably, the clarity of the hyperspectral images denoised by S2S has significantly improved over the spectrum-based denoising algorithms using peak intensity as the imaging signal, such as the unsupervised PEER (Figure p–s) or supervised CLRMA that are current top-performing denoising algorithms without requirement of reference (Figure t–w).…”

Section: Experiments Sectionmentioning

confidence: 99%

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Signal2signal: Pushing the Spatiotemporal Resolution to the Limit by Single Chemical Hyperspectral Imaging

Luo,

Zhao,

Cao

et al. 2024

Anal. Chem.

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There is growing interest in developing a high-performance self-supervised denoising algorithm for real-time chemical hyperspectral imaging. With a good understanding of the working function of the zero-shot Noise2Noise-based denoising algorithm, we developed a self-supervised Signal2Signal (S2S) algorithm for real-time denoising with a single chemical hyperspectral image. Owing to the accurate distinction and capture of the weak signal from the random fluctuating noise, S2S displays excellent denoising performance, even for the hyperspectral image with a spectral signal-to-noise ratio (SNR) as low as 1.12. Under this condition, both the image clarity and the spatial resolution could be significantly improved and present an almost identical pattern with a spectral SNR of 7.87. The feasibility of real-time denoising during imaging was well demonstrated, and S2S was applied to monitor the photoinduced exfoliation of transition metal dichalcogenide, which is hard to accomplish by confocal Raman spectroscopy. In general, the real-time denoising capability of S2S offers an easy way toward in situ/in vivo/operando research with much improved spatial and temporal resolution. S2S is open-source at https://github.com/3331822w/Signal2signal and will be accessible online at https://ramancloud. xmu.edu.cn/tutorial.

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Section: Applicable Scenarios Of S2s 221 Improved Spatial Resolution ...mentioning

confidence: 93%

Section: Experiments Sectionmentioning

confidence: 99%

Signal2signal: Pushing the Spatiotemporal Resolution to the Limit by Single Chemical Hyperspectral Imaging

Luo,

Zhao,

Cao

et al. 2024

Anal. Chem.

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“…To reduce the Raman measurement time, a hybrid PCA denoising algorithm was developed to extract the weak Raman signal from the noisy spectra measured by the R-NTA system. Inspired by the high-quality reference data assisted "CLRMA" method, 25 a small number of high SNR spectra from individual nanoliposomes were used as a training data set to construct a high quality PCA subspace. The contribution of each principal component to the SNR of the reconstructed spectra was calculated, as described in Equations S3−S5.…”

Section: Automated Rr-nta System and Its Process Flowmentioning

confidence: 99%

“…Here, the limited chemical composition also limits the sources of spectral variability. He et al recently proposed an adaptive extension to PCA denoising dubbed the Collaborative Low-Rank Matrix Approximation method "CLRMA", 25 which combines high SNR spectra with the target data to perform collaborative matrix decomposition and only selects those singular submatrices that contribute positively to the SNR. While the original CLRMA work was directed at denoising of Raman images of cells and tip-enhanced Raman images of inorganic materials, our goal is to measure nanoparticles whose signals may be orders of magnitude weaker.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Principal Component Analysis Denoising Enables Rapid, Label-Free Morpho-Chemical Quantification of Individual Nanoliposomes

Dai

Wang

et al. 2022

Anal. Chem.

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Laser tweezers Raman spectroscopy enables multiplexed, quantitative chemical and morphological analysis of individual bionanoparticles such as drug-loaded nanoliposomes, yet it requires minutes-scale acquisition times per particle, leading to a lack of statistical power in typical small-sized data sets. The long acquisition times present a bottleneck not only in measurement time but also in the analytical throughput, as particle concentration (and thus throughput) must be kept low enough to avoid swarm measurement. The only effective way to improve this situation is to reduce the exposure time, which comes at the expense of increased noise. Here, we present a hybrid principal component analysis (PCA) denoising method, where a small number (∼30 spectra) of high signal-to-noise ratio (SNR) training data construct an effective principal component subspace into which low SNR test data are projected. Simulations and experiments prove the method outperforms traditional denoising methods such as the wavelet transform or traditional PCA. On experimental liposome samples, denoising accelerated data acquisition from 90 to 3 s, with an overall 4.5-fold improvement in particle throughput. The denoised data retained the ability to accurately determine complex morphochemical parameters such as lamellarity of individual nanoliposomes, as confirmed by comparison with cryo-EM imaging. We therefore show that hybrid PCA denoising is an efficient and effective tool for denoising spectral data sets with limited chemical variability and that the RR-NTA technique offers an ideal path for studying the multidimensional heterogeneity of nanoliposomes and other micro/nanoscale bioparticles.

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“…Raman microscopy can provide label-free vibrational fingerprint information on both the enzyme and support materials . Moreover, Raman hyperspectral imaging offers the capability to simultaneously image multiple chemical species, with spatial information collected in the X–Y plane, while the spectral information is represented in the Z plane. − However, the complex background and potential peak overlapping of Raman hyperspectral imaging data limit its widespread application.…”

Section: Introductionmentioning

confidence: 99%

Modernized Machine Learning Approach to Illuminate Enzyme Immobilization for Biocatalysis

Wei,

Smith

2023

ACS Cent. Sci.

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Biocatalysis is an established technology with significant application in the pharmaceutical industry. Immobilization of enzymes offers significant benefits for commercial and practical purposes to enhance the stability and recyclability of biocatalysts. Determination of the spatial and chemical distributions of immobilized enzymes on solid support materials is essential for an optimal catalytic performance. However, current analytical methodologies often fall short of rapidly identifying and characterizing immobilized enzyme systems. Herein, we present a new analytical methodology that combines non-negative matrix factorization (NMF)an unsupervised machine learning toolwith Raman hyperspectral imaging to simultaneously resolve the spatial and spectral characteristics of all individual species involved in enzyme immobilization. Our novel approach facilitates the determination of the optimal NMF model using new data-driven, quantitative selection criteria that fully resolve all chemical species present, offering a robust methodology for analyzing immobilized enzymes. Specifically, we demonstrate the ability of NMF with Raman hyperspectral imaging to resolve the spatial and spectral profiles of an engineered pantothenate kinase immobilized on two different commercial microporous resins. Our results demonstrate that this approach can accurately identify and spatially resolve all species within this enzyme immobilization process. To the best of our knowledge, this is the first report of NMF within hyperspectral imaging for enzyme immobilization analysis, and as such, our methodology can now provide a new powerful tool to streamline biocatalytic process development within the pharmaceutical industry.

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Collaborative Low-Rank Matrix Approximation-Assisted Fast Hyperspectral Raman Imaging and Tip-Enhanced Raman Spectroscopic Imaging

Cited by 10 publications

References 21 publications

Signal2signal: Pushing the Spatiotemporal Resolution to the Limit by Single Chemical Hyperspectral Imaging

Signal2signal: Pushing the Spatiotemporal Resolution to the Limit by Single Chemical Hyperspectral Imaging

Hybrid Principal Component Analysis Denoising Enables Rapid, Label-Free Morpho-Chemical Quantification of Individual Nanoliposomes

Modernized Machine Learning Approach to Illuminate Enzyme Immobilization for Biocatalysis

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