Improved performance of near infrared excitation Raman spectroscopy using reflective thin-film gold on glass substrates for cytology samples

Barton, Sinead J.; O’Dwyer, Kevin; Butler, Margaret H.; Dignam, Adam; Byrne, Hugh J.; O’Neill, Luke A.J.; Hennelly, Bryan M.

doi:10.1039/c9ay01672d

Cited by 5 publications

(6 citation statements)

References 39 publications

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“…A spectrum was recorded from a prostate cancer cell line (PC3) fixed to a calcium fluoride substrate with 785 nm laser illumination and additional information on the sample preparation and system parameters may be found at Ref. [8]. Let it be noted that spectrum (b), as shown in Figure 6, is an estimation of expected collected irradiance that has been achieved by taking the average across a dataset of spectra.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…The effect of these noise sources can be alleviated if the system configuration/application has no restrictions in terms of cost or time. However, in many cases this is not practical [ 4 , 5 , 6 , 7 , 8 ]. It must also be noted that even with an optimal Raman spectrometer and the most carefully designed experimental procedure, a noisy Raman spectrum is unavoidable for many sample types.…”

Section: Introductionmentioning

confidence: 99%

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Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra

Barton

Alakkari

O’Dwyer

et al. 2021

Sensors

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Raman spectroscopy is a powerful diagnostic tool in biomedical science, whereby different disease groups can be classified based on subtle differences in the cell or tissue spectra. A key component in the classification of Raman spectra is the application of multi-variate statistical models. However, Raman scattering is a weak process, resulting in a trade-off between acquisition times and signal-to-noise ratios, which has limited its more widespread adoption as a clinical tool. Typically denoising is applied to the Raman spectrum from a biological sample to improve the signal-to-noise ratio before application of statistical modeling. A popular method for performing this is Savitsky–Golay filtering. Such an algorithm is difficult to tailor so that it can strike a balance between denoising and excessive smoothing of spectral peaks, the characteristics of which are critically important for classification purposes. In this paper, we demonstrate how Convolutional Neural Networks may be enhanced with a non-standard loss function in order to improve the overall signal-to-noise ratio of spectra while limiting corruption of the spectral peaks. Simulated Raman spectra and experimental data are used to train and evaluate the performance of the algorithm in terms of the signal to noise ratio and peak fidelity. The proposed method is demonstrated to effectively smooth noise while preserving spectral features in low intensity spectra which is advantageous when compared with Savitzky–Golay filtering. For low intensity spectra the proposed algorithm was shown to improve the signal to noise ratios by up to 100% in terms of both local and overall signal to noise ratios, indicating that this method would be most suitable for low light or high throughput applications.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra

Barton

Alakkari

O’Dwyer

et al. 2021

Sensors

Self Cite

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“…Glass slides (76 × 26 × 1 mm; Marienfeld GmbH & Co. KG, Lauda-Königshofen, Germany) were coated with ~100 nm thick gold and an ~10 nm titanium adhesive layer 56 , 57 . Glass slides were cleaned by ultrasonication in trichloroethylene, acetone, isopropyl alcohol, and pure water and dried by centrifugation.…”

Section: Methodsmentioning

confidence: 99%

Glycan chip based on structure-switchable DNA linker for on-chip biosynthesis of cancer-associated complex glycans

et al. 2021

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On-chip glycan biosynthesis is an effective strategy for preparing useful complex glycan sources and for preparing glycan-involved applications simultaneously. However, current methods have some limitations when analyzing biosynthesized glycans and optimizing enzymatic reactions, which could result in undefined glycan structures on a surface, leading to unequal and unreliable results. In this work, a glycan chip is developed by introducing a pH-responsive i-motif DNA linker to control the immobilization and isolation of glycans on chip surfaces in a pH-dependent manner. On-chip enzymatic glycosylations are optimized for uniform biosynthesis of cancer-associated Globo H hexasaccharide and its related complex glycans through stepwise quantitative analyses of isolated products from the surface. Successful interaction analyses of the anti-Globo H antibody and MCF-7 breast cancer cells with on-chip biosynthesized Globo H-related glycans demonstrate the feasibility of the structure-switchable DNA linker-based glycan chip platform for on-chip complex glycan biosynthesis and glycan-involved applications.

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“…A variety of substrates including aluminum coated glass, thin-lm gold coated substrate, borosilicate glass, synthetic fused silica, extra white soda lime glass and others, have been previously investigated to overcome the limitations of weak Raman SNR and high costs associated with the traditional CaF 2 substrate. 27,28,45 Stainless steel has the notable advantages of being easily accessible, robust/durable, relatively cheap and highly biocompatible. In our study, BC cells were found to easily attach, multiply and retain expected morphology with equal propensity on stainless steel compared to CaF 2 substrate (Fig.…”

Section: Papermentioning

confidence: 99%

Enhanced characterization of breast cancer phenotypes using Raman micro-spectroscopy on stainless steel substrate

Thomas

Fitzgerald

Gautam³

et al. 2023

Anal. Methods

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Improved performance of near infrared excitation Raman spectroscopy using reflective thin-film gold on glass substrates for cytology samples

Abstract: Thin-film gold substrates offer improved performance and cost for NIR excitation Raman spectroscopy of biological cells when compared with CaF2. We demonstrate a 1.65 times enhancement in the signal to noise ratio with <5% of the cost.

Cited by 5 publications

References 39 publications

Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra

Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra

Glycan chip based on structure-switchable DNA linker for on-chip biosynthesis of cancer-associated complex glycans

Enhanced characterization of breast cancer phenotypes using Raman micro-spectroscopy on stainless steel substrate

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