Weighted spectral reconstruction method for discrimination of bacterial species with low signal-to-noise ratio Raman measurements

Zhu, Shenghuo; Cui, Xiaoyu; Xu, Wenbin; Chen, Shuo; Qian, Wei

doi:10.1039/c9ra00327d

Cited by 13 publications

(2 citation statements)

References 36 publications

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“…Due to the low signal-to-noise ratio of Raman spectroscopy from bacterial species and phenotypes, it is difficult to quickly and accurately characterize these biological samples ( Zhu et al, 2019 ). Thus, SERS was developed to enhance Raman scattering effects.…”

Section: Discussionmentioning

confidence: 99%

Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species

et al. 2021

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Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphylococcus species in order to test the capacity of different machine learning methods for bacterial rapid differentiation and accurate prediction. According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting Staphylococcus species via SERS spectra (ACC 98.21%, AUC 99.93%). Taken together, this study shows that machine learning methods are capable of distinguishing closely related Staphylococcus species and therefore have great application potentials for bacterial pathogen diagnosis in clinical settings.

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Section: Discussionmentioning

confidence: 99%

Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species

et al. 2021

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“…When the online full transmission mode collects the spectral signal of the sample, the incomplete signal is collected at both ends, so they are first eliminated before further data processing. The use of a weighting method signifies an effective strategy for spectral analysis ( Somers et al., 2011 ; Zhu et al., 2019 ). In this study, distinct weights are assigned to individual data acquisition locations based on either the signal-to-noise ratio ( SNR ) (quality assessment metric).…”

Section: Methodsmentioning

confidence: 99%

Determination of soluble solids content of multiple varieties of tomatoes by full transmission visible-near infrared spectroscopy

Li,

Wang

et al. 2024

Front. Plant Sci.

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IntroductionSoluble solids content (SSC) is a pivotal parameter for assessing tomato quality. Traditional measurement methods are both destructive and time-consuming.MethodsTo enhance accuracy and efficiency in SSC assessment, this study employs full transmission visible and near-infrared (Vis-NIR) spectroscopy and multi-point spectral data collection techniques to quantitatively analyze SSC in two tomato varieties (‘Provence’ and ‘Jingcai No.8’ tomatoes). Preprocessing of the multi-point spectra is carried out using a weighted averaging approach, aimed at noise reduction, signal-to-noise ratio improvement, and overall data quality enhancement. Taking into account the potential influence of various detection orientations and preprocessing methods on model outcomes, we investigate the combination of partial least squares regression (PLSR) with two orientations (O1 and O2) and two preprocessing techniques (Savitzky-Golay smoothing (SG) and Standard Normal Variate transformation (SNV)) in the development of SSC prediction models.ResultsThe model achieved the best results in the O2 orientation and SNV pretreatment as follows: ‘Provence’ tomato (Rp = 0.81, RMSEP = 0.69°Brix) and ‘Jingcai No.8’ tomatoes (Rp = 0.84, RMSEP = 0.64°Brix). To further optimize the model, characteristic wavelength selection is introduced through Least Angle Regression (LARS) with L1 and L2 regularization. Notably, when λ=0.004, LARS-L1 produces superior results (‘Provence’ tomato: Rp = 0.95, RMSEP = 0.35°Brix; ‘Jingcai No.8’ tomato: Rp = 0.96, RMSEP = 0.33°Brix).DiscussionThis study underscores the effectiveness of full transmission Vis-NIR spectroscopy in predicting SSC in different tomato varieties, offering a viable method for accurate and swift SSC assessment in tomatoes.

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Improving signal-to-noise ratio of Raman measurements based on ensemble learning approach

Jia,

Gao,

et al. 2024

Anal Bioanal Chem

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Weighted spectral reconstruction method for discrimination of bacterial species with low signal-to-noise ratio Raman measurements

Abstract: Raman spectra recovered from low SNR Raman measurements by weighted spectral reconstruction method show excellent preservation of information about bacterial identification.

Cited by 13 publications

References 36 publications

Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species

Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species

Determination of soluble solids content of multiple varieties of tomatoes by full transmission visible-near infrared spectroscopy

Improving signal-to-noise ratio of Raman measurements based on ensemble learning approach

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