Towards an improvement of model transferability for Raman spectroscopy in biological applications

Guo, Shuxia; Heinke, Ralf; Stöckel, Stephan; Rösch, Petra; Bocklitz, Thomas; Popp, Jürgen

doi:10.1016/j.vibspec.2016.06.010

Cited by 32 publications

(34 citation statements)

References 31 publications

(41 reference statements)

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“…In addition to MS and SA, the standard wavenumber calibration and the TR‐PLSR were performed as a comparison. It is worth noting that the two parameters of the TR‐PLSR, λ and ƞ , were optimized with a genetic algorithm by maximizing the mean sensitivity of the optimization samples instead of the accuracy as in a previous work . The results were shown in Figure b.…”

Section: Resultsmentioning

confidence: 99%

“…Two and five secondary spectra from each species were used as the reference dataset and optimization dataset, respectively. Parameters λ and ƞ were optimized with a genetic algorithm by maximizing the mean sensitivity of the optimization dataset . The results were presented in Figure S4.…”

Section: Resultsmentioning

confidence: 99%

“…The information on cultivation and measurement of three spore species B. mycoides DSM 299, B. thuringiensis DSM 350, B. subtilis DSM 347, and B. subtilis DSM 10 strains can be found in our previous publications . In summary, 1,592, 624, 654, and 848 Raman spectra were measured on four Raman spectrometers, almost equally distributed among the three bacteria species.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, model transfer for Raman‐based classification was reported by Guo et al, where a Tikhonov‐regularization‐based partial least squares regression (TR‐PLSR) method was developed. With TR‐PLSR, the model was updated with a number of Raman spectra measured under secondary conditions.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Model transfer for Raman‐spectroscopy‐based bacterial classification

Guo

Heinke

Stöckel

et al. 2018

J Raman Spectroscopy

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Raman spectroscopy has gained increasing attention in biomedical diagnostics thanks to instrumental developments and chemometric models that enhance the accuracy and speed of this technique. In particular, a model transfer procedure is needed if the chemometric models are utilized to predict a new dataset measured under (secondary) conditions different to the training data (primary). The model transfer methods try to achieve satisfactory prediction on the secondary dataset with minimal or no training samples measured under secondary conditions. Model transfer methods that have been reported are mostly applied for near‐infrared spectroscopy and in regression problems. The investigation of model transfer in Raman spectroscopy and classification is rare. Our recently reported Tikhonov regularization based on partial least squares regression (TR‐PLSR) was utilized for model transfer of Raman‐based classification models for spore species. In the present work, we show that the TR‐PLSR also works for Raman spectra of vegetative bacteria, even though the Raman spectra of 3 species of bacteria were acquired on 3 different Raman spectrometers. Additionally, we report 2 newly developed model transfer methods for Raman spectra: movement of principal components scores and spectral augmentation. Both methods were validated based on the Raman spectra of bacterial spores and vegetative bacteria, where a significant improvement of the model transferability was observed. The movement of principal components scores method yielded results comparable with those of the TR‐PLSR. However, the new methods are superior to TR‐PLSR in 2 ways: No training samples in the secondary conditions are necessary, and the methods are not restricted to partial least squares regression but can also be applied to other models. Both advantages are important in real‐world applications and represent a large step for improving the model transfer of Raman spectra.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model transfer for Raman‐spectroscopy‐based bacterial classification

Guo

Heinke

Stöckel

et al. 2018

J Raman Spectroscopy

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“…Currently, studies are concentrated on the similar questions for the same samples derived from different instruments, measurement conditions, or other aspects (called calibration transfer) [15][16][17][18][19]. Many algorithms have successfully resolved these calibration transfer, such as direct standardization (DS) [20,21], piecewise direct standardization (PDS) [22,23], orthogonal signal method (OSC) [24,25], wavelet transform (WT) algorithm [26,27], and canonical correlation analysis(CCA) [28,29].…”

Section: Introductionmentioning

confidence: 99%

Calibration Transfer of Soil Total Carbon and Total Nitrogen between Two Different Types of Soils Based on Visible-Near-Infrared Reflectance Spectroscopy

Liu

et al. 2018

Journal of Spectroscopy

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Although visible-near-infrared spectroscopy can rapidly and accurately determine soil nutrients without sample destruction, some problems remain unresolved, such as the mismatch of the established spectral model with different types of samples, limiting the wide application of this technology. Here, we took riverside and mountain soils as examples to explore the calibration transfer between two different types of soils by the WMPDS-S/B algorithm (wavelet multiscale piecewise direct standardization combined with Slope/Bias correction method) and by adding new samples. e predicted TN and TC concentrations improved significantly after being transformed. Compared with adding new samples, the WMPDS-S/B algorithm obtained more accurate results. e average relative errors dropped from 440.2% (without transformation) to approximately 6% for TN and from 342.0% to approximately 7% for TC. e maximum relative errors were reduced from 538.1% to less than 20% for TN and from 403.7% to less than 20% for TC. e RMSEP decreased from 2.42 to approximately 0.04 for TN and from 15.74 to approximately 0.4 for TC. e WMPDS-S/B algorithm had advantages in selecting fewer known samples and obtaining better prediction results. In contrast to past studies, which resolved the calibration transfer between different spectrometers and the measurement environment for the same samples, our study resolved the calibration transfer between different types of samples under the same spectrometer and the measurement environment. e former could only be used for correction among instruments, while the latter fundamentally solved the problem of model sharing across different samples.

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Calibration Transfer Methods

Ying

2022

Chemometric Methods in Analytical Spectroscopy Technology

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Towards an improvement of model transferability for Raman spectroscopy in biological applications

Cited by 32 publications

References 31 publications

Model transfer for Raman‐spectroscopy‐based bacterial classification

Model transfer for Raman‐spectroscopy‐based bacterial classification

Calibration Transfer of Soil Total Carbon and Total Nitrogen between Two Different Types of Soils Based on Visible-Near-Infrared Reflectance Spectroscopy

Calibration Transfer Methods

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