# Evaluation of Shifted Excitation Raman Difference Spectroscopy and Comparison to Computational Background Correction Methods Applied to Biochemical Raman Spectra

^{1}

^{2}

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Theoretical Approach

#### 2.2. Experimental Approach

^{−1}. The signal is dispersed onto a charge-coupled device (CCD) (PIXIS-400BR-eXcelon; Princeton Instruments) with a nominal quantum efficiency of up to 98% at 800 nm. The setup is controlled by in-house written data-acquisition software in LabView (National Instruments, Austin, TX, USA). The data acquisition was designed such that at each spatial location in the sample five Raman spectra at different excitation wavelengths, ranging between 784 nm to 786 nm with an interval of 0.5 nm, were measured consecutively. After all measurements for one spatial location were performed, the next location was measured.

## 3. Results

#### 3.1. Theoretical Approach

^{−1}, respectively (see Figure 2b). These values are much higher than commonly reported [37,67,68]. It is also visible in Figure 2b that when the shift in wavelength for lipid is higher than 7 nm the 1-acf decreases, resulting in a reduction of signal. The influence of the shift on the retained signal intensity can be best visualized by plotting the corresponding difference Raman spectra for lipids with and without noise, as seen in Figure 2c,d. For a shift of 1 nm (green) there is 47% of the signal retained, for a shift of 2 nm (black) 54% of the signal is retained and by shifting 4 nm (blue) a considerable part of the signal is retained (68%).

_{2}smaller than S

_{1}the SNR

_{SERDS}will be smaller or equal to half of the SNR

_{RAMAN}; for S

_{2}= S

_{1}SNR

_{SERDS}is 0; and for S

_{2}≥ 2S

_{1}SNR

_{SERDS}≥ SNR

_{RAMAN}/2. The last term indicates that the SERDS difference spectrum can locally have a higher SNR as a Raman spectrum. However, in general the total spectral SNR is of importance and the local SNR is not always meaningful. The summation in Equation (10) allows assessing the SNR over the entire measurable spectral region or a region of interest. Because the autocorrelation function for any reasonable shift is non-zero (see also Figure 2a), the total SNR of SERDS is always smaller than the SNR of Raman.

#### 3.2. Experimental Approach

^{−1}), four different shifts in excitation wavelength were measured in a 0.5 nm interval. In Figure 4 these different mean SERDS spectra of raw data for the lipid cluster of a tissue sample are shown. They show the same tendencies that could be seen in the simulations: the largest shift (blue) has the highest intensity, while the lowest can be observed for the smallest shift (black). This 0.5 nm shift is so small that the retained background is significant to the retained signal and cannot correct the spectra efficiently. It is also evident that the subtraction of raw spectra does not remove the background completely.

_{1}= 786 nm, λ

_{2}= 784 nm, λ

_{SERDS}= λ

_{1}− λ

_{2}) were used to generate the SERDS spectra. The EMSC correction was performed on the spectra measured at 785.0 nm.

_{n}bands between 2800 and 3100 cm

^{−1}result in not very pronounced difference bands. This shows that the small wavelength shift of 2 nm is not sufficient to have a clear separation of the broad CH

_{n}bands. Hence, the CH

_{n}bands are canceled out by subtracting the two spectra

**.**Z-score normalization and the subtraction optimization both result in a better spectral overlap of the background and provide a better correction than the area normalization. The remaining background of the difference spectra can be corrected, using a polynomial fitting approach.

## 4. Discussion

^{−1}. If the spectra are shifted by more than 7.5 nm, the correlation between the spectra will result in reduced signal after the subtraction. The reduction is due to an increase in an overlap between bands, which do not correspond to the same vibrations, being shifted into each other, resulting in misleading Raman difference information.

## 5. Conclusions

^{−1}(7 nm for 785 nm excitation) and for protein at 160 cm

^{−1}(10 nm for 785 nm excitation). The proposed wavelength shifts are rather large, and can lead to changes in fluorescence intensity or, in the worst case, can excite different fluorophores and result in completely different fluorescence profiles. The interpretation of the difference spectra can be challenging, since it is hard to determine the exact band positions. On the other hand, SERDS is advantageous because no previous knowledge of the background is necessary for a correction.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References and Notes

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**Figure 1.**Baseline corrected spectra and spectra with simulated fluorescence backgrounds and shot noise levels: (

**a**) Baseline corrected spectrum of lipids; (

**b**) Baseline corrected spectrum of proteins; (

**c**) Lipid spectra with added fluorescence intensities, i.e., 10 (blue), 8 (green) and 5 (red) times the maximal band of the lipid signal, including corresponding noise levels. The black spectrum is the lipid spectrum without fluorescence; (

**d**) Protein spectra with added fluorescence intensities, i.e., 10 (blue), 8 (green) and 5 (red) times the maximal band of the lipid signal, including corresponding noise levels. The black spectrum is the protein spectrum without fluorescence; (

**e**) Spectral bands and windows of the measured lipid spectrum, the black points are the maxima and the windows estimated by the Savitzky and Golay function; (

**f**) SNR of the lipid spectra with fluorescence, the black spectrum is the SNR of the lipid spectrum, the red spectrum correspond to the SNR of the lipid spectrum with fluorescence 5 times the maximal band of the lipid signal, the blue spectrum is the SNR of the lipid spectrum with fluorescence factor 10 of the lipid signal.

**Figure 2.**Optimal shift: (

**a**) Estimation by using the autocorrelation function of lipid and protein spectra (

**b**) Estimation by using the 1-autocorrelation function of lipid and protein spectra; (

**c**) Simulation of the difference spectra of lipid at 1 nm shift (green), 2 nm shift (black) and 4 nm shift (blue) with noise, the values are the percentage values of the retained signal; (

**d**) Simulation of the difference spectra of lipid at 1 nm shift (green), 2 nm shift (black) and 4 nm shift (blue) without noise.

**Figure 3.**EMSC and SERDS spectra for lipid and protein: (

**a**) Lipid spectrum without background (black); lipid spectrum with noise added (red, noise corresponds to the poison distribution of original spectrum and the added fluorescence five times the maximal band of the lipid signal); EMSC corrected lipid spectrum after adding a fluorescence background five times the maximal band of the lipid signal (green); absolute values of SERDS difference spectrum of lipid after shifting 4 nm (blue); (

**b**) Protein spectrum without background (black); protein spectrum with noise added (red, this noise corresponds to the poison distribution of original spectrum and the added fluorescence five times the maximal band of the protein signal); EMSC corrected protein spectrum after having fluorescence background five times the maximal band of the protein signal (green); absolute values of SERDS difference spectrum of protein after shifting 4 nm (blue).

**Figure 4.**Measured difference mean raw spectra of a lipid cluster at different excitation wavelength shifts (shift: 0.5 nm (black), 1.0 nm (red), 1.5 nm (green), 2.0 nm (blue)).

**Figure 5.**Comparison of measured lipid spectrum (

**a**,

**d**,

**g**) and two protein spectra (

**b**,

**e**,

**h**) and (

**c**,

**f**,

**i**). Raw SERDS difference spectra (2 nm shift, (

**a**–

**c**), blue); EMSC corrected spectra at 785 nm ((

**d**–

**f**), green); 1st derivative of the EMSC corrected spectra at 785 nm ((

**g**–

**i**), green); the vertical black lines mark band positions.

**Figure 6.**Comparison of different SERDS data processing methods with a 2 nm shift for (

**a**) measured lipid spectra and (

**b**) measured protein spectra. For every method the mean spectrum (darker shade) along with its standard deviation is shown. Black: difference spectra obtained by subtraction of raw data; red: difference spectra after area normalization; green: difference spectra after z-score normalization; blue: difference spectra after subtraction optimization.

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**MDPI and ACS Style**

Cordero, E.; Korinth, F.; Stiebing, C.; Krafft, C.; Schie, I.W.; Popp, J. Evaluation of Shifted Excitation Raman Difference Spectroscopy and Comparison to Computational Background Correction Methods Applied to Biochemical Raman Spectra. *Sensors* **2017**, *17*, 1724.
https://doi.org/10.3390/s17081724

**AMA Style**

Cordero E, Korinth F, Stiebing C, Krafft C, Schie IW, Popp J. Evaluation of Shifted Excitation Raman Difference Spectroscopy and Comparison to Computational Background Correction Methods Applied to Biochemical Raman Spectra. *Sensors*. 2017; 17(8):1724.
https://doi.org/10.3390/s17081724

**Chicago/Turabian Style**

Cordero, Eliana, Florian Korinth, Clara Stiebing, Christoph Krafft, Iwan W. Schie, and Jürgen Popp. 2017. "Evaluation of Shifted Excitation Raman Difference Spectroscopy and Comparison to Computational Background Correction Methods Applied to Biochemical Raman Spectra" *Sensors* 17, no. 8: 1724.
https://doi.org/10.3390/s17081724