## 4 Discussion
Tumor tissue is characterized by lower spectral contributions of collagenous proteins relative to non-collogenous proteins, which is detectable in both spectral ranges. A smaller spectral range was chosen due to unspecific variations that will be further studied in upcoming work. In the urological context, Raman-based techniques also offer potential applications in kidney diseases [31]. Raman spectroscopy of urinary calculi was already reviewed in 1997 [32], and progress in the analysis and classification of kidney stones has recently been reported [33]. Raman spectra were used to identify cystine-type kidney stones [34], calcium-oxalate-type kidney stones [35], and phosphate-type kidney stones [36]. Kidney stones are known for their intense autofluorescence, which has even been used for experimental evaluation of human kidney stone fluorescence spectra [37]. Chemical bleaching of renal stones was one approach to reduce fluorescence in Raman spectra at 532 nm laser excitation [38]. Other approaches analyzed human urinary stones by FT-Raman [39; 40] and a portable dispersive Raman system [14], both at 1064 nm excitation to suppress unwanted fluorescence background. Raman chemical imaging using a confocal Raman microspectrometer generated maps of the constituents' distribution in a case study of structural analysis of kidney stones [41]. It was concluded that (i) a huge autofluorescence background interferes with the detection of low-concentrated and poor Raman scatterers, and (ii) this analysis is time-consuming. The Raman imaging spectrometer described here allows a faster and more sensitive way to map kidney stones due to the larger laser diameter and absence of fluorescence background. Furthermore, the lower confocality of our fiber probe instrument would facilitate the mapping of uneven stone surfaces. Preliminary experiments in Figure 5 compare the quality of 1064 nm excited Raman spectra of four kidney stones with 785 nm excited Raman spectra that were collected with a state-of-the-art instrument (RNX1 microprobe, Kaiser Optical System, Ann Arbor, MI, USA) used in a previous study [17]. A quantitative analysis of the signal-to-noise ratios is complicated considering the quantum efficiency and the dark noise of InGaAs versus CCD detectors, the exposure time, the laser intensity, and the wavelength\({}^{-4}\) dependent light scattering. According to this law, the scattered band intensities are expected to decrease by a factor of 0.3 = 1064\({}^{-4}\)/785\({}^{-4}\). Qualitative comparisons are given instead. With the same exposure time of 5 s, the better signal-to-noise ratio with the 1064 nm instrument is due to the higher laser power and enables to assign stone #1 to Ca\({}^{2+}\) hydrogen phosphate. The intensity of the 987 cm\({}^{-1}\) band at 450 mW of the 1064 nm laser is estimated as 8000 counts above a 2000 counts background, whereas the intensity of this band at 100 mW of the 785 nm laser (maximum power determined at the sample plane) is reduced to 2000 counts above an elevated 10,000 counts background. To avoid detector saturation, the 785 nm laser power was reduced to 25 mW, and the acquisition time was set to three exposures of 2 s for stones #2 and #3 and six exposures of 1 s for stone #4, totaling 6 s. Despite the reduced laser power, the background increases to 15,000, 100,000, and 200,000 counts for stones #2, #3, and #4, respectively. The most intense bands in stone #2 at 1649, 1037, and 628 cm\({}^{-1}\) show ca. 3000 counts above the background, and virtually no bands are resolved in stones #3 and #4 with a darker appearance. At unchanged 1064 nm laser power and 5 s exposure time, acceptable background between 1000 and 10,000 counts and maximum band intensities between 5000 and 20,000 counts enable to assign stone #3 to Ca\({}^{2+}\) oxalate monohydrate and stones #2 and #4 to cystine types. This clearly demonstrated the benefits of 1064 nm Raman spectroscopy, and more data will be collected in the future.