Diffuse reflectance spectroscopy and Raman spectroscopy for label-free molecular characterization and automated detection of human cartilage and subchondral bone

Sensors & Actuators: B. Chemical 301 (2019) 127121

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Diffuse reflectance spectroscopy and Raman spectroscopy for label-free molecular characterization and automated detection of human cartilage and subchondral bone

T

Lucas Kreißa,c, ,1, Martin Hohmannb,c, ,1, Florian Klämpflb,c, Sebastian Schürmanna,c, Faramarz Dehghanid, Michael Schmidtb,c, Oliver Friedricha,c, Lorenz Büchlere ⁎

⁎

a

Institute of Medical Biotechnology (MBT), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Paul-Gordan-Straße 3, 91052 Erlangen, Germany Institute of Photonic Technologies (LPT), FAU, Konrad-Zuse-Straße 3/5, 91052 Erlangen, Germany Erlangen Graduate School in Advanced Optical Technologies (SAOT), Paul-Gordon-Straße 6, 91052 Erlangen, Germany d Institut für Anatomie und Zellbiologie, Martin Luther Universität Halle-Wittenberg, Große Steinstr. 52, 06097 Halle (Saale), Germany e Klinik und Poliklinik für Orthopädische Chirurgie Inselspital, Universität Bern, 3010 Bern, Switzerland b c

ARTICLE INFO

ABSTRACT

Keywords: Diffuse reflectance spectroscopy Raman spectroscopy Bayesian inference Bone Cartilage Label-free Machine learning

Optical technologies hold potential for bio-medical research as well as the clinical routine, since they allow contact-free, non-destructive access to several clinically relevant features of tissues. In this study, diffuse reflectance spectroscopy and spontaneous Raman spectroscopy have been applied to human hip samples in order to characterize differences between cartilage and adjacent subchondral bone tissue. Mathematical decomposition of absorption and scattering properties is an important aspect in diffuse reflectance data analysis. Here, we present the novel application of Bayesian statistics to this analysis, which can extract probability density spectra of the absorption coefficient and the scattering coefficient. Using this concept of a Bayesian decomposition algorithm for the spectral decomposition, it was possible to find characteristic differences in the relative concentration of melanin and haemoglobin as well as in scattering properties. Furthermore, this allowed a reconstruction of probability density plots for scattering coefficient and absorption coefficient instead of singular values. Complemented by the results of Raman spectroscopy, it was further possible to detect significant differences in the ratio of minerals (such as hydroxyl apatite) to bio-molecules (such as proteins) when comparing bony tissue regions to the surrounding cartilage. Finally, it was possible to use these molecular contrast mechanisms for highly-accurate, automated tissue classification using machine learning algorithms on the level of individual patients.

1. Introduction Accurate recognition of tissue boundaries is an active field of biomedical research [1]. Ideally, the experimental setup should be contactfree, minimally invasive and provide reliable molecular or structural analysis. Furthermore, label-free technologies, functioning without external markers (e.g. fluorescent antibodies), are advantageous, since they allow analysis of native tissues under natural conditions. Such techniques hold potential as possible feedback mechanism during surgery [2] or as diagnostic tools to support physicians and scientists. These requirements can be matched by several advanced optical technologies, such as laser induced breakdown spectroscopy (LIBS) [3], multiphoton microscopy (MPM) [4], spontaneous Raman spectroscopy

[5] and diffuse reflectance spectroscopy (DRS) [6]. In contrast to LIBS and MPM, Raman spectroscopy and DRS show less limitations regarding costs and potential usage in the clinical routine. Raman spectroscopy is based on the inelastic scattering of photons at molecules with discrete vibrational and rotational energy levels [5,7]. Diode lasers with narrow emission spectra are usually used to illuminate samples, the back scattered light is collected and detected by a spectrometer. Well-established software tools can correct for unwanted background from autofluorescence [8]. Raman spectroscopy is a widely used method in many fields [9] such as cancer research [10–12]. It has already been used to determine age-related changes in the concentration of Amid I and III in human bone [13]. In DRS, samples are usually illuminated by a white light source and

Corresponding authors. E-mail addresses: [email protected] (L. Kreiß), [email protected] (M. Hohmann). 1 Equally shared First-author. ⁎

https://doi.org/10.1016/j.snb.2019.127121 Received 13 June 2019; Received in revised form 28 August 2019; Accepted 9 September 2019 Available online 28 September 2019 0925-4005/ © 2019 Elsevier B.V. All rights reserved.

Sensors & Actuators: B. Chemical 301 (2019) 127121

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the diffusely reflected light is analysed by a spectrometer. This analysis is targeting the optical absorption and/or the scattering properties of a sample. In the case of biological tissues, the most prominent absorbers are usually haemoglobin, melanin and water, depending on the spectral range of interest. The composition of these absorbers can be characteristic for certain types of tissues [14,15]. Mathematical models are used for the deconvolution of effects from different absorbers and scatterers. By extracting the absorption coefficient of tissues, it was possible to monitor blood volume and oxygenation level inside of blood vessels ex vivo [16,17]. The scattering properties on the other hand, can be linked to the micro-structure within the sample [17,18]. It is wellknown that the spectral slope of the scattering coefficient is correlated to the average size of the main scatterers [17,18], and it has been shown that the scatterer size distribution [17] as well as the thickness of joint cartilage in human knee [19] can be determined using DRS. Finally, the overall reflectance signal, composed of absorption and scattering properties, can be used for automated classification of different tissue types. This has been demonstrated for the differentiation between fat and nerve [14] or for the in vivo detection of dysplasia [20]. Preliminary data have also shown differentiation between cartilage and different bone types [15]. Here, we present an approach using a combination of DRS and Raman spectroscopy for the investigation of human tissue biopsies. This experimental setup is rather simplistic and requires cheaper hardware components. Although it does not target the molecular 3-dimensional tissue structure, it can give access to the relative concentration of certain bio-molecules as well as basic conclusions regarding structural parameters. The accuracy of the mathematical decomposition into the different contributions from absorbtion and scattering strongly depends on the statistical variance within the data. It is often overlooked that this variance plays another important role in the mathematical decomposition, since errors can be propagated or even amplified through the mathematical model. Often, uncertainty is seen as a purely statistical variation of the measurement data, while the overall uncertainty arising from data and its propagation through models is neglected in some cases. To the present, mathematical model parameters in DRS are usually stated as single values and vary strongly between different studies on the same type of tissue [21], while the observed differences between different types of tissue are within the same range of variability [21]. Therefore, a reliable method for the decomposition in DRS that allows estimation of the robustness of the model and includes statistical variance as well as error propagation is crucial. In this context, Bayesian inference provides a powerful tool for mathematical decomposition of experimental data. In Bayesian statistics, each parameter of a mathematical model is represented as probability densities (PD), including statistical variance of data as well as error propagation within the model. Therefore, Bayesian inference is the recommended tool for experimenters in several, rather different fields of research [22–25]. The combination of DRS, Bayesian decomposition of scattering and absorption and Raman spectroscopy is believed to be synergistic by giving access to the composition of main absorbers complemented by structural information and more in-depth analysis of biochemical composition. Beyond that, the obtained data from each of the modalities was used for automated classification by machine learning algorithms that allow an accurate tissue recognition on the level of individual patients. In order to evaluate the potential of these approaches within an application of clinical relevance, DRS and Raman spectroscopy were applied to ex vivo tissue samples from the hips of patients suffering from femoroacetabular impingement (FAI). FAI reflects morphological changes of the hip joint that lead to a reduced range of motion and abutment of the femoral head against the acetabular rim. It is a significant cause of clinical symptoms among young and active patients as well as athletes [26,27]. The annual incidence rate of groin pain among

soccer players is between 5% and 18% [28]. It has been reported that between September 2000 and April 2005, 36% of athletes who required arthroscopic surgery had been diagnosed with FAI [27]. In addition to the high prevalence, there is strong evidence that symptomatic FAI is a risk factor for later development of osteoarthritis of the hip [29–34]. The changes in molecular composition and micro-structure that occur prior to and during FAI are still widely unknown. In this context, the proposed approach is used to analyse molecular differences between subchondral bone and the surrounding cartilage. Both are regions of interest for the diagnosis as well as for the therapy of FAI. A reliable tool, which allows contact-free analysis of these tissues, could support scientists and surgeons working on FAI. Beyond the potential impact to FAI alone, a successful application of the proposed approach might also pave the way for label-free, optical technologies in bio-medical applications in general. 2. Methods In order to demonstrate the potential of DRS, Raman spectroscopy and a Bayesian decomposition of DRS data, a study was performed on 21 hip biopsies from 18 human patients. The research was carried out in accordance with the Declaration of Helsinki and has been approved by the Kantonale Ethikkomission Bern (KEK – decision from October, 15 2015). From each patient, one or two samples were taken during surgical procedure at the orthopaedic clinic of the University in Bern, Switzerland. After the procedure, the samples were stored in a 4% formaldehyde solution (Roti-Histofix 4%, Carl Roth) for several weeks up to a few months. The samples were first scanned by DRS (see Fig. 1A) and then by Raman spectroscopy (see Fig. 1B). The area under investigation was the location where the femoral neck is impinging against the acetabulum. Due to the different colouring, a macroscopic identification of white cartilage and coloured bone was easily possible (see Fig. 1C and D). From each of these samples, 10-20 recording points were taken across the whole sample. The data from one patient were excluded from the set since the sample only consisted of bone and did not contain a cartilage rim. 2.1. Diffuse reflectance spectroscopy, experimental setup Fig. 1A shows the experimental DRS setup used in this study. During the experiments, the recording points are selected randomly across the sample to minimize bias from the experimenter. The basic set-up consists of a 200 W white light source (Oriel Apex Arc lamp source, Newport Corporation, Irvine, USA) coupled to a 50 μm bifurcated fibre (2m QBIF50-VIS-NIR, Ocean Optics, Dunedin, USA) with a source-detectorseparation of 200 μm. The second fibre tip is connected to a spectrometer (QE65000 Spectrometer, Ocean Optics, Dunedin, USA). The combined end of the fibres is placed a few millimetres above the sample. Before the recording of each sample, the dark noise (D) is measured and subtracted from each measurement. A plate of BASO4 powder (Shimadzu, Japan) is used as reference sample for the maximal reflectance (R). Mathematically, this calibration procedure can be described by Eq. (1):

R% =

M R

D × 100% D

(1)

where M is the measured spectrum and R% the calibrated spectrum. At every recording point, five spectra are acquired in the range from 450 to 800 nm. Each of these five spectra is the averaged spectrum of four scans with an integration time of 50 ms. Thus, a single, averaged spectrum was recorded within 200 ms and the recording time for each measurement spot was 1 s. In total, 1330 spectra are acquired, of which 725 are from bone and 605 are from the adjacent cartilage reference tissue. 2

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Fig. 1. Combined diffuse reflectance and Raman spectroscopy of human hip biopsies. (A) Schematic of the DRS set-up. White light is used to illuminate the sample and the back-reflected light is analysed in a spectrometer. (B) Schematic of the Raman set-up. A narrow bandwidth diode laser is coupled to a Raman probe with a focal length (fd) of 7.5 mm. The second fibre end of the probe is connected to a spectrometer to detect the back scattered signal. (C) A typical human biopsy from the hip of a patient with femoroacetabular impingement. The image section with a higher magnification shows white rim of cartilage (green arrow) and centre part of the tissue consisting of subchondral bone (red arrow). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

2.2. Spontaneous Raman spectroscopy

is assumed that this peak is independent of the sample under investigation. According to Takahashi et al. [37], formaldehyde shows Raman peaks at 542, 911, 1046, 1239 and 1492 cm−1. All of these values from literature have been validated with the setup used in this trail (see Supplementary Fig. 1). Here, the peak at 537 cm−1 is chosen for normalization, since the others have strong overlaps with neighbouring peaks or are less distinct which would lead to stronger fluctuations from post-processing. After normalization, the statistical distribution of the peak values throughout all spectra is analysed. For this purpose, the mean of the peak values is plotted with the standard error of the distribution. The p-value of each peak is deduced by a Wilcoxon rank sum test (ranksum, The MathWorks, Inc., Narick, MA, USA). The entire post-processing procedure is displayed in Supplementary Fig. 2.

The basic set-up consists of a diode laser source (LASER-785-LABADJ-S, Newport Corporation, Ocean Optics, Dunedin, USA) emitting a multi-mode beam at a wavelength of 785 nm with a bandwidth of 0.05 nm (2.4 cm−1) and was operated at 149 mW. The light is coupled into a fibre of a coupled fibre probe (RIP-RPB-785-SMA-SMA, Ocean Optics, Dunedin, USA) by an SMA 905 coupling connector. The lens at the tip of the fibre probe has a working distance of 7.5 mm with a lateral spot size of 158 μm and a depth of field of 2.2 mm. Inside the probe, excitation and collection light are separated by a filter and the collected signal is coupled into a different fibre. Finally, this fibre is connected to a spectrometer (QE65000 Spectrometer, Ocean Optics, Dunedin, USA). In total, 594 spectra were recorded in the range from 810 to 940 nm or 400 to 2100 cm−1, of which 336 are from bone and 258 from cartilage tissue. For each measurement, four spectra were averaged with an integration time of 5 s each. From each spot, three spectra are acquired. Thus in total, 12 measurements are done per spot, which are averaged to three effective measurements. With this procedure, a single spot was measured in 60 s. Between 8 and 15 measuring points are selected randomly over each sample to reduce bias from the experimentalist. Due to the fact that the Raman signal of tissue with an excitation wavelength of 785 nm shows strong autofluorescence, a correction procedure is required. First, all three raw spectra per measurement spot are averaged and cropped to the spectral range of 200–2000 cm−1. Then, a three pixel-wide one-dimensional median filter is applied to the spectra, in order to remove any artefacts from the sensor readout. The final noise reduction is done with a discrete wavelet transform (DWT) with a denoising parameter of k = 2 and a maximum number of wavelet scales of Jmax = 2 [35,36]. The DWT is used due to the fact that it is peak-preserving and is known to show good results with Raman spectra [36]. After the de-noising, the autofluorescence is calculated with an asymmetric least square fit (ALS) with an asymmetric weight of p = 0.001 and a regularization parameter of λ = 73 [8]. This autofluorescence background is then subtracted. Finally, the spectra are normalized to the Raman peak of formaldehyde. Due to the fact that the same formaldehyde concentration is used for all samples, it

2.3. Diffuse reflectance spectroscopy, Bayesian decomposition Usually, diffuse reflectance spectra contain a broad signal of the absorption and scattering properties of the sample. Furthermore, the signal has a periodic component of higher frequency which can be used to conclude the size distribution of scatterers close to the surface via a Fourier transformation as shown by Perelman et al. [17]. Typically, the broad signal of diffusive light can be approximated by expression such as the following [17]:

Id ( , s ) = F (s )· Ii ( , s )·

1 e (µa + µs) L 1 + (µa / µs )

(2)

where Id is the intensity of the diffusive light, F(s) describes the angular dependence of the light, Ii is the incident light intensity, L is representing the layer thickness and μs and μa are the scattering and absorption coefficients. Typically, μs and μa are derived from Monte Carlo simulations or other a priori knowledge [17]. This intensity is then used to compute the reflectance R¯ (k ) , since the recorded spectra are already normalized to the incident light (see Eq. (1)):

1 e (µa + µs) L R¯ = F (s )· 1 + (µa / µs )

(3)

Further simplifications are done by setting L = 0.2 cm, to reduce the 3

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amount of parameters and make the fit more stable. This parameter is chosen by running a general Bayesian estimate and selecting the most likely one with a low amount of repetitions. In order to further reduce the number of unknown parameters, the constant scaling factor F(s) is set to the maximum reflectance signal of the averaged spectra. Instead of relying on Monte Carlo simulations or educated guesses to approximate scattering and absorption, all unknown parameters and their respective uncertainty are only obtained from the proposed model (Eq. (3)) and the recorded data themselves. For this purpose, the scattering coefficient μs and the absorption coefficient μa are modelled and then inserted into Eq. (3). The simulated scattering coefficient μs is approximated by [21]:

µs ( ) = a ·

* ( ) = 51.9· µa,mel

µa ( ) = (1

2.303· ( )·cm = cm·µa* ( ) wm

S )·cm,Hb·µa*,Hb ( ) + S·cm,Hb·µa*,HbO2 ( ) + cm,mel·µa*,mel ( ) (7)

where S is the saturation of oxygen in haemoglobin and the c-parameters represent the relative concentration of haemoglobin or melanin, respectively. Before inserting μa into Eq. (3), all reference spectra are interpolated to match the spectral resolution of the recorded data. Bayesian statistics. Fitting this model to the measured data yields a highly ill-posed, inverse problem of a non-linear equation with five unknown parameters, which is targeted by using Bayesian inference [23,24], a priori knowledge and a Markov chain algorithm (slicesample, Matlab 2016a, The MathWorks, Inc., Narick, MA, USA) [44]. First, the mean spectrum of all measurements and the associate standard deviation for every wavelength (σ) are determined. A non-linear least-square algorithm (lsqcurvefit, Matlab 2016a, The MathWorks, Inc., Narick, MA, USA) is applied to the mean spectrum with the given lower and upper boundaries (see Table 1). This initial value already represents a possible solution for the fit, however, it cannot be assured that it is only a local maximum of the probability density function, and that there is indeed a better fitting solution. Therefore, the Markov chain algorithm uses pseudo-random number sampling weighted by probability and is able to find global minima of the residual function. In contrast to other methods, it can give access to the a posteriori probability of each parameter in the model, which allows an evaluation of the obtained fit results. Within the context of the Bayesian framework, the observed measurement (in this case the mean spectrum) is usually referred to with the abbreviation Mo. The residual function F(x) is then given by:

(4)

where b < 0 and a is a scaling parameter. Finally, this expression for μs is inserted into Eq. (3). Furthermore, this model could be used to extract the periodic component from the signal and perform a Fourier analysis in order to obtain the scatterer size distribution [17]. However, it should be noted that this methods allows analysis with a precision of 6 μm [17]. This is insufficient to resolve the typical size of nuclei that are one of the main scatterers in biological tissues (nuclei in cartilage tissue have a radius of approximately 3 μm [38]). Therefore, the analysis of the scatterer size distribution is forgone in this case. Choice of absorbers for the model. The absorption coefficient μa is replaced by a linear combination of the most prominent absorbers in tissue. The choice of absorbers is based on reference spectra from literature, the recorded raw spectra and the type of tissue in this study. In the range of 500–1000 nm, the characteristic peaks of deoxygenated haemoglobin (556 nm) and oxygenated haemoglobin (542 and 576 nm) as well as the high absorption of melanin are most prominent, while the contribution of water can be neglected (see Supplementary Fig. 3). The contribution of collagen in this spectral range is relatively low and does not have characteristic peaks [39], so that absorption spectroscopy is not suitable to quantify collagen in this case. The contribution of water [40] and fat can be neglected in the visible and near-infrared range. Bilirubin has the most prominent absorption peak at 460 nm [41], indicating that already a low concentration would have high contribution to the overall absorption spectrum. In the case of this study, a strong absorption at 460 nm was not observed. Therefore, it is assumed that bilirubin does not give a major contribution to the overall spectrum. The double-peak of oxygenated haemoglobin on the other hand, is indeed apparent in the recorded spectra of this study (see Fig. 4), which is why haemoglobin is taken into account in its oxygenated state as well as in the deoxygenated state. In order to allow a useful comparison, the spectra have been transferred to a concentration-independent absorption coefficient µa* . Therefore, the reference spectra of the molar extinction coefficient ϵ(λ) are transferred to μa via the following equation [42]:

µa ( ) =

(6)

500

However, the general trend in these melanin spectra changes very little and only the scaling differs. In the model that is applied here, this problem can give rise to a constant offset in the relative concentration of melanin. Differences between bone and cartilage tissue analysed with the same model will not be affected. Therefore, melanin is also included into the model. Finally, the absorption coefficient in this study is modelled as:

b

500

3

F (x ) =

B (x )

Mo

(8)

In the context of this study, B(x) is the applied model from Eq. (3) and x is the vector of the five unknown parameters (a, b, S, cm,Hb, cm,mel). A priori knowledge is applied to the model by defining upper and lower boundaries, as summarized in Table 1. This a priori knowledge represents broad boundaries for the absorption properties. The oxygen saturation is generally limited to the range between zero and one, however, it was already discussed that the lower boundary is in fact > 0. According to the review from Jacques [21], b ranges between −4 and 0, while the boundaries for a are chosen between 1 and 150. These boundaries have been used to find suitable initial values using lsqcurvefit. This a priori knowledge is transferred into a PDF via a five-dimensional heavy side step function ppr(x) in order to use it for the probability-based Markov chain. The a posteriori PDF, p(x|b) is then given by:

(5)

By inserting the reference spectra µa* ( ) as well as values for wm (wm,hemo = 64 ,458 [42], wm,mel = 295 [43]), it is then possible to estimate cm. The presented method was not calibrated to exact molar concentrations of known components. Therefore, the differences in concentration of the chosen components are stated in relative units that allow valid comparison between the two classes of tissue. In general however, it is possible to use the same metrology and data analysis to determine firm values for the molar concentration upon proper calibration. Furthermore, it should be noted that the absorption spectra of melanin differ in the y-scaling throughout different studies depending on the exact type of melanin that was used (e.g. eumelanin or pheomelanin). Other studies use 3rd order equations like the following, in order to approximate the absorption of melanin [16]:

p (x|b) = p (b|x )·ppr (x ) = e[

(1/2)·F t ·F ]· p (x ) pr

(9)

Finally, p(x|b) is traced by the Markov chain algorithm with the initial value determined before, a burn-in of 1,500,000 with 15,000,000 iteration where only every tenth value is stored. The trace of this algorithm for each of the five parameters is plotted as histogram normalized to the area under the curve. This represents the a posteriori probability distribution for each of the parameters in the reflectance model. Probability density spectra. After the derivation of the probabilities of the fitting parameters, these probabilities are used to derive 4

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significance based on the general population and finally, to use the data for an accurate classifications of unknown data on the level of individual patients. For the presented study, only differences in the lateral plane are considered. For a differentiation of different tissue layers along the axial direction, a confocal setup will be required. This might even allow mapping of 3-dimensional tissue boundaries in the future, which would only be limited by the optical penetration depth in tissue of a few 100 μm.

Table 1 The boundary conditions for the parameters in the DRS model. The values are used as boundaries for the non-linear least-square algorithm. In case of the Markov chain, they are transferred into probability density function via a five-dimensional heavy side step function. Parameter

Boundaries for fit [lower; upper]

S cm,Hb cm,mel a b

[0 ; 1] [0 ; 1, 000] [0 ; 1, 000] [1 ; 150] [− 4 ;0]

3.1. Spontaneous Raman spectroscopy Raman spectroscopy was used to analyse the biochemical fingerprints of subchondral bone compared to the ones of the surrounding cartilage. Fig. 2A shows the averaged Raman spectra after post-processing. Relevant peaks with significant differences are summarized in Fig. 2B. Based on the results presented in Fig. 2, it is apparent that a variety of Raman peaks show highly significant differences between bone and cartilage with an effect size larger than the error rate. When assigning these peaks to their known molecular origin, it is apparent that the molecular contrast is mostly based on minerals and proteins. The peaks with the most prominent difference are located at 1069 cm−1 and at 962 cm−1 and can be assigned to the mineral components v3 PO4 3 and 2 1 CO3 [45–47]. Both can be related to hydroxyl apatite [48,47], which is a major component of bone tissue. As expected, the Raman signal at these bands is significantly reduced in cartilage compared to bone. Signals from proteins and protein components, on the other hand, are significantly increased in cartilage. These peaks can be assigned to collagen at 1668 cm−1 [45], phenylalanine at 1003 cm−1 [49,50], phenyl at 1496 cm−1 [51] or Amide III at 1340 cm−1 [37]. A detailed analysis of the ratio of proteins to minerals (see Fig. 3) reveals that the protein-to-mineral ratio is indeed significantly increased in cartilage in every single permutation of these components. Similar findings were already observed using the more complex method of laser induced breakdown spectroscopy (LIBS) in a study on a small number of porcine samples [52]. The results presented in this study confirm the relevance of the protein-to-mineral ratio for the differentiation of bone and cartilage in a study of higher impact, including a larger amount of samples from human patients. Compared to LIBS, the experimental setup of Raman spectroscopy consists of less expensive hardware components and could lead to an easier integration to the clinical routine. Thus, the assignment of the molecular origin of the main differences in the Raman spectra, can provide a deep insight into the relative concentrations of several relevant bio-molecules. Furthermore, it is plausible that these fundamentally different data sets can be exploited to correctly classify bone and cartilage from unknown data sets of single patients, using machine learning.

wavelength-dependent probabilities of the absorption coefficient (see Eq. (7)) and the scattering coefficient (see Eq. (4)). For all possible combinations of parameters with a probability greater than 0, the final probability is calculated by an n-dimensional inner product. For the absorption coefficient, the relative probabilities of the concentrations of the absorbers are inserted into Eq. (7) in order to obtain the probability of the overall absorption spectrum μa(λ). For the scattering coefficient, the parameters a and b are treated respectively to obtain μs(λ). This resulted in a probability function for every single wavelength. However, due to the wavelengths dependence of the scattering and absorption, the probability scaling differs for each wavelength. To compensate for this, all probabilities of each wavelength are projected to the same scale. The final scale is logarithmic and ranges from 10−1 to 103 1/cm. This results in the appearance of Moiré artefacts, which are smoothed by a 19 pixel wide Gaussian filter with a FWHM of 3 pixels. Finally, the sum of the probabilities is normalized to unity for each wavelength. 2.4. Principle component analysis and machine learning Beyond the in-depth analysis of several molecular contrast mechanisms, it is important to show that both techniques are sensitive enough for a correct classification of unknown data from single patients with only few measurement spots. Therefore, a principle component analysis (PCA) is applied to the data together with a machine learning procedure. First, all raw DRS spectra (nbone = 725, ncartilage = 605) as well as all Raman spectra (nbone = 336, ncartilage = 258) are sorted by individual patient (n = 17). Following the Leave-One-Out model for cross-validation, the data of n − 1 patients are used to train a model which is then used for the prediction of the data set from the one remaining human patient. The procedure was repeated for each of the patients. Prior to training, the data are analysed by a PCA with a certain amount of principal components (DRS: 8, Raman: 30). Different classifiers were used (support vector machine – SVM, robust boost – RB, random forest walk – RFW, linear regression – LR) and the most successful classifier is presented. The accuracy of this prediction is evaluated based on the receiver operating characteristic (ROC) curve and its area under curve (AUC). Finally, the overall accuracy of this prediction is stated as the average AUC of all 17 iterations of the Leave-One-Out validation. The AUC-values are rounded to two digits (10−2) and then stated as percentage.

3.2. Diffuse reflectance spectroscopy Beyond a successful application of SRS, this study aimed to evaluate a DRS, as a second label-free, contact-free technology for the presented application of human hip samples. In this process, Bayesian inference should be established for the decomposition of DRS data. The raw data obtained from the DRS measurements are displayed in Fig. 4A as averaged spectra. The mean as well as the standard deviation of each spectral data point were then used as input for the presented Markov model, in order to approximate absorption and scattering coefficients. Finally, the a posteriori probability densities from this model (see Fig. 4B) have been used to reconstruct the probability density spectra of μs and μa (see Fig. 5). The raw data already show that the peaks associated with oxygenated haemoglobin are clearly visible at 541 and 576 nm. In the infra-red region beyond 700 nm, the spectra are fairly similar. However, there is a big difference in the visible region. The averaged spectrum from cartilage shows the highest values with 26-

3. Results and discussion It is the aim of this study to evaluate the application of contact-free, optical technologies to the field of molecular characterization and accurate differentiation of adjacent tissues. Therefore, DRS and Raman spectroscopy were applied to human hip biopsies to map molecular differences between subchondral bone and the surrounding cartilage. A novel decomposition algorithm was applied to DRS data to extract absorption and scattering properties, while Raman spectroscopy was used to obtain a more in-depth analysis of the molecular fingerprint. The readout of each technique was then used to evaluate the statistical 5

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Fig. 2. Results of Raman spectroscopy. The Raman intensity after post-processing (de-noising, baseline subtraction, normalization) is plotted on the y-axis. (A) The averaged Raman intensity is plotted against the Raman shift as the mean spectra of bone and cartilage tissue. The blue curve shows the averaged Raman spectra recorded from subchondral bone (n = 336) and the red curve shows the Raman spectra recorded from cartilage (n = 258). Some of the most prominent peaks are labelled with their respective Raman shift. (B) Statistical analysis of the most prominent peaks. Except the peak at 1255 cm−1, all indicated Raman peaks show a highly significant difference with an effect size larger than the error rate. Error bars show the standard error and the significance levels are: * p < 0.05, ** p < 0.01, *** p < 0.001. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

27% between 450 and 525 nm and remains almost constant between 500 and 700 nm. The recorded reflectance from bone however, is reduced by half in the range of 450–600 nm and steadily increases between 600 and 750 nm. This behaviour reflects the reddish appearance of the bone regions as it was observed in this case (see Fig. 1C). Furthermore, there are fluctuations of higher frequency above 600 nm for both tissue types. These frequencies can be linked to wavelength specific scattering, due to superficial tissue structures [17]. Using Fourier analysis from a normalized signal, it would be possible to analyse this surface roughness [17]. However, this study targets the much broader contributions from absorption and scattering, where higher-frequency fluctuations related to surface roughness only play a minor role. Absorption. Fig. 4B shows the probability distributions of the three absorbers in the model. Two of these parameters provide apparent differences between both tissue types, while the third parameter (the oxygenation level) remains inconclusive. The a posteriori probabilities of the concentration of haemoglobin and melanin are described by Gaussian shaped density functions and both show an increased mean value in bone tissue compared to cartilage. The presented method shows that the relative concentration of haemoglobin in bone is increased by a factor of 20 (31.9 ± 11.1 a.u. compared to 1.67 ± 1.2 a.u.; μ ± σ), while the increase in melanin is around 4-fold (0.4 ± 0.19 a.u. compared to 0.09 ± 0.06 a.u.). The presence of oxygenated haemoglobin was already indicated by its characteristic double peak in the individual spectra (see Fig. 4). Furthermore, an increased amount is suggested by the probability density spectrum of the absorption coefficient (see Fig. 5A and B). Therefore, it is probable that residuals of haemoglobin are the cause of for the reddish stain of the tissue. An increased concentration of melanin, on the other hand, might seem unlikely in this type of tissue, but melanin or melanin-like deposits have already been found in cartilage and bone related to degenerative diseases such as Alkaptonuria [53,54]. Furthermore, there

are types of melanin that appear reddish in color (e.g. pheomelanin) that could play a role in the observed reddish stain of the bone tissue. The histogram of the oxygenation level does not show an ideal probability density function, which indicates that the fit did not converge perfectly for this parameter. This does not allow valid conclusions regarding the oxygenation level of the haemoglobin residuals. Probability density function of the scattering and absorption. Fig. 5A and B shows the probability distribution of the absorption coefficient for cartilage and bone, and Fig. 5C and D shows the probability distribution of the scattering coefficient, respectively. The sum of all values at a given wavelength equals unity. In case of μa from cartilage, the trend is comparatively flat between 2 and 20 cm−1. The absorption coefficient from bone tissue however, is overall increased (10-100 cm−1). Furthermore, it shows a steeper, spectral slope, which corresponds well with the whitish colour in the cartilage tissue (flat absorption spectrum) and the reddish color in the bony tissue (higher absorption at shorter wavelengths). The characteristic double peak of oxygenated haemoglobin is apparent in both cases, however, it is much more prominent for the bone tissue regions, indicating a higher amount of haemoglobin in bone. A possible explanation for the increased concentration of haemoglobin might be the rough surface texture of bone, which provides more contact area and cavities for the binding of blood residuals during surgery. The results in Fig. 5C and D display the probability density spectra of the wavelength-depending scattering coefficient μs. The spectral trend of the scattering coefficient shows an overall stronger effect of scattering and a lower spectral slope in bone compared to cartilage. It has already been shown that a lower spectral slope of μs indicates a larger size of the main scattering structures [18,17], while the overall increase in scattering indicates the presence of a larger number of scattering structures. These structural differences could be related to ligaments of collagen-II in cartilage [55], which are mostly absent in 6

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Fig. 3. Boxplots of the ratios of proteins to minerals based on the Raman data. The ratio was calculated as A/(A + B) with A being the Raman intensity associated to proteins and B the one of minerals. All possible combinations of two minerals peaks (at 962 and at 1069 cm−1) and four protein peaks (1003, 1340, 1496 and 1668 cm−1) are displayed. Every single permutation shows a significantly increased amount of proteins in cartilage tissue compared to bone. The significance levels are: * p < 0.05, ** p < 0.01, *** p < 0.001.

the mineral lattice in bone. Although, it can be observed that the data from cartilage tissue are affected stronger by noise, it is shown that the differences between cartilage and bone are larger than the uncertainty, thus indicating that DRS still allows a substantial differentiation between scattering and absorption in the general population of the presented data. The novel method of Bayesian-based decomposition in DRS allows a more forthright conclusion based on the probability density spectra for absorption and scattering coefficient. These spectra display the overall uncertainty, including statistical variance of the data as well as possible error propagations of the model, instead of a simple regression function with a given quality parameter (e.g. R2 value).

3.3. Automated classification of unknown data from individual patients It was shown that both technologies of DRS and Raman spectroscopy in combination with the presented post-processing and data analysis are able to track significant differences in the relative concentrations of several bio-molecules based on the general population of the data from all patients. Additionally, a machine learning classification procedure is applied to prove that both techniques can be used to automatically differentiate bone and cartilage on the level of individual patients. This procedure aims to provide evidence that an automated classification between cartilage and bone is possible based on the presented DRS and Raman measurements. The success rate of the machine 7

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Fig. 4. The raw data from DRS measurements and the obtained probability densities from the Markov chain decomposition algorithm. (A) The raw data are shown as averaged spectra recorded from bone and cartilage tissue. The mean as well as the standard deviation of each spectral data point were then used as input for the presented Markov model, in order to approximate absorption and scattering coefficients. Bone is displayed in red (n = 725) and cartilage in blue (n = 605). The double peak from oxygenated haemoglobin at 541 and 576 nm is clearly visible in the raw data. (B) A posteriori probability densities of the parameters used to model the absorption coefficient (S, cm,Hb and cm,mel) obtained by the Markov chain model. The a posteriori probability p(x|b) is plotted on the y-axis against the values of the fit parameter on the x-axis. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 5. Probability density spectra of the absorption coefficient and the scattering coefficient were reconstructed from the a posteriori probability densities obtained from the Markov chain. Colour bar represents the probability value. (A) Probability spectrum of the absorption coefficient μa from healthy reference tissue. (B) Probability spectrum of the absorption coefficientμa from bone tissue. (C) Probability spectrum of the scattering coefficient μs from cartilage. (D) Probability spectrum of the scattering coefficient μs from bone. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

learning procedure is judged by the scatter plot of the first two principal components after PCA, the ROC curve and its AUC. From all evaluated classifiers, SVM showed the best results. In the case of DRS, only 2 out of 17 iterations showed an accuracy below 95% but 8 showed perfect performance (100% AUC). The averaged accuracy was 98%. The PCA showed that more than 90% of the variability in the training set can already be explained by the first principal component.

Therefore, the rather low number of principal components of 8 was already sufficient to achieve the very successful classification. Furthermore, it is revealed that the lowest accuracy was due to a small subset of statistical outliers (see arrow in Fig. 6A). Based on these results, it is apparent that DRS is suitable for an automated differentiation between cartilage and bone, without post-processing of the raw data such as noise reduction, smoothing or other. 8

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Fig. 6. Automated differentiation between bone and cartilage based on the unknown data of individual patients using machine learning. Leave-One-Out was used for cross-validation, the average accuracy for DRS was 98% and the one for SRS was 89%. For each modality, the best and the worse result during cross-validation are shown as principle component analysis (PCA) and the receiver operating characteristic curve. The PCA is represented by 2D scatter plots with the first two components and the explained variances are stated in %. (A) and (C) Best cases with 100% accuracy. The PCA plot shows clear separability of the data clusters. (B) and (D) worst iteration during cross-validation. The lowest accuracy in the DRS data set was 84% accuracy and can be explained by a subset of outliers (arrow). The lowest accuracy for SRS was 43% and is probably due to an unbalanced data set.

on fundamentally different contrast mechanisms, so that only the combined analysis from Raman spectroscopy and DRS can reveal differences in the concentrations of specific molecules and in the absorption and scattering properties.

In the case of Raman spectroscopy, 5 out of the 17 validation steps achieved 100% accuracy and 3 showed a performance below 75%. One single outlier dropped down to an accuracy of 44%, which is probably due to the rather unbalanced data set for this patient of 6 vs 24 measurement spots. Unbalanced data sets are known to hamper reliable machine learning [56], however, this issue could be targeted by proximal SVM in combination with Newton refinement modification [57]. The average AUC over all validation steps reached 89% (if the worst and the best case are removed, the average AUC is 91%). In this case, the PCA already indicates that more components are required to explain the variability in the Raman data set sufficiently. In contrast to DRS, the first principal component accounts for only about 40–45%. Nevertheless, the results from the SVM classification are sufficient to automatically differentiate cartilage tissue from subchondral bone at an accuracy of around 90%. Compared to Raman spectroscopy, DRS shows superior classification result, indicating that the more global differences in white light reflection offers a sharper contrast as the rather specific differences in the relatively low concentrations of certain molecules. Nevertheless, the results from both methods provide evidence that the molecular differences between bone and cartilage are not only significant in the general population, but are also sufficiently sensitive for a correct classification of only few measurement spots from unknown patients. These results show the sensitivity and specificity of the molecular differences obtained from Raman spectroscopy and DRS, using a rather simplistic experimental setup. The acquisition time of Raman spectroscopy was in the range of several seconds, while DRS recordings can be recorded within milliseconds. The computational processing time of tissue classification is practically negligible if a previously trained SVM model is applied, allowing fast and accurate tissue differentiation, which could allow implementation of an automated classification software, applicable in situ during surgery. The combination of both methods to the one experimental routine is beneficial, since it allows cross-validation of both methods. It is worth mentioning that the patient in Fig. 6A that scored only 44% in Raman spectroscopy, scored 99.3% in the DRS measurement. Thus, by comparing the predictions from both methods (e.g. by averaging or weighted majority voting), outliers that score a low accuracy on one method could be compensated by a sufficient accuracy in the other methods. Furthermore, both methods are based

4. Conclusion Spontaneous Raman spectroscopy and diffuse reflectance spectroscopy (DRS) show the potential for optical technologies in the field of molecular characterisation and differentiation of tissues. It was shown that both techniques allow automated classification between bone and surrounding cartilage, with high accuracy of 89% (SRS) and even 98% (DRS). It should be emphasized that these results were obtained with minimal impact to the sample (non-destructive, label-free, contact-free, in the case of DRS: without potentially harmful laser radiation) and at a high acquisition rate. Beyond a fully automated classification, it was possible to evaluate the molecular contrast mechanisms that is the foundation of this successful differentiation. It could be shown that the distinction between bone and cartilage in the Raman signal is mostly based on proteins, amino acids and mineral components. The new decomposition algorithm for DRS data is superior to state-of-the-art techniques, since it is based on probability density spectra that allow a more forthright evaluation of the data. In this case, it enabled plausible conclusions regarding relative concentration of haemoglobin and melanin as well as structural differences that are probably linked to tendons of collagen-II in cartilage in contrast to the mineral lattice of bone of human FAI-patients. In the area of degenerative diseases such as FAI, follow-up studies could determine the degree of pre-arthritic changes in cartilage which is of high clinical relevance. In order to gain deeper understanding of possible molecular pathways that could lead to the development of FAI symptoms, other label-free, optical technologies such as multiphoton microscopy (MPM), near-IR spectroscopy or laser-induced breakdown spectroscopy (LIBS) could complement the analysis in the future. In the long term, this could lead to minimally invasive, molecular mapping of degenerative cartilage, subchondral bone and healthy cartilage, facilitating benefits for diagnosis and treatment of FAI-patients. On a more general level, it was possible to demonstrate the high potential of contact-free, all-optical analysis of biological samples. 9

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Inexpensive and simplistic hardware could allow pragmatic use in clinical routine and might even serve as in situ feedback system during surgery. Furthermore, potent data analysis tools such as Bayesian-based decomposition could then be applied downstream in order to extract valuable diagnostic parameters from the data.

[11] N. Stone, C. Kendall, J. Smith, P. Crow, H. Barr, Raman spectroscopy for identification of epithelial cancers, Farad. Discuss. 126 (2004) 141–157. [12] A.S. Haka, K.E. Shafer-Peltier, M. Fitzmaurice, J. Crowe, R.R. Dasari, M.S. Feld, Identifying microcalcifications in benign and malignant breast lesions by probing differences in their chemical composition using Raman spectroscopy, Cancer Res. 62 (18) (2002) 5375–5380. [13] J.W. Ager, R. Kiran Nalla, K.L. Breeden, R.O. Ritchie, Deep-ultraviolet Raman spectroscopy study of the effect of aging on human cortical bone, J. Biomed. Opt. 10 (3) (2005) 034012–0340128. [14] F. Stelzle, A. Zam, W. Adler, K. Tangermann-Gerk, A. Douplik, E. Nkenke, M. Schmidt, Optical nerve detection by diffuse reflectance spectroscopy for feedback controlled oral and maxillofacial laser surgery, J. Transl. Med. 9 (1) (2011) 20. [15] R. Gunaratne, I. Monteath, R. Sheh, C.N. Ironside, M. Kapfer, B. Robertson, R. Khan, D. Fick, Human joint tissue identification by employing diffuse reflectance and auto-fluorescence spectroscopy, in combination with machine learning, 2017 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC), IEEE, 2017, p. 1. [16] M. Ewerlöf, M. Larsson, E. Göran Salerud, Spatial and temporal skin blood volume and saturation estimation using a multispectral snapshot imaging camera, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XV, vol. 10068, International Society for Optics and Photonics, 2017, p. 1006814. [17] L.T. Perelman, V. Backman, M. Wallace, G. Zonios, R. Manoharan, A. Nusrat, S.J. Shields, M. Seiler, C. Lima, T. Hamano, I. Itzkan, J. Van Dam, J.M. Crawford, M. Feld, Observation of periodic fine structure in reflectance from biological tissue: a new technique for measuring nuclear size distribution, Phys. Rev. Lett. 80 (3) (1998) 627. [18] G. Zonios, J. Bykowski, N. Kollias, Skin melanin, hemoglobin, and light scattering properties can be quantitatively assessed in vivo using diffuse reflectance spectroscopy, J. Investig. Dermatol. 117 (6) (2001) 1452–1457. [19] A. Johansson, T. Sundqvist, J.H. Kuiper, P.A. Öberg, A spectroscopic approach to imaging and quantification of cartilage lesions in human knee joints, Phys. Med. Biol. (2011). [20] M.B. Wallace, L.T. Perelman, V. Backman, J.M. Crawford, M. Fitzmaurice, M. Seiler, K. Badizadegan, S.J. Shields, I. Itzkan, R.R. Dasari, et al., Endoscopic detection of dysplasia in patients with Barrett's esophagus using light-scattering spectroscopy, Gastroenterology 119 (3) (2000) 677–682. [21] S.L. Jacques, Optical properties of biological tissues: a review, Phys. Med. Biol. 58 (11) (2013) R37. [22] R. Pan, K.J. Daun, T. Dreier, C. Schulz, Uncertainty quantification and design-ofexperiment in absorption-based aqueous film parameter measurements using Bayesian inference, Appl. Opt. 56 (11) (2017) E1–E7. [23] S.J. Grauer, P.J. Hadwin, K.J. Daun, Bayesian approach to the design of chemical species tomography experiments, Appl. Opt. 55 (21) (2016) 5772–5782. [24] F.J.T. Huber, S. Will, K.J. Daun, Sizing aerosolized fractal nanoparticle aggregates through Bayesian analysis of wide-angle light scattering (WALS) data, J. Quant. Spectr. Radiat. Transfer 184 (2016) 27–39. [25] D.G. Altman, S.N. Goodman, Transfer of technology from statistical journals to the biomedical literature: past trends and future predictions, JAMA 272 (2) (1994) 129–132. [26] M.J. Philippon, M.L. Schenker, Arthroscopy for the treatment of femoroacetabular impingement in the athlete, Clin. Sports Med. 25 (2) (2006) 299–308. [27] M.J. Philippon, M.L. Schenker, Athletic hip injuries and capsular laxity, Oper. Techn. Orthop. 15 (3) (2005) 261–266. [28] A.B. Nielsen, J. Yde, Epidemiology and traumatology of injuries in soccer, Am. J. Sports Med. 17 (6) (1989) 803–807. [29] R. Ganz, M. Leunig, K. Leunig-Ganz, W.H. Harris, The etiology of osteoarthritis of the hip, Clin. Orthop. Relat. Res. 466 (2) (2008) 264–272. [30] R. Ganz, J. Parvizi, M. Beck, M. Leunig, H. Nötzli, K.A. Siebenrock, Femoroacetabular impingement: a cause for osteoarthritis of the hip, Clin. Orthop. Relat. Res. 417 (2003) 112–120. [31] M. Beck, M. Kalhor, M. Leunig, R. Ganz, Hip morphology influences the pattern of damage to the acetabular cartilage, Bone Joint J. 87 (7) (2005) 1012–1018. [32] T.M. Ecker, M. Tannast, M. Puls, K.A. Siebenrock, S.B. Murphy, Pathomorphologic alterations predict presence or absence of hip osteoarthrosis, Clin. Orthop. Relat. Res. 465 (2007) 46–52. [33] M. Tannast, D. Goricki, M. Beck, S.B. Murphy, K.-A. Siebenrock, Hip damage occurs at the zone of femoroacetabular impingement, Clin. Orthop. Relat. Res. 466 (2) (2008) 273–280. [34] S. Wagner, W. Hofstetter, M. Chiquet, P. Mainil-Varlet, E. Stauffer, R. Ganz, K.A. Siebenrock, Early osteoarthritic changes of human femoral head cartilage subsequent to femoro-acetabular impingement, Osteoarthr. Cartil. 11 (7) (2003) 508–518. [35] F. von Wegner, M. Both, R. Fink, O. Friedrich, Fast xyt imaging of elementary calcium release events in muscle with multifocal multiphoton microscopy and wavelet denoising and detection, IEEE Trans. Med. Imaging 26 (2007) 925–934. [36] P.M. Ramos, I. Ruisánchez, Noise and background removal in Raman spectra of ancient pigments using wavelet transform, J. Raman Spectrosc. 36 (9) (2005) 848–856. [37] Y. Takahashi, T. Shishido, K. Yamamoto, Y. Sawaji, J. Nishida, G. Pezzotti, Do formalin fixation and freeze-thaw affect near-infrared Raman spectroscopy of cartilaginous tissue? a preliminary ex vivo analysis of native human articular cartilage, J. Raman Spectrosc. 46 (11) (2015) 1166–1172. [38] M.D. Buschmann, E.B. Hunziker, Y.-J. Kim, A.J. Grodzinsky, Altered aggrecan synthesis correlates with cell and nucleus structure in statically compressed cartilage, J. Cell Sci. 109 (2) (1996) 499–508. [39] S.K. Venkata Sekar, I. Bargigia, A. Dalla Mora, P. Taroni, A. Ruggeri, A. Tosi,

Conflict of interest The authors declare no competing interests. None of the funding sources had any involvement in study design, in the collection, analysis and interpretation of data, in the writing of the report, or in the decision to submit the article for publication. Authors’ contribution MH, FD, LB and MS: Conceptualization; LB: sample preparation; LK: Data acquisition; LK and MH: Data analysis, Visualization and Methodology; MH, LB, FD, SS, OF and MS: Funding acquisition; FK, LB, SS, OF and MS: Project administration and Resources; FK, SS, OF, MH and MS: Supervision; LK: Writing – original draft; MH, FK, FD, SS, LB, OF and MS: Writing – review & editing. Acknowledgement The study was funded by a grant of the Deutsche Arthrose-Hilfe e.V. The authors gratefully acknowledge the funding of the Erlangen Graduate School in Advanced Optical Technologies (SAOT) by the Deutsche Forschungsgemeinschaft (German Research Foundation – DFG) within the framework of the Initiative for Excellence. Furthermore, the collaborative research centre TRR-241 of the DFG (subproject C01) as well as of the DFG-project 337270237 (“Dreidimensionale Abbildung trüber Medien mittels hyperspektraler Bildgebung”) supported parts of this work. The authors wish to acknowledge the support of the non-profit German Arthritis Society (Deutsche Arthrose-Hilfe e.V.) and its president Helmut H. Huberti (to MD by grant P319). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.snb.2019.127121. References [1] J. Iavindrasana, G. Cohen, A. Depeursinge, H. Müller, R. Meyer, A. Geissbuhler, Clinical data mining: a review, Yearb. Med. Inform. 18 (1) (2009) 121–133. [2] B. Triana, J. Cha, A. Shademan, A. Krieger, J.U. Kang, P.C.W. Kim, Multispectral tissue analysis and classification towards enabling automated robotic surgery, Advanced Biomedical and Clinical Diagnostic Systems XII, vol. 8935, International Society for Optics and Photonics, 2014, p. 893527. [3] S.J. Rehse, H. Salimnia, A.W. Miziolek, Laser-induced breakdown spectroscopy (libs): an overview of recent progress and future potential for biomedical applications, J. Med. Eng. Technol. 36 (2) (2012) 77–89. [4] W.R. Zipfel, R.M. Williams, W.W. Webb, Nonlinear magic: multiphoton microscopy in the biosciences, Nat. Biotechnol. 21 (11) (2003) 1369. [5] L.-P. Choo-Smith, H.G.M. Edwards, H.P. Endtz, J.M. Kros, F. Heule, H. Barr, J.S. Robinson Jr., H.A. Bruining, G.J. Puppels, Medical applications of Raman spectroscopy: from proof of principle to clinical implementation, Biopolymers: Orig. Res. Biomol. 67 (1) (2002) 1–9. [6] F. Stelzle, K. Tangermann-Gerk, W. Adler, A. Zam, M. Schmidt, A. Douplik, E. Nkenke, Diffuse reflectance spectroscopy for optical soft tissue differentiation as remote feedback control for tissue-specific laser surgery, Lasers Surg. Med. 42 (4) (2010) 319–325. [7] C. Venkata Raman, K. Srinivasa Krishnan, A new type of secondary radiation, Nature 121 (3048) (1928) 501. [8] A. Jiang, J. Wei, C. Li, J. Peng, S. Peng, J. Tan, Asymmetric least squares for multiple spectra baseline correction, Anal. Chim. Acta 683 (1) (2010) 63–68. [9] E.V. Efremov, F. Ariese, C. Gooijer, Achievements in resonance Raman spectroscopy: review of a technique with a distinct analytical chemistry potential, Anal. Chim. Acta 606 (2) (2008) 119–134. [10] P. Matousek, N. Stone, Emerging concepts in deep Raman spectroscopy of biological tissue, Analyst 134 (6) (2009) 1058–1066.

10

Sensors & Actuators: B. Chemical 301 (2019) 127121

L. Kreiß, et al.

[40]

[41] [42] [43] [44] [45] [46] [47] [48] [49]

A. Pifferi, A. Farina, Diffuse optical characterization of collagen absorption from 500 to 1700 nm, J. Biomed. Opt. 22 (1) (2017) 015006. Natural Phenomena Simulation Group, The Molar Extinction Coefficient of Bilirubin, David R. Cheriton School of Computer Science, University of Waterloo, Ontario, Canada, 2018 [Online]. Available: http://www.npsg.uwaterloo.ca/data/ skin.php. S. Prahl, S. Jacques, Bilirubin, Oregon Medical Laser Center, Portland, 2018 [Online]. Available: https://omlc.org/spectra/PhotochemCAD/html/119.html. S. Prahl, Optical Absorption of Hemoglobin, Oregon Medical Laser Center, Portland, 2018 [Online]. Available: https://omlc.org/spectra/hemoglobin/index.html. T. Sarna, H.A. Swartz, The Physical Properties of Melanins, The Pigmentary System: Physiology and Pathophysiology, 2nd ed., (1998), pp. 311–341. R.M. Neal, Slice sampling, Ann. Stat. (2003) 705–741. Y. Wang, P. Spencer, M.P. Walker, Chemical profile of adhesive/caries-affected dentin interfaces using Raman microspectroscopy, J. Biomed. Mater. Res. Part A 81 (2) (2007). R. Osorio, S. Sauro, T.F. Watson, M. Toledano, Polyaspartic acid enhances dentine remineralization bonded with a zinc-doped Portland-based resin cement, Int. Endodont. J. 49 (9) (2016) 874–883. M.A. Walters, Y.C. Leung, N.C. Blumenthal, K.A. Konsker, R.Z. LeGeros, A Raman and infrared spectroscopic investigation of biological hydroxyapatite, J. Inorg. Biochem. 39 (3) (1990) 193–200. W.-T. Cheng, M.-T. Liu, H.-N. Liu, S.-Y. Lin, Micro-Raman spectroscopy used to identify and grade human skin pilomatrixoma, Microsc. Res. Techn. 68 (2) (2005) 75–79. J. De Gelder, K. De Gussem, P. Vandenabeele, L. Moens, Reference database of

[50]

[51] [52]

[53]

[54]

[55] [56] [57]

11

Raman spectra of biological molecules, J. Raman Spectrosc. 38 (9) (2007) 1133–1147. R. Jain, D. Calderon, P.R. Kierski, M.J. Schurr, C.J. Czuprynski, C.J. Murphy, J.F. McAnulty, N.L. Abbott, Raman spectroscopy enables noninvasive biochemical characterization and identification of the stage of healing of a wound, Anal. Chem. 86 (8) (2014) 3764–3772. G. Varsányi, Assignments for Vibrational Spectra of Seven Hundred Benzene Derivatives, vol. 1, Halsted Press, 1974. F. Mehari, M. Rohde, C. Knipfer, R. Kanawade, F. Klämpfl, W. Adler, F. Stelzle, M. Schmidt, Laser induced breakdown spectroscopy for bone and cartilage differentiation – ex vivo study as a prospect for a laser surgery feedback mechanism, Biomed. Opt. Express 5 (11) (2014) 4013–4023. L. Millucci, A. Spreafico, L. Tinti, D. Braconi, L. Ghezzi, E. Paccagnini, G. Bernardini, L. Amato, M. Laschi, E. Selvi, et al., Alkaptonuria is a novel human secondary amyloidogenic disease, Biochim. Biophys. Acta (BBA) – Mol. Basis Dis. 1822 (11) (2012) 1682–1691. A.M. Taylor, B. Wlodarski, P.J. Wilson, J. Jarvis, L. Ranganath, A. Boyde, J.A. Gallagher, Deposition of ochronotic pigment in articular cartilage in alkaptonuria is initiated near the tidemark and progresses to the articular surface, Bone 47 (2010) S79. C. Stecco, W.I. Hammer, Functional Atlas of the Human Fascial System, Churchill Livingstone Elsevier Edinburgh, 2015. N.V. Chawla, N. Japkowicz, A. Kotcz, Special issue on learning from imbalanced data sets, ACM SIGKDD Explor. Newslett. 6 (1) (2004) 1–6. G.M. Fung, O.L. Mangasarian, Multicategory proximal support vector machine classifiers, Mach. Learn. 59 (1–2) (2005) 77–97.