Narrowband diffuse reflectance spectroscopy in the 900-1000 nm wavelength region to quantify water and lipid content of turbid media.

We report a narrow wavelength band diffuse reflectance spectroscopy (nb-DRS) method to determine water and fat ratios of scattering media in the 900-1000 nm range. This method was shown to be linearly correlated with absolute water and fat concentrations as tested on a set of turbid emulsion phantoms with a range of water and lipid compositions. Robustness to scattering assumptions was demonstrated and compared against measured scattering by a frequency-domain photon migration system. nb-DRS was also tested on ex-vivo porcine samples and compared against direct tissue water extraction by analytical chemistry methods. We speculate nb-DRS has potential applications in portable devices such as clinical and digital health wearables.


Introduction
Quantification of tissue water and fat is valuable in a wide variety of fields for understanding structure and physiological response. In cancer research, water and fat have been shown to be crucial biomarkers for monitoring tumors [1][2][3], allowing groups to differentiate tissue types [4,5] and locate resection margins [6,7]. Tissue composition has also been the subject of interest in areas such as sports medicine to track fitness and weight loss [8][9][10][11], emergency medicine to regulate patient hydration [12][13][14], and cosmetic dermatological treatments [15][16][17].
However, despite the established importance of tissue water and fat, providing routine and frequent access to body composition devices in the clinic and in homes remains a challenge. Conventional body composition devices including magnetic resonance imaging [18], computed tomography [19], and dual energy X-ray absorptiometry [20] have practical considerations such as weight, size, and cost. One of the simplest methods of estimating body fat is through the use of skinfold calipers [21], but reproducibility and accuracy of this approach has been debated [22][23][24]. Lastly, body impedance devices have been emergent, but are challenging to use when measurements of water and fat at specific tissue sites are desired [25][26][27]. An accurate, non-invasive method with a low-barrier to entry is needed in order to expand adoption of tissue compositional analysis as a more common clinical and personal health procedure.
Tissue spectroscopy research in the near-infrared (NIR) have proven especially relevant for detecting water and fat in biological scattering media as a non-invasive optical modality. Whereas visible and infrared interrogating light is typically limited to dermal layers by intense water absorption [28,29], NIR wavelengths (600-1000 nm) fall into a spectral window wherein deeper light penetration into subsurface tissues, such as adipose and muscle, is possible [30][31][32]. NIR devices which determine tissue water and fat include diffuse optical spectroscopy (DOS) typically based in the frequency-domain [33,34] or time-domain [35][36][37]. However, these instruments utilize complex components such as a high-frequency modulation generator or picosecond sources/detectors making it challenging to enter the consumer market. A continuous-wave method taking advantage of multiple water and fat absorption spectral features within the 900-1600 nm wavelength window has been previously reported [38]. However, tissue spectroscopy methods beyond 1000 nm necessitates specialized detectors such as an Indium Gallium Arsenide spectrometer which typically use subzero thermo electric cooling. Furthermore, acute absorption due to water beyond 1000 nm limits the optical interrogation depth.
In this report, we propose a narrowband diffuse reflectance spectroscopy (nb-DRS) method with simple hardware requirements in the 900-1000 nm wavelength region to estimate tissue water and fat. Emulsion phantoms with varying water and lipid proportions were employed to confirm our method. In addition, advantages of our approach, such as robustness to various scattering assumptions is demonstrated. Lastly, ex-vivo porcine samples were subjected to direct water extraction by analytical chemical methods and compared to our nb-DRS results. We see potential adoption of nb-DRS for small form-factor devices such as continuous clinical monitors and personal health wearables.

Optical instrumentation
nb-DRS measurements were performed using a 3 mm diameter source optical fiber bundle comprised of 50 µm optical fibers and a 1 mm diameter solid-core collection fiber (R Specialty Optical Fibers LLC, Williamsburg, VA). A tungsten-halogen lamp (HL-2000-FHSA, Ocean Optics Inc., Largo, FL) provided steady-state broadband light while a spectrometer (HS2048XL-U2, Avantes, Apeldoorn, Netherlands) detected the diffuse reflectance. The optical fibers were fixed at a 19.5 mm source-detector separation (ρ) by a 3D-printed polylactic acid (PLA) probe. Reflectance calibration was achieved by use of a Spectralon standard (SRS-02-020, Labsphere Inc., North Sutton, NH). To prevent contamination of optical fibers, a transparent plastic barrier (approximately 0.025 mm thick) was placed between the probe and samples, as shown in Fig. 1.   Fig. 1. Illustration of the measurement setup for the emulsion phantoms and porcine samples. A source fiber connected to a tungsten-halogen lamp was fixed at a distance from the spectrometer detection fiber. The two optical fibers were mounted using a black 3D-printed probe. A transparent plastic barrier was placed between the probe and measured surfaces.
The transparent plastic barrier was also used during calibration procedures. Frequency domain photon migration (FDPM) [39] data was captured on select samples. FDPM data was not necessary for our nb-DRS method but was captured in order to illustrate robustness of our method whether using quantified scattering (as measured by FDPM) or assumed scattering. FDPM has been extensively documented in previous reports [33,39,[40][41][42]. FDPM laser diodes wavelengths were centered at 659 nm, 690 nm, 791 nm, and 829 nm and guided using an optical fiber bundle combining multiple 400 µm optical fibers to a common output (R Specialty Optical Fibers LLC, Williamsburg, VA). Laser modulation was provided by a network analyzer (TR1300/1, Copper Mountain Technologies, Indianapolis, Indiana). Detection of modulated light was collected using an avalanche photodiode (S12023-10 with C5658, Hamamatsu Photonics K. K., Hamamatsu City, Japan). A consumer laptop was used to operate the devices. Data analysis and visualization was performed by custom MATLAB 2016a code and Microsoft Excel.

Data processing of nb-DRS
Water and fat content of measured samples was estimated by our nb-DRS methods. Only chromophore extinction coefficients of water (H2O) and lipid (FAT) in the 900-1000 nm wavelength region were considered. In this wavelength range, the H2O and FAT spectral peaks are included, as shown in Fig. 2. Theoretical reflectance (R) from a turbid sample can be expressed using a model of diffusion related to µ a and µ' s with extrapolated boundary conditions [33,43,44]: where c 1 and c 2 are constants related to the air-sample interface [43], fluence is φ and flux is Φ: with: 1−R eff , and R eff as a constant based on refractive indices of the air and sample. Application of this model assumes µ s ' is much greater than µ a . Although the calibrated measured reflectance (R m ) is typically expressed as unitless intensity, a µ a spectrum can be calculated by assumption of µ s ' and application of numerical solvers, such as the MATLAB function fzero: where R is the theoretical reflectance from implementing Eq. (1). For each wavelength (λ), µ a is selected such that the difference between R m and R is minimized resulting in a µ a spectrum. While this µ a spectrum cannot be readily expressed as true absolute units due to the scattering assumption, the shape of µ a can still provide information to estimate the relative chromophore concentration in the sample. Using the H2O and FAT extinction coefficients in the 900-1000 nm region, solving for chromophore concentration by fitting to the spectral features can be achieved by: Equation (3) describes the predicted absorption spectrum (µ a,fit ) as a result of the linear combination of the H2O concentration (C H2O ) and extinction coefficient (ε H2O ) product, the FAT concentration (C FAT ) and extinction coefficient (ε FAT ) product, and a scaling factor (SF). Values for C H2O , C FAT , and SF can be solved by least-square minimization comparing µ a,fit predicted using Eq. (3) and µ a as calculated using Eq. (2). The SF is an offset constant with no wavelength dependance utilized during the minimization process [38]. By use of the SF, chromophore concentrations are solved by fitting to the shape of µ a rather than the absolute values. In the final step, the ratio of H2O fraction (R H2O ) given by R H2O = C H2O C H2O +C FAT and ratio of FAT fraction (R FAT ) given by R FAT = C FAT C H2O +C FAT are calculated. Shown in the Results section of this report, our approach is relatively agnostic to the choice of µ' s assumption. As R H2O and R FAT calculations are ratios based on a common µ a spectrum, only a single-distance nb-DRS measurement at ρ=19.5 mm is required.

Emulsion phantom fabrication
To test our method, a liquid emulsion phantom was selected over traditional, solid phantoms [45][46][47]. Emulsion phantoms allow us to more precisely control the ratio of H2O and FAT [34,35]. A set of 650 mL emulsion phantoms with different lipid (soybean oil) and distilled water proportions were created using soy lecithin as the emulsifier (Wako Pure Chemical Industries, Ltd, Osaka, Japan). Phantoms were created as water-in-oil emulsions. However, as previously described by Ohmae et al., increasing the water to lipid ratio of emulsion phantoms can result in greatly increased scattering characteristics [35]. Thus, to maintain all phantoms as water-in-oil, an R H2O of 0.6 was maximally achieved. Emulsion volume proportions as well as the expected R FAT and R H2O ratios are outlined in Table 1.
Soybean oil and distilled water volumes were measured separately. For each phantom, soybean lecithin was added as 2% by weight of the oil volume. Due to the highly viscous consistency of soy lecithin, the oil was first heated to 60°C and then used to help transfer any remaining lecithin that may adhere to the sides of the weighing beaker. The oil-lecithin mixture was sonicated (Bransonic CPX3800H-E, Emerson Electric, Danbury, CT) for 1 hour at 60°C. For homogenization, Merritt et al. utilized a vacuum chamber after blending to remove air bubbles [34]. For our study, a blender with a built-in vacuum function was utilized (i8800, Jiaxiang Electric Co., LTD, Guangdong, China).

Ex-vivo porcine model
Two porcine slabs (approximately 12 × 10 × 5 cm each) from the abdominal area were sourced from a local slaughterhouse. During transportation to the experimental site, the samples remained at low temperature, but not frozen. Before measurements, the slabs were allowed to rest at room temperature. The nb-DRS probe was mounted using an optical post and placed in contact with the samples during the measurement. Two different measurement sites were selected on each porcine sample. Each measurement site was at least 3 cm from the edges of the sample in order to minimize light leakage during optical measurements. Additionally, the two measurement locations were separated by 3 cm. After nb-DRS data was captured on both locations, 3 cm sized cubes were excised centered at each optical measurement point. The excised tissues were immediately processed for moisture (MOI) analysis.

Tissue moisture extraction
The MOI fraction of ex-vivo porcine tissue was determined using quantitative chemistry based on methods from the Association of Official Analytical Chemists (950.46 AOAC). Briefly, each 3 × 3×3 cm section was weighed using a high precision balance (AS 220.R2, Radwag, Radom, Poland). The samples were then blended separately until homogenized. Duplicates were extracted from each sample and weighed in aluminum dishes. The samples were then placed into a mechanical oven (Heratherm OMS60, Thermo Scientific, Waltham, MA) at 125°C for 4 hours. After the heating period, the samples were placed into a desiccator (Nalgene 5312-0230, Thermo Scientific, Waltham, MA) to cool down. The final weight for each sample was then recorded using the high precision balance and MOI was calculated by: where ω (s) i is the initial weight of sample before heating and ω (s) f is the final sample weight after heating.

Emulsion phantoms
The absorption spectra and chromophore fits solving for C H2O and C FAT in the 900 to 1000 nm wavelengths for each emulsion phantom is shown in Fig. 3. µ' s was assumed to be 1.0 mm −1 across wavelengths. The H2O spectral peak at 976 nm can be seen rising relative to the FAT peak at 930 nm as the phantom series progressed. The absorption contribution of each fitting component to the total absorption is also shown. H2O and FAT chromophore concentrations were solved by fitting to the shape of µ a while the SF provided absolute scaling. Recovered R H2O and R FAT values compared to the expected values are depicted in Fig. 4. In adipose-rich biological samples, R H2O and R FAT approaches C H2O and C FAT , respectively. Highly linear trends were observed for both R H2O and R FAT compared to expected values with a coefficient of determination of R 2 =0.99. R H2O and R FAT errors for each parameter ranged from 1.1% to 8.4% with an average error of 3.7 ± 3.0% across the five emulsion phantoms.

Effects of using various scattering assumptions
Using Eq. (2), calibrated reflectance was converted to absorption. Shown in Fig. 5., three different µ' s values were used for phantom P2: a) measured µ' s using the FDPM system b) assumption of µ' s =1.0 mm −1 without wavelength dependance and c) assumption of µ' s =2.0 mm −1 without wavelength dependance. Here, the values of the µ a spectrum as well as µ a (930 nm) relative to µ a (976 nm), which are the FAT and H2O absorption peaks, respectively, are affected by the choice of µ' s .
Calculated R H2O and R FAT for each of the three scenarios with different µ' s assumptions are shown in Fig. 6. for P2. Despite different µ' s assumptions resulting in various µ a spectra as previously shown in Fig. 5., the ratios of FAT and H2O remained consistent within 1-2%. Results were similar as well for the H2O-dominant emulsion phantom (P5).   Fig. 6. The R H2O and R FAT recovered from datasets: 1) using reduced scattering as measured by FDPM 2) µ' s assumed as 1.0 mm −1 and 3) µ' s assumed as 2.0 mm −1

Composition of porcine samples
Four porcine abdomen tissue samples were dehydrated allowing for the calculations of MOI. Two samples were excised from one abdominal block ("Sample A") observed to have a thick adipose layer (∼2 cm). An additional two samples were taken from the other abdominal slab ("Sample B") which was observed to be significantly leaner with a thin adipose layer (∼0.3 mm). R H2O estimated using nb-DRS and MOI derived from analytical chemistry methods are shown in Fig. 7. Due to the thick adipose layer of Sample A, the optical probing volume for nb-DRS consisted of primarily adipose tissue. In contrast, MOI was extracted based on a ∼3 cm thick excision, which included additional lean tissue inflating the perceived water content. In contrast, as a result of the thinner adipose layer of Sample B, a volume more similar in composition was analyzed by both optical and chemical methods.  7. An ex-vivo porcine sample was optically measured at two different spatial locations. At the center of each optical measurement, approximately 3 × 3×3 cm of tissue was excised to calculate MOI. R H2O as calculated using our nb-DRS technique (n=18 per sample) and MOI extracted by analytical chemistry methods (n=4 per sample) are compared.

Discussion
To evaluate our nb-DRS method to quantify H2O and FAT content from scattering media, a series of phantoms were fabricated using various distilled water and oil ratios. The wavelength region 900-1000 nm was selected to include known FAT and H2O absorption peaks at 930 nm and 976 nm, respectively. While other studies have reported H2O and FAT of turbid media by quantitative approaches [33][34][35]37,38], our nb-DRS technique utilizes basic diffuse reflectance, which is a measure of relative light intensity over wavelength. However, despite the absence of absolute optical properties, the shape of the intensity spectrum can still provide the necessary information to estimate H2O and FAT. As depicted in Fig. 3, although our µ a spectra illustrated cannot be considered truly absolute due to an assumed scattering, the H2O peak can be seen rising relative to the FAT peak as the phantom series progressed, as expected. We recover a relative chromophore concentration by fitting a linear combination of C H2O , C FAT , and SF by Eq. (3) wherein inclusion of the SF allows for the de-emphasis of absolute values in favor of fitting to the shape of the absorption spectra. However, even so, attempting to directly determine chromophore concentrations from a relative absorption spectrum will result in inaccurate H2O and FAT values. Thus, we introduced parameters R H2O and R FAT , which are ratios based on a common absorption spectrum. Using our nb-DRS approach, when drawing comparisons to the known soybean oil and distilled water proportions, we find a highly linear trend and excellent agreement with expected results.
Calibrated reflectance can be related to absorption as shown by Eq. (1). However, in lieu of a priori information about the optical properties of the sample, we assume a scattering profile to estimate absorption. We observed that our method was insensitive to the scattering assumption and final R H2O and R FAT results were only affected within a margin of 1-2%. This can be explained as R H2O and R FAT are defined as ratios, each which reference the same absorption spectrum in a narrow wavelength range. As predicted with Mie theory, the wavelength dependance of µ' s (λ) diminishes with increasing wavelength, making the scattering profile nearly spectrally flat in the 900-1000 nm region [48]. Within reasonable ranges for biological tissues, the mild slope of µ' s (λ 900−1000 ), while affecting the absolute value of µ a (λ 900−1000 ), minimally impact the overall shape of µ a (λ 900−1000 ). Thus, as our method utilizes parameters R H2O and R FAT , more simplified nb-DRS devices can be developed without the need to include complex hardware to precisely solve for scattering as seen in other quantitative NIR approaches.
To validate optically measured R H2O in an ex-vivo porcine sample, our approach was to directly extract moisture content from the tissue by analytical chemistry methods. Two sections of porcine tissue were utilized. "Sample A" was observed to have a significantly thicker top adipose layer than "Sample B". At our source-detector configuration, our mean optical penetration depth and over 95% of our optical signal was likely contained within the first 1 cm of entering the samples [8]. However, for the MOI analysis, much larger 3 × 3×3 cm volumes were removed from each sample in order to obtain sufficient sample mass for multiple dehydration trials. Thus, discrepancy between R H2O and MOI may be explained by a difference in analyzing volumes. For Sample A, sensitivity of nb-DRS may have been constrained to the top adipose layer containing a higher FAT content and lower H2O content, while for the MOI analysis, significantly more lean tissue may have been included in the 3 × 3×3 cm excisions. Indeed, R H2O for Sample A is in agreement with past work reporting 0.20-0.30 H2O fraction in abdominal tissues [8,49]. In contrast, a MOI value of 0.43 likely indicates a mixed adipose-muscle composition. For Sample B, due to the thinner adipose presence, tissue volumes for the optical and MOI extraction methods both contained samples of similar composition (lean tissue dominant), thus were in closer agreement. In subsequent studies, the optical penetration depth could be simulated by models of photon propagation in order to guide the depth of tissue removed for MOI analysis.
In comparison to previously reported studies based on frequency-domain DOS [34] and time-domain DOS [35], our linearity and accuracy results are comparable. However, our approach utilized significantly less complex optical and electrical hardware, in essence only requiring spectrometry of a 100 nm (900 nm to 1000 nm) wide light source. R H2O and R FAT error was observed to increase with H2O content of the sample. This was due to the decreasing lipid peak prominence combined with overshadowing by H2O absorption, as illustrated in Fig. 3. Additionally, the enhanced absorption in H2O-dominated samples resulted in a lower signal-to-noise ratio which can impact the accuracy when fitting for chromophore concentrations. This may be remedied by changing our light source, a tungsten-halogen lamp, and specializing in higher powered 900-1000 nm light-emitting diodes (LEDs). Our method was also shown to maintain a high degree of accuracy in lipid dominated tissues, which may be particularly useful for applications on abdomen and breast. Notably, our method presented near perfect linearity when comparing measured R H2O and R FAT with expected values. This linearity translates to direct correlation between R H2O and R FAT and real-world H2O and FAT changes, respectively, with a high degree of sensitivity.
While the spectrometer used in this study was the largest component of our system in terms of physical dimensions, commercially available NIR miniature spectrometers could be utilized to produce a portable nb-DRS device based on our proposed method. Furthermore, a photodiodebased device with LEDs or lasers centered at wavelengths determined by optimization analysis [50] could result in an even more compact or wearable-type sensor.

Conclusions
We report a nb-DRS method capable of estimating H2O and FAT in turbid media. Our approach was tested on a series of scattering emulsion phantoms with various water to lipid ratios comparing favorably to the expected results. In addition, we showed that our metrics were insensitive to various scattering assumptions, absolving the need for accurate scattering quantification. Finally, our technique was applied to porcine samples and compared to direct hydration extraction by analytical chemistry methods. Our nb-DRS method may be utilized by simplified tissue composition devices. We see potential benefits not only in medical fields, such as for tracking for patients' hydration status, but also in consumer sectors such as for fitness trackers and personal at-home health wearables.