Impact of fluorescence on Raman remote sensing of temperature in natural water samples

A comprehensive investigation into the impact of spectral baseline on temperature prediction in natural marine water samples by Raman spectroscopy is presented. The origin of baseline signals is investigated using principal component analysis and phytoplankton cultures in laboratory experiments. Results indicate that fluorescence from photosynthetic pigments and dissolved organic matter may overlap with the Raman peak for 532 nm excitation and compromise the accuracy of temperature predictions. Two methods of spectral baseline correction in natural waters are evaluated: a traditional tilted baseline correction and a new correction by temperature marker values, with accuracies as high as ± 0.2°C being achieved in both cases. © 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement


Introduction
The use of Raman spectroscopy to predict water temperature was first proposed in [1,2] and the potential for extending this to determine depth-resolved temperature profiles using LIDAR methods was investigated in [3,4]. The applications for such knowledge are extensive, and include making predictions about underwater communication, validating hydrologic and climate change models, and obtaining habitat information. In principle, the methods could be compatible with airborne, surface, land-based or even underwater platforms. Since the comprehensive studies of several decades ago, there have been a modest number of studies that have advanced the field [5][6][7], accompanied by large advances in the sensitivity of photomultipliers, spectrometers and numerical statistical methods.
In 2015, we reported our first work in this field, harnessing these advances to obtain high quality Raman spectra, using statistical methods to identify the spectral parameters most sensitive to temperature change, and systematically predicting the accuracy with which temperature could be predicted by means of a temperature marker. In this case the temperature marker was the ratio of Raman signal intensities at two particular frequencies within the broad Raman band associated with OH stretching [8]. The method is commonly known as the "two-colour method", and the marker is commonly called the "two-colour ratio". It was found that water temperature could be predicted with an accuracy better than ± 0.2 °C in the case of pure (reverse-osmosis) water, and informed the design of a simple twochannel apparatus that used pulsed excitation, high fidelity filters and photomultipliers in proof of principle experiments to predict the temperature of tap water in a 1 m long cell to within ± 0.5 °C. That was a first step towards optical instrumentation for vertically profiling water temperature in natural environments.
A key challenge to implementing the two-colour method for temperature determination in real environments is the presence of additional materials such as dissolved organic matter species of phytoplankton and measured the concentrations that gave rise to a baseline large enough to perturb the Raman signal. These concentrations were within the normal range of concentrations along the coast of eastern Australia. We propose two methods for correcting for fluorescence and present the accuracy with which temperature can be predicted with and without baseline correction, considering the implications of our findings for developing future methods that are less susceptible to the presence of fluorescing matter.

Raman spectral measurements and analysis
Natural water samples were collected from various locations around Sydney. These include Manly Beach, which is outside the Harbour, Clontarf and Sugarloaf Bay, which are located in Middle Harbour approximately 4 km and 7 km respectively from the Harbour entrance, and Rhodes which is located in the main harbour approximately 20 km from the Harbour entrance. Water samples from Rose Bay were of particular importance to this study. These samples were collected from deep waters in an open part of the harbour, approximately 2 km from its entrance and investigated after being filtered and UV treated. Water samples from Manly Dam, a freshwater body of approximately 2000 ML located in an urban bush reserve were investigated, along with pure (Milli-Q) laboratory water.
Raman spectra were collected for each sample within a few hours of being collected, and all the data was collected, using methods that have been described in detail in [8]. Briefly the spectrometer used was an Enwave EZRaman-I, a dispersive Raman spectrometer having a spectral resolution of 8 cm −1 and using 30 mW continuous-wave (CW) laser at 532 nm for excitation (Fig. 1). The unpolarised Raman signal was detected using a 180° backscattering geometry, and wavelength calibration of the spectrometer was carried out using an acetonitrile (CH 3 CN) reference sample. Spectral data were smoothed with the Savitsky-Golay algorithm to reduce noise (2nd order, 25-point window). The spectrometer integration time was typically 30 seconds and each spectrum shown is an average of 3 acquisitions to improve consistency. Each sample was conditioned inside a quartz cuvette (pathlength of 10 mm) and stepped through a range of temperatures from 12 to 33°C, with a waiting time of several minutes allowed after reaching each set point to enable the water sample to reach thermal equilibrium. The reference temperature was measured using a temperature probe within the QNW QPod2e, which has a specified accuracy of ± 0.2°C.
The unpolarised Raman spectra were analysed using the "two-colour" method, in which the temperature marker is the ratio of the Raman signals in spectral bands on either side of the isosbestic point (point of equal scattering) of the OH stretching band. This ratio is found to be proportional to water temperature. In this work, as in [8], the ratio is determined by integrating the Raman signals that correspond to the spectral bands of interest. The spectral resolution (8 cm −1 ) was substantially lower than the channel widths over which Raman signals are integrated (200 cm −1 ). The analysis method involves carrying out a linear least squares regression, using MatlabR2017b, of the two-colour ratio against the measured reference temperature to yield a temperature-predictive model. Each combination of wavenumber pairs produced a Root Mean Squared Temperature Error (RMSTE), the set of which was used as a parameter for estimating the accuracies of temperature predictions in various water samples.

Phytopla
Non-axenic c sp., Ditylum b were obtained Sydney and m Cultures wer microscopy. O every two we the study.
To investi spectra, thre Nannochlorop most phytopla volume in th medium samp Raman spectr each sample. concentration microscope. Figure 2 show corresponding Rose Bay, C registered by spectral respo shown in Fig  well- Fig. 3(c)) positive alues for her) than gs plot is alues for of DOM fluorescence; and from 665 nm to 700 nm, including the Chl-a fluorescence peak at 680 nm. Negative loadings at these spectral regions indicate higher than average values for samples with negative scores for PC-1 (Fig. 3(a)), represented by Rhodes, Clontarf and Manly Dam. Ultimately, these samples exhibited higher than average variance at spectral regions with known spectral signatures associated with fluorescence of common optically active constituents in natural waters. Rose Bay, Sugarloaf Bay and Manly Beach had positive PC-1 scores.

Backgrou origin
PC-2 accounted for 3% of total modelled variance among locations and its loadings plot show well-defined negative peaks at around 580 nm, area of Gelbstoff fluorescence and 680 nm (Chl-a) (Fig. 3(c)). Negative loading peaks were associated with high variabilities for negative scores, here attributed to Rhodes (Fig. 3(a)), indicating higher than average values for fluorescence on this sample. Conversely, the analysis indicated lower than average values for fluorescence at these well-defined peaks in the Clontarf sample. Rose Bay, Sugarloaf Bay, Manly Beach and Manly Dam samples exhibited PC-2 scores close to zero, indicating PC-2 doesn't explain significant spectral variances on these samples.
In summary, the PCA analysis reveals systematic but complex differences between the water samples investigated. It is interesting to note that including a freshwater sample (Manly Dam) didn't compromise the model performance. It may be indicative that, when temperature variation is excluded, background fluorescence seems to be more important than salinity effects on spectral variance among these natural water samples.
Having established that fluorescence largely accounts for the background effects we observe, we sought to explore the extent to which fluorescence from different species of phytoplankton might interfere with measurements of the Raman spectra for natural waters, and to quantify the concentration of phytoplankton that might substantially modify the Raman signal. To do this we selected species of phytoplankton that have a range of characteristic pigments that are broadly representative of the major phytoplankton groups such as zeaxanthin, phycocianin, phycoerythrin, carotene and violaxathin. These were Synechococcus Red, Synechococcus Green, Nannochloropysis sp., Ditylum Brightwelli, Dunallella tertiolecta, Ostreopsis siamensis and Rhodomonas salina. Dilute samples of each were placed in a Varian Eclipse fluorescence spectrometer and emission and excitation spectra recorded. Each species, with the exception of Synechococcus Red, when excited at 532 nm exhibited fluorescence with a broad (typically 10-30 nm full width at half maximum) peaks in the range 660-685 nm. When excited at the shorter wavelength of 473 nm, Synechococcus Red and Rhodomonas salina exhibited additional peaks at 560 and 590 respectively. The fluorescence from phytoplankton has been widely studied and our observations are consistent with the literature [16].
Having observed the overlap between phytoplankton fluorescence and the Raman signal, we set about investigating what phytoplankton concentrations would cause significant distortion to the Raman signal. In order to do this, Raman spectra were recorded while small amounts of phytoplankton were added to the f/2 growth medium. The intensity of the Raman signal exhibited considerable variation after the phytoplankton was added, and we hypothesise that this variation was due to phytoplankton drifting across the excitation volume.
Selected fluorescence spectra obtained using 532 nm excitation are shown in Fig. 4. Figure 4(a) shows the Raman spectrum obtained for the f/2 growth medium, and it can be seen that the Raman band is clean, with relatively low background. Substantial changes were observed as phytoplankton was added, with the Raman band then being superimposed upon a fluorescence pedestal. For Nannochloropsis and Synechococcus Green, the fluorescence was peaked around 680 nm, while for Synechococcus Red, substantial fluorescence occurred around 575 nm. The corresponding spectra are shown in Fig. 4, along with the corresponding phytoplankton cell counts that were determined using the counting procedure outlined earlier. The concentrations are comparable to those found in nature, and thus it is clear that the presence efficiency wit

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On the basis signals obser phytoplankton concentration excitation at Raman excita already used correction te accuracy usin It is appar reduce the ac wavelength fo with which t effective met prediction an RMSTE map Fig. 5

Discussion
The marine environment is known for its complex and dynamic nature, especially in coastal areas, resulting in high variability of water properties such as active optical components, salinity and vertical thermal stratification. For these methods presented here to be truly useful, they need to be applicable to a range of water samples in which fluorescing material may be present, and this paper is intended to advance that cause. We have taken a qualitative approach to establishing that the baseline found in our Raman spectra for natural water samples arise mainly from DOM and Chl-a fluorescence. Furthermore, we have measured the phytoplankton concentrations that give rise to substantial fluorescence which overlaps with the Raman spectral band.
We have demonstrated two methods for baseline correction which are effective in increasing the accuracy with which temperature can be determined in natural waters. In terms of ease of field implementation, both methods present distinctive challenges. The tilted baseline correction method would require an additional two channels for collecting the very weak signals arising from fluorescence on either side of the Raman band, thereby reducing the signal-to-noise ratio for each of the main channels. A second drawback of this method is that there is no physical reason to expect the fluorescence spectrum to be linear around the Raman feature, and this is the fundamental assumption of the method Method 2 does not require special filters or impact directly on SNR but it does require a database of temperature-dependent ratios as standards for comparison. In principle, ratios for all Jerlov water types could be obtained and used for calibration; alternatively, a local water sample can be collected and analysed to determine the correction factors required for analysis.
The work presented here provides proof of concept and future work will test the viability of this promising method. To this end, we have recently reported [19] a 4-channel Raman spectrometer that uses pulsed excitation and fast detection by photomultipliers. It has been used, in the laboratory, to predict water temperature of 3 natural water samples with accuracies in the range ± 0.4 °C to ± 0.8 °C. That recent work is an important step in validating the approach outlined in this manuscript, because it proves that it is not necessary to acquire the full Raman spectrum in order to predict temperature. We anticipate that the correction methods described here could lead to improved accuracies in predicting temperature, and also expand the range of waters that can be covered by a single model.

Conclusion
The findings in this paper help us to move one step forward our final goal: a LIDARcompatible custom-built multichannel Raman spectrometer able to measure depth-resolved temperature data in situ as firstly proposed in [8].
We have shown that Raman spectra collected from natural water samples around Sydney Harbour exhibit background signal levels that adversely affect the accuracy with which temperature can be determined. We have shown the background signals arise from fluorescence, allegedly from DOM and Chl-a, and quantified the phytoplankton concentrations that cause distortion of the OH stretching band. We have proposed two methods of baseline corrections that are effective in improving temperature accuracy and considered how they could be implemented in the field. There is scope for a systematic fluorescence and Raman spectroscopic study that considers a wider range of samples which are characterised in terms of DOM concentration, chlorophyll-a concentration and salinity.