A detailed investigation into the use of Raman spectroscopy for determining water temperature is presented. The temperature dependence of unpolarized Raman spectra is evaluated numerically, and methods based on linear regression are used to determine the accuracy with which temperature can be obtained from Raman spectra. These methods were also used to inform the design and predict the performance of a two-channel Raman spectrometer, which can predict the temperature of mains supply water to an accuracy of ± 0.5 °C.
Raman spectra for a natural water sample have been comprehensively investigated as a function of temperature and salinity, and we demonstrate that temperature and salinity can be determined from Raman spectra with RMS errors consistently below ±0.2 °C and ±0.6 PSU respectively where there is variation only in one parameter. Most significantly, we have applied multivariate methods to show that both temperature and salinity can be determined simultaneously from Raman spectra with RMS errors of ±0.7 °C and ±1.4 PSU respectively, and designed a three-channel Raman spectrometer that will be used for future studies.
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.
The design and operation of a custom-built LIDAR-compatible, four-channel Raman spectrometer integrated to a 532 nm pulsed laser is presented. The multichannel design allowed for simultaneous collection of Raman photons at two spectral regions identified as highly sensitive to changes in water temperature. For each of these spectral bands, the signals having polarization parallel to (∥) and perpendicular to (⟂), the excitation polarization were collected. Four independent temperature markers were calculated from the Raman signals: two-colour(∥), two-colour(⟂), depolarization(A) and depolarization(B). A total of sixteen datasets were analysed for one ultrapure (Milli-Q) and three samples of natural water. Temperature accuracies of ±0.4 °C–±0.8 °C were achieved using the two-colour(∥) marker. When multiple linear regression models were constructed (linear combination) utilizing all simultaneously acquired temperature markers, improved accuracies of ±0.3 °C–±0.7 °C were achieved.
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