Rapid Evaluation of Smoke exposure in Grapes and Wine by Raman Spectroscopy – A Concept Proposal

Summary – Final Report:

Raman Spectroscopy of Smoke Compounds

We have evaluated ten different smoke compounds in synthetic wine using a 785 nm Wavelength Raman setup using defined parameters to gain spectra.  Individual compounds and combinations were tested to provide us with initial results showing the various Raman fingerprints of the smoke compounds and help determine any potential interferences between them.

Development of a Spectrum Collection Method using Gold Nanoparticle strips

We developed a method for data collection to improve the reproducibility of obtaining the Raman spectrum, using gold nano-strips and a 3D-printed 3×3 grid.  The nano-strips were cut into nine identical pieces, each placed in a well on a 3×3 grid.  Then, our sample was dropped onto the nano-strips for spectrum collection.  Our scan method involves obtaining five separate scans for a set period to determine if signal enhancement improves as the nano-strip becomes more saturated.  We are creating an auto-sampling system for the 785nm Raman to streamline data collection as we finalize the enhancement protocols.

Surface Enhanced Raman Spectroscopy using Gold Nanoparticle Strips

Using the commercially produced gold nano-strips, we obtained scans of smoke compounds in synthetic wine that show strong Raman enhancement properties.  Initial scans of smoke compounds were obtained to have benchmark results, and then the nano-strips were used to determine enhancement effectiveness with lower concentrations.  Two significant results were obtained; a unique fingerprint for guaiacol using the nano-strips was found in the form of a very strong peak at 576 cm-1, and we have been able to detect guaiacol at concentrations in synthetic wine as low as 100ppb (parts-per-billion) using the nano-strips.

Removal of Anthocyanins and Tannins through different Extraction Methods

We have trialed many different extraction methods testing the removal of anthocyanins, tannins, and any other compounds present that interfere with the obtained spectra of smoke compounds in wine.  Various parameters were tested to determine the best method for removal while recovering the smoke compounds present to figure out how to obtain clean scans of smoke compounds in a wine matrix.  Scans of two different types of wines spiked with smoke compounds prior to extraction were obtained to see how well these extraction methods recovered the compounds when collecting Raman spectra.  Un-spiked red wine was also treated through the extraction process and then spiked with smoke compounds to see if there was any interference with scans taken post-extraction using an actual wine matrix rather than a synthetic wine matrix.

Summary of Major Research Accomplishments and Results by Objective

In the 2023-2024 funding year, we have created a new data collection method for observing Raman enhancements using the gold nanoparticle strips, which has produced promising results and allowed us to see phenolic concentrations at very low levels.

We have collected Raman spectra of 10 phenolic compounds of interest (Guaiacol, 4- methylguaiacol, 4-ethylguaiacol, 4-ethylphenol, 4-vinylphenol, whiskey lactone, phenol, o-cresol, m-cresol, and p-cresol) using both of our data collection methods for the 785nm Raman spectrophotometer, the multi-well plate with liquid silver nanoparticles and the 3×3 grid with gold nanoparticle strips.  The gold nanoparticle strips produced data with strong repeatability and noticeable Raman enhancements.

Using these data collection methods, we have established benchmarks for the Raman fingerprints of each of the ten compounds we are testing with the 785nm Raman setup.  This data has been used for signal confirmation of these compounds when conducting scans at low concentrations.

We have successfully produced spectra of some phenolic compounds at low concentrations in a synthetic wine matrix, specifically guaiacol at a parts-per-billion level.  The collected data has been reproduced successfully for method confirmation.

Various solid-phase and stir-bar sterile extraction methods have been tested and refined for sample treatment, attempting to remove anthocyanins/tannins and other compounds that provide spectra interference.  Color removal has been successfully achieved, and now the focus is on increasing signal or concentration enhancement and testing different solvents for the most effective elution process.

Using our previously developed machine learning algorithm, we collected spectra of each compound with the 785nm to remove the Rayleigh scattering and background fluorescence present.  After backgrounds were removed, the intensities of each peak were captured through a regression algorithm.  After the base models were built using average spectra, each sample was measured using juice or wine base models to report the intensity of each peak.  Intensities of extracted peaks were subsequently analyzed using a correlation matrix and principal component analysis.  The above metrics were fed into Orange Data Mining for data processing and ML development.