During the 2022-2023 funding cycle, we have finished the volatile phenol analysis of 82 wine samples having various degrees of smoke exposure and about 60 pooled samples from different wineries. Those samples will be used for Raman spectra collection once the nanoparticle and Raman signal enhancement protocol is finalized. We have collected grape samples throughout the 2022 season from eight vineyards in California and Oregon and an enology lab. Out of the 214 independent samples, 180 are pinot noir. The samples were collected between September 9 and October 24. About half of the samples were collected from Salem, OR, where the wildfire created a lot of worry during harvest. The lab offered us samples and enology test results in an anonymous way. More grape samples will be collected in 2023. All the grape samples will be analyzed in the 2023-2024 funding cycle simultaneously. Those samples will be used for Raman spectra collection once the nanoparticle and Raman enhancement protocols are finalized. Different sizes of silver nanoparticles have been synthesized, and the Raman enhancements were observed. However, the enhancement depends on the size, shape, and concentration of the nanoparticles. More nanoparticles with different sizes will be synthesized and evaluated in the 2023-2024 funding cycle in conjunction with a linker agent such as benzenethiol. An automated sampling system for the Raman Spectrometer was developed by Dr. Feng Ye’s team from Spectra Scientific company to replace manual spectra collection. The system is based on an alumina 3D printed 117 well plate to eliminate Raman interference from plastics. Because alumina is not stable at acidic pH, we will obtain another 3D printing plate and coat it with gold so it can be used for wine samples. 18 We have established benchmark results for the Raman fingerprints of wine samples, especially using a combination of steady-state electronic and vibrational spectroscopies in a table-top optical setup. We have implemented FSRS with a tunable Raman pump and probe pulses in a femtosecond laser amplifier system for assessing the smoke-exposed wine. We have combined FSRS with AgNPs to optimize conditions for SE-FSRS and achieve higher sensitivity than the resonance Raman enhancement alone. We are investigating nanoparticle receptors for smoke compounds to enhance the Raman signal. Raman spectrum of 82 smoke-exposed wines and 214 smoke-exposed grape samples (grape juice) were collected on the 1064nm FT-Raman system. We have developed an algorithm for machine learning. First, we applied a background subtraction algorithm to remove the Rayleigh scattering and background fluorescence. 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. The model showed an excellent correlation of Raman to titratable acidity. The undergoing research is to use nanoparticles for surface enhancement Raman Spectroscopy and smoke compound receptor to enhance the signal so that the GC-MS data can be correlated with the smoke compounds.
/wp-content/uploads/2017/09/AFV-Header-Logo.png 0 0 AVF /wp-content/uploads/2017/09/AFV-Header-Logo.png AVF2023-06-13 10:27:492023-06-13 10:27:49Raman for Smoke Exposure