Alcoholic Fermentation Monitoring and pH Prediction in Red and White Wine by Combining Spontaneous Raman Spectroscopy and Machine Learning Algorithms
In the following study, total sugar concentrations before and during alcoholic fermentation, as well as ethanol concentrations and pH levels after fermentation, of red and white wine grapes were successfully predicted using Raman spectroscopy. Fluorescing compounds such as anthocyanins and pigmented...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Beverages |
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| Online Access: | https://www.mdpi.com/2306-5710/7/4/78 |
| _version_ | 1797506595987390464 |
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| author | Harrison Fuller Chris Beaver James Harbertson |
| author_facet | Harrison Fuller Chris Beaver James Harbertson |
| author_sort | Harrison Fuller |
| collection | DOAJ |
| description | In the following study, total sugar concentrations before and during alcoholic fermentation, as well as ethanol concentrations and pH levels after fermentation, of red and white wine grapes were successfully predicted using Raman spectroscopy. Fluorescing compounds such as anthocyanins and pigmented phenolics found in red wine present one of the primary limitations of enological analysis using Raman spectroscopy. Unlike the spontaneous Raman effect, fluorescence is a highly efficient process and consequently emits a much stronger signal than spontaneous Raman scattering. For this reason, many enological applications of Raman spectroscopy are impractical as the more subtle Raman spectrum of any red wine sample is in large part masked by fluorescing compounds present in the wine. This work employs a simple extraction method to mitigate fluorescence in finished red wines. Ethanol and total sugars (fructose plus glucose) of wines made from red (Cabernet Sauvignon) and white (Chardonnay, Sauvignon Blanc, and Gruner Veltliner) varieties were modeled using support vector regression (SVR), partial least squares regression (PLSR) and Ridge regression (RR). The results, which compared the predicted to measured total sugar concentrations before and during fermentation, were excellent (R<sup>2</sup><sub>SVR</sub> = 0.96, R<sup>2</sup><sub>PLSR</sub> = 0.95, R<sup>2</sup><sub>RR</sub> = 0.95, RMSESVR = 1.59, RMSEPLSR = 1.57, RMSERR = 1.57), as were the ethanol and pH predictions for finished wines after phenolic stripping with polyvinylpolypyrrolidone (R<sup>2</sup><sub>SVR</sub> = 0.98, R<sup>2</sup><sub>PLSR</sub> = 0.99, R<sup>2</sup><sub>RR</sub> = 0.99, RMSESVR = 0.23, RMSEPLSR = 0.21, RMSERR = 0.23). The results suggest that Raman spectroscopy is a viable tool for rapid and trustworthy fermentation monitoring. |
| first_indexed | 2024-03-10T04:34:48Z |
| format | Article |
| id | doaj.art-7ba5e66dfe844f0ebc7e917e568de139 |
| institution | Directory Open Access Journal |
| issn | 2306-5710 |
| language | English |
| last_indexed | 2024-03-10T04:34:48Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Beverages |
| spelling | doaj.art-7ba5e66dfe844f0ebc7e917e568de1392023-11-23T03:51:55ZengMDPI AGBeverages2306-57102021-12-01747810.3390/beverages7040078Alcoholic Fermentation Monitoring and pH Prediction in Red and White Wine by Combining Spontaneous Raman Spectroscopy and Machine Learning AlgorithmsHarrison Fuller0Chris Beaver1James Harbertson2St Michelle Wine Estates WSU Wine Science Center, School of Food Science, College of Agricultural, Human, and Natural Resource Sciences, Washington State University, 359 University Drive, Richland, WA 99354, USASt Michelle Wine Estates WSU Wine Science Center, School of Food Science, College of Agricultural, Human, and Natural Resource Sciences, Washington State University, 359 University Drive, Richland, WA 99354, USASt Michelle Wine Estates WSU Wine Science Center, School of Food Science, College of Agricultural, Human, and Natural Resource Sciences, Washington State University, 359 University Drive, Richland, WA 99354, USAIn the following study, total sugar concentrations before and during alcoholic fermentation, as well as ethanol concentrations and pH levels after fermentation, of red and white wine grapes were successfully predicted using Raman spectroscopy. Fluorescing compounds such as anthocyanins and pigmented phenolics found in red wine present one of the primary limitations of enological analysis using Raman spectroscopy. Unlike the spontaneous Raman effect, fluorescence is a highly efficient process and consequently emits a much stronger signal than spontaneous Raman scattering. For this reason, many enological applications of Raman spectroscopy are impractical as the more subtle Raman spectrum of any red wine sample is in large part masked by fluorescing compounds present in the wine. This work employs a simple extraction method to mitigate fluorescence in finished red wines. Ethanol and total sugars (fructose plus glucose) of wines made from red (Cabernet Sauvignon) and white (Chardonnay, Sauvignon Blanc, and Gruner Veltliner) varieties were modeled using support vector regression (SVR), partial least squares regression (PLSR) and Ridge regression (RR). The results, which compared the predicted to measured total sugar concentrations before and during fermentation, were excellent (R<sup>2</sup><sub>SVR</sub> = 0.96, R<sup>2</sup><sub>PLSR</sub> = 0.95, R<sup>2</sup><sub>RR</sub> = 0.95, RMSESVR = 1.59, RMSEPLSR = 1.57, RMSERR = 1.57), as were the ethanol and pH predictions for finished wines after phenolic stripping with polyvinylpolypyrrolidone (R<sup>2</sup><sub>SVR</sub> = 0.98, R<sup>2</sup><sub>PLSR</sub> = 0.99, R<sup>2</sup><sub>RR</sub> = 0.99, RMSESVR = 0.23, RMSEPLSR = 0.21, RMSERR = 0.23). The results suggest that Raman spectroscopy is a viable tool for rapid and trustworthy fermentation monitoring.https://www.mdpi.com/2306-5710/7/4/78Raman spectroscopypredictive modelingmachine learningregressionenologywinemaking |
| spellingShingle | Harrison Fuller Chris Beaver James Harbertson Alcoholic Fermentation Monitoring and pH Prediction in Red and White Wine by Combining Spontaneous Raman Spectroscopy and Machine Learning Algorithms Beverages Raman spectroscopy predictive modeling machine learning regression enology winemaking |
| title | Alcoholic Fermentation Monitoring and pH Prediction in Red and White Wine by Combining Spontaneous Raman Spectroscopy and Machine Learning Algorithms |
| title_full | Alcoholic Fermentation Monitoring and pH Prediction in Red and White Wine by Combining Spontaneous Raman Spectroscopy and Machine Learning Algorithms |
| title_fullStr | Alcoholic Fermentation Monitoring and pH Prediction in Red and White Wine by Combining Spontaneous Raman Spectroscopy and Machine Learning Algorithms |
| title_full_unstemmed | Alcoholic Fermentation Monitoring and pH Prediction in Red and White Wine by Combining Spontaneous Raman Spectroscopy and Machine Learning Algorithms |
| title_short | Alcoholic Fermentation Monitoring and pH Prediction in Red and White Wine by Combining Spontaneous Raman Spectroscopy and Machine Learning Algorithms |
| title_sort | alcoholic fermentation monitoring and ph prediction in red and white wine by combining spontaneous raman spectroscopy and machine learning algorithms |
| topic | Raman spectroscopy predictive modeling machine learning regression enology winemaking |
| url | https://www.mdpi.com/2306-5710/7/4/78 |
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