首页|Data on Machine Learning Reported by Researchers at University of California (Ad vancing grape chemical analysis through machine learning and multi-sensor spectr oscopy)
Data on Machine Learning Reported by Researchers at University of California (Ad vancing grape chemical analysis through machine learning and multi-sensor spectr oscopy)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news originating from Merced, United States, by NewsRx correspondents, research stated, “Chemical testing of grapes is essential for wine-making, allowing informed decisions about grape compositio n and potential wine characteristics.” Funders for this research include University of California Merced. Our news editors obtained a quote from the research from University of Californi a: “However, current invasive laboratory methods to identify the key components as total soluble solid contents and acidity, present challenges in terms of time and cost. To address these issues, non-invasive techniques using Visible- Near-I nfrared (Vis-NIR) and Raman spectroscopy are explored for rapid and accurate gra pe chemical analysis. Various machine learning methods are deployed in this stud y to address the challenges of grape chemical analysis and enhance estimation ac curacy. The contributions of this research include pioneering a direct compariso n between Vis-NIR and Raman spectroscopy, establishing a regression model benchm ark, and providing an open-source dataset for grape composition analysis. We ide ntify Gaussian Process Regression (GPR) and Support Vector Machine Regression (S VMR) as the most effective regression models, with GPR achieving an RMSE of 0.97 7 °Birx for sugar content estimation in French Colombard grapes and SVMR achievi ng 0.780 °Brix for Cabernet grapes.”
University of CaliforniaMercedUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine Lear ning
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