摘要
由机器人与机器学习每日新闻的新闻记者兼工作人员新闻编辑-人工智能的新数据在一份新的报告中呈现。根据NewsRx记者来自美国默塞德的消息，研究表明，“葡萄的化学测试对酿酒至关重要，允许对葡萄成分和潜在葡萄酒特征做出知情的决定。”这项研究的资助者包括加州大学默塞德分校。我们的新闻编辑从加州大学A分校的研究中获得了一句话：“然而，目前鉴定关键成分的侵入性实验室方法，如总可溶性固形物含量和酸度，在时间和成本方面带来了挑战。”本研究利用可见近红外光谱(Vis-NIR)和拉曼光谱技术对葡萄化学分析进行了快速、准确的研究，并采用了多种机器学习方法来解决葡萄化学分析的难题，提高了葡萄化学分析的准确性。本研究的主要贡献包括：开拓了可见近红外光谱与拉曼光谱的直接比较，建立了回归模型基准，建立了葡萄化学分析的数学模型。确定高斯过程回归(GPR)和支持向量机回归(SVMR)是最有效的回归模型,GPR对法属哥伦比亚葡萄含糖量估算的RMSE为0.977°Birx,对赤霞珠葡萄的SVMR为0.780°Brix。
Abstract
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.”
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