Kulasiri Don, Somin Sarawoot, Kumara Pathirannahalage Samantha
Centre for Advanced Computational Solutions (C-fACS), Lincoln University, Lincoln 7647, New Zealand.
Foods. 2024 Sep 27;13(19):3091. doi: 10.3390/foods13193091.
The quality of wine depends upon the quality of the grapes, which, in turn, are affected by different viticulture aspects and the climate during the grape-growing season. Obtaining wine professionals' judgments of the intrinsic qualities of selected wine products is a time-consuming task. It is also expensive. Instead of waiting for the wine to be produced, it is better to have an idea of the quality before harvesting, so that wine growers and wine manufacturers can use high-quality grapes. The main aim of the present study was to investigate the use of machine learning aspects in predicting Pinot Noir wine quality and to develop a pipeline which represents the major steps from vineyards to wine quality indices. This study is specifically related to Pinot Noir wines based on experiments conducted in vineyards and grapes produced from those vineyards. Climate factors and other wine production factors affect the wine quality, but our emphasis was to relate viticulture parameters to grape composition and then relate the chemical composition to quality as measured by the experts. This pipeline outputs the predicted yield, values for basic parameters of grape juice composition, values for basic parameters of the wine composition, and quality. We also found that the yield could be predicted because of input data related to the characteristics of the vineyards. Finally, through the creation of a web-based application, we investigated the balance of berry yield and wine quality. Using these tools further developed, vineyard owners should be able to predict the quality of the wine they intend to produce from their vineyards before the grapes are even harvested.
葡萄酒的品质取决于葡萄的品质，而葡萄品质又反过来受不同的葡萄栽培因素以及葡萄生长季节的气候影响。获取葡萄酒专业人士对所选葡萄酒产品内在品质的判断是一项耗时且昂贵的任务。与其等待葡萄酒酿造出来，不如在收获前就对品质有所了解，这样葡萄种植者和葡萄酒制造商就可以使用高品质的葡萄。本研究的主要目的是探讨机器学习方法在预测黑皮诺葡萄酒品质方面的应用，并开发一个从葡萄园到葡萄酒品质指标的主要步骤的流程。本研究基于在葡萄园进行的实验以及由这些葡萄园生产的葡萄，专门针对黑皮诺葡萄酒。气候因素和其他葡萄酒生产因素会影响葡萄酒品质，但我们的重点是将葡萄栽培参数与葡萄成分联系起来，然后将化学成分与专家所衡量出的品质联系起来。这个流程输出预测产量、葡萄汁成分基本参数的值、葡萄酒成分基本参数的值以及品质。我们还发现，由于与葡萄园特征相关的输入数据，可以预测产量。最后，通过创建一个基于网络的应用程序，我们研究了浆果产量和葡萄酒品质之间的平衡。利用进一步开发的这些工具，葡萄园主甚至在葡萄收获之前就应该能够预测他们打算从自己的葡萄园生产的葡萄酒的品质。
