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使用机器学习预测特种咖啡中的SCA分数

Gabriel Rezende Ferraz¹, Felipe André Oliveira Freitas¹, Harim H Baldi¹

¹Universidade de Sao Paulo (USP), Piracicaba, Brasil.

Journal of food science

|February 28, 2026

概括

机器学习模型可以使用咖啡加工数据预测特种咖啡协会 (SCA) 的分数. 变量选择为估计咖啡质量提供了强大而有效的方法,减少了对广泛感官评估的依赖.

科学领域:

农业科学农业科学
数据科学数据科学数据科学
食品科学食品科学食品科学

背景情况:

特种咖啡的质量对其全球商品价值至关重要.
专业咖啡协会 (SCA) 的评分是关键的商业指标.
对于SCA分数的感官评估是资源密集和可变的.

研究的目的:

开发用于估计SCA分数的预测模型.
利用加工和生产变量进行质量预测.
为了比较不同的效率和稳健性的建模方法.

主要方法:

收集的处理数据 (2019-2023) 涵盖接收到存储.
应用随机森林 (RF) 和XGBoost (XGB) 回归算法.
评估了三种方法:完整变量,PCA和选定变量.

主要成果:

所有变量的射频模型显示出最佳性能 (MAE=0.80,RMSE=1.03,R2=0.53).
使用七个选定变量的模型取得了可比结果 (MAE=0.81,RMSE=1.06,R2=0.50).
变量选择比PCA更有效和更强大;基于PCA的模型表现更差.

结论:

机器学习模型可以适度且实际预测SCA分数.
与PCA相比,变量选择提高了模型的效率和稳定性.
预测工具可以支持在特种咖啡生产中更早,更有效的质量监测.

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