数据集和基于机器学习的计算机辅助工具用于模拟干皮革和绿色咖啡豆中的工作合异温体
Gentil A Collazos-Escobar1,2, Andrés F Bahamón-Monje1,3, Nelson Gutiérrez-Guzmán1
1Centro Surcolombiano de Investigación en Café (CESURCAFÉ), Departamento de Ingeniería Agrícola, Universidad Surcolombiana, Neiva-Huila, 410001, Colombia.
这项研究提供了一个数据集和分析咖啡豆水吸附和光谱特性的工具. 它可以优化存储条件和对羊皮纸和绿色咖啡的质量监测.
科学领域:
- 农业科学 农业科学
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
背景情况:
- 了解水吸附对于咖啡豆的储存和质量至关重要.
- 羊皮皮影响咖啡豆的湿透性行为.
- 中红外光谱学为咖啡的成分和特性提供了洞察力.
研究的目的:
- 为了呈现一个全面的数据集的工作吸附等温和和中红外光谱的羊皮皮,羊皮咖啡,和绿色咖啡豆.
- 开发计算机辅助工具,使用机器学习对吸附异热和红外数据进行数学建模.
- 为优化咖啡储存和质量监测提供实用工具.
主要方法:
- 使用动态露点异温 (DDI) 方法在各种水活动 (aw) 和温度条件下确定工作吸附异温.
- 使用减弱总反射率-里埃变换红外光谱法 (ATR-FTIR) 获得的中红外光谱.
- 开发用于支持向量机 (SVM),随机森林 (RF) 和主要组件分析 (PCA) 建模的 MATLAB 脚本.
主要成果:
- 在受控条件下对不同咖啡类型的水吸附异温和ATR-FTIR光谱的实验数据.
- 经过验证的机器学习模型 (SVM,RF) 用于根据a,温度和咖啡类型预测平衡水分含量 (Xe).
- 使用红外光谱数据进行咖啡样本的强有力的差异化PCA模型.
结论:
- 数据集和计算工具有助于更深入地了解咖啡的湿度表现.
- 可以确定最佳的储存条件,从而改善保质期和质量.
- 该研究为咖啡行业的研究人员,生产者和利益相关者提供了宝贵的资源.
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