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基于可解释的机器学习来评估Korla梨的脆度:在高采样率下同步进行仪器机械声学测试
Xiaoming Mo1,2, Jiaping Wang1, Youfang Yu1,3
1College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832003, China.
Current research in food science
|June 23, 2025
概括
这项研究引入了一种新的仪器方法,通过合并机械声学信号来预测水果的脆度. 先进的极端梯度增强 (XGBoost) 模型准确评估梨的新鲜度和质地.
科学领域:
- 食品科学 食品科学 食品科学
- 工具分析 工具分析
- 机器学习 机器学习
背景情况:
- 水果的脆度对于质地评估,新鲜度和消费者接受度至关重要.
- 需要客观的仪器方法来补充果实质感的感觉分析.
研究的目的:
- 开发和验证一种用于预测水果脆度的新型仪器方法.
- 通过机器学习提高脆度预测的准确性和可靠性.
主要方法:
- 在高采样速率 (51,200 Hz) 的同步机械声学信号采集.
- 机械声学信号的数据级融合以模仿人类的感知.
- 功能提取 (26 个功能) 和机器学习模型开发 (XGBoost).
- 对于模型可解释性和特征选择的沙普利添加式解释 (SHAP).
主要成果:
- 极端梯度增强 (XGBoost) 模型在脆度预测方面表现出卓越的性能.
- 使用14个积极贡献特征的可解释模型实现了高精度 (R_P^2 = 0.92,RMSEP = 0.32,RPD = 3.66).
- 提出的方法显著提高了脆度预测的准确性,稳定性和可靠性.
结论:
- 同步采集高采样率,数据级融合和正特征选择提高了仪器脆度预测.
- 开发的方法为客观测量梨的脆度提供了可靠的方法.
- 这种技术可能适用于各种各样的水果和蔬菜.
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