储存果的激光质量评估:整合LIBS,LIPA和DFT分析
Muhammad Umar Majeed1, Asiri Iroshan1, Muhammad Kashif Majeed2
1State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion, Nanjing University of Information Science & Technology, Nanjing 210044, China; Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing, 9, 210044, China.
Food chemistry
|January 30, 2026
概括
一种结合激光诱导分解光谱 (LIBS) 和激光诱导等离子声学 (LIPA) 信号的新方法准确地在不同温度下对果肉质量进行分类. 这种LIBS-LIPA方法在质量评估中显著改进了仅LIBS的方法.
科学领域:
- 农业科学 农业科学
- 频谱学是一种光谱学.
- 声学 声学 在声学方面
背景情况:
- 准确评估水果质量对于食品工业至关重要.
- 非常希望使用非破坏性的方法来评估果实内部的特性.
- 温度显著影响果肉特性和质量指标.
研究的目的:
- 开发和验证一种新的现场方法来分类果肉质量.
- 研究激光诱导分解光谱 (LIBS) 和激光诱导等离子体声学 (LIPA) 信号的联合潜力.
- 评估温度对开发方法的分类性能的影响.
主要方法:
- 实施LIBS-LIPA组合技术进行现场果肉分析.
- 在各种温度范围内测试该方法: 2°C,15°C和25°C.
- 使用机器学习算法 (KNN,梯度提升,随机森林,XGBoost,SVM) 进行分类,并使用沙普利增量解释 (SHAP) 实现模型透明度.
- 使用密度函数理论 (DFT) 计算来支持实验发现.
主要成果:
- 合并的LIBS-LIPA模型实现了高分类准确度,从94%到98%不等.
- 仅LIBS的分类业绩明显较低,在48%至85%之间.
- 声信号,特别是LIPA-LD2 (15.66%),被认为对捕捉结构变化和加强分类至关重要.
- DFT的计算证实了特定元素在声学特征中的占主导地位.
结论:
- 集成的LIBS-LIPA方法为非破坏性果肉质量分类提供了强大而准确的方法.
- 与单独的光谱学相比,纳入声信号可以大幅提高分类性能.
- 开发的方法在不同的温度条件下证明了有效性,突出了其实际适用性.
关键词:
在 DFT 方面,它是最重要的.在 LDA LDA 中.在LIBS中,LIBS是指LIBS.利帕·利帕 (Lipa Lipa) 是一个名字.ML ML 在 ML这就是 SHAP SHAP 的意思.更多相关视频
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