使用挥发性有机化合物指纹和机器学习对谷物衰老进行智能歧视:全面审查
Liuping Zhang1,2, Jingtao Zhou3, Guoping Qian2
1School of Food Science and Technology, Jiangnan University, Wuxi 214122, China.
Foods (Basel, Switzerland)
|January 28, 2026
概括
智能谷物存储使用挥发性有机化合物 (VOC) 指纹和机器学习 (ML) 来检测老化. 这种方法提供了一种非破坏性的实时方法来监测谷物质量并提高粮食安全.
科学领域:
- 农业科学 农业科学
- 分析化学 分析化学
- 数据科学数据科学数据科学
背景情况:
- 谷物老化导致质量损失和经济损失,需要先进的监测.
- 传统的谷物分析方法缓慢且劳动密集,阻碍了智能储存.
- 挥发性有机化合物 (VOC) 是用于早期检测谷物衰老的敏感生物标志物.
研究的目的:
- 通过使用VOC指纹和机器学习 (ML) 审查谷物智能老化歧视的最新进展.
- 探索谷物衰老的生物化学基础和VOCs作为生物标志物的作用.
- 讨论在谷物质量评估中用于VOC测定和ML应用的方法.
主要方法:
- 头部空间固态微提取与气色谱-质谱学 (HS-SPME-GC-MS) 结合,用于VOC指纹.
- 机器学习算法的应用 (PLS-DA,SVM,RF,CNN) 用于模式识别和分类.
- 分析高维的VOC数据,以区分老化阶段,腐烂和谷物品种.
主要成果:
- 与ML结合的VOC指纹有效地区分谷物老化阶段和腐烂.
- HS-SPME-GC-MS为分析提供了全面的挥发性概况.
- 机器学习算法在分析复杂的谷物质量数据方面显示出巨大的潜力.
结论:
- 将VOC分析与ML集成为智能谷物监测提供了一种强大而非破坏性的方法.
- 方法的标准化,数据库开发和现实世界的验证对于实际应用至关重要.
- 该框架支持通过改善谷物质量管理来加强全球粮食安全.
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