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Updated: Jan 7, 2026

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一个多式融合模型,用于利用机器视觉,超光谱成像和电子鼻子来预测核桃核的感应的脆度

Jiacheng Fu¹, Dan Dai¹, Shunying Huang¹

¹College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou, 311300, China.

Current research in food science

|January 1, 2026

概括

这项研究引入了一种新的多模式交叉注意力融合网络 (MCAFNet),用于核桃的非破坏性脆度评估. MCAFNet准确地预测了脆度,为基于坚果的食品的智能质量监测提供了一个有效的解决方案.

科学领域:

食品科学与技术食品科学与技术
人工智能在农业中的应用
感官分析和质量控制

背景情况:

评估像核桃这样的坚果食品的脆度是复杂的,特别是在热处理后.
现有的质量评估方法可能具有破坏性或缺乏精度.
有效的质量监测需要智能,非破坏性技术.

研究的目的:

开发一种新的智能系统,用于对林安山核桃进行非破坏性脆度评估.
解决质量评估中整合多模式数据 (电子鼻子和光谱) 的挑战.
提高预测基于坚果产品的物理质量属性的准确性和效率.

主要方法:

使用多模式交叉注意力融合网络 (MCAFNet) 集成电子鼻子和光谱数据.
使用马尔科夫过渡场 (MTF) 将时间序列信号转换为图像以提取时间特征.
实现了多尺度卷积,光谱指数和双分支特征融合模块,并专注于深度数据交互.

主要成果:

对于度预测,MCAFNet实现了0.968的高确定系数 (R2).
该模型显示剩余预测偏差 (RPD) 为5.578,表明预测性能很好.
提出的方法有效地融合了多模式数据,克服了语义差距并增强了特征表示.

结论:

MCAFNet提供了一种高效准确的解决方案,用于对以坚果为基础的产品进行非破坏性脆度评估.
该研究为食品工程中的智能质量监测奠定了坚实的理论和实践基础.
这种方法对食品工业的质量控制和流程优化产生了重大影响.

关键词:

核桃核的脆度检测核桃核的检测.深度学习是一种深度学习.多式联络融合是多式联络的融合.非破坏性测试是指非破坏性测试.

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