基于GASF和Mel频谱特征的多传感器融合的葡萄酒歧视,使用增强的EfficientNet-B0模型
Meng He1, Jianfei Xing2, Meng Wang3
1College of Engineering, China Agricultural University, Beijing 100083, China.
Food chemistry
|September 28, 2025
概括
这项研究使用多传感器融合和深度学习来准确分类葡萄酒原材料. 这种新的策略将传感器数据转化为图像,在识别八种不同葡萄酒类型时达到92.17%的准确性.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 机器学习 机器学习
背景情况:
- 葡萄酒原料的准确分类对于质量控制和真实性至关重要.
- 传统的方法可能会在感官形状的微妙差异上扎.
- 开发客观的,高通量的葡萄酒分析方法是一个持续的挑战.
研究的目的:
- 开发和验证一种新的多传感器融合战略,以根据原材料对葡萄酒进行歧视.
- 使用先进的转换技术,从复杂的传感器数据中增强特征提取.
- 为了利用深度学习来准确地分类葡萄酒来源.
主要方法:
- 使用电子鼻子和贵金属电极收集香味和味道数据.
- 通过格拉米安角总和场 (GASF) 和梅尔光谱算法将一维时间序列传感器数据转换为二维RGB图像.
- 使用了Enhanced-EfficientNet-B0深度学习模型进行基于图像的分类.
主要成果:
- 多传感器融合策略成功地从八种不同的原料中区分了葡萄酒.
- 增强效率Net-B0模型实现了92.17%的验证准确性.
- 图像转换技术有效地保留了来自传感器数据的时间和光谱信息.
结论:
- 多传感器数据,先进的信号转换和深度学习的整合为葡萄酒原材料分类提供了准确有效的方法.
- 这种方法有可能改善葡萄酒的真实性和质量评估.
- 这项研究证明了将感官数据与食品分析中复杂的计算技术相结合的有效性.
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