肉类科学与可解释AI的协同作用:量化脆度梯度,用于提拉皮片加工的质量认证
Shuqi Tang1, Ling Zhang2, Xingguo Tian3
1College of Engineering, South China Agricultural University, Guangzhou 510642, China; Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence, Guangzhou 510642, China.
Food chemistry
|April 19, 2025
概括
超光谱成像与双分支卷积神经网络 (DB-CNN) 结合,准确地分类了鱼片的脆度. 这项技术增强了对脆脆的鱼和其他水产品的食品质量控制.
科学领域:
- 食品科学 食品科学 食品科学
- 频谱学是一种光谱学.
- 人工智能的人工智能
背景情况:
- 脆鱼是一种高需求的水产品,但其质量随着脆度而变化,影响营养价值和味道.
- 对生产商,经销商和消费者来说,准确评估鱼片的脆度至关重要.
- 超光谱成像 (HSI) 为详细的食品质量分析提供了丰富的光谱数据.
研究的目的:
- 开发和验证一种方法来根据其脆度阶段对鱼片进行分类.
- 利用HSI技术和一种新的深度学习模型进行客观的质量评估.
主要方法:
- 使用超光谱成像 (HSI) 来捕获来自鱼片的光谱数据.
- 开发了一种双分支卷积神经网络 (DB-CNN),以分别处理可见和近红外 (VNIR) 和短波红外 (SWIR) 数据.
- 在特征空间中融合的光谱特征用于分类.
主要成果:
- 在DB-CNN模型中,鱼片脆度的分类准确率达到95.74%.
- 拟议的方法优于传统的光谱数据融合技术.
- 格拉德-CAM++可视化证实了该模型专注于相关的光谱特征.
结论:
- HSI和DB-CNN的方法为客观的质量控制和脆脆的鱼片的真实性识别提供了有效的解决方案.
- 这种方法显示出广泛应用的巨大潜力,用于根据质量属性对各种食品和水产品进行分类.
更多相关视频
相关概念视频
02:07Improving Translational Accuracy
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
02:07Improving Translational Accuracy
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...