使用NIR光谱和可解释的人工智能进行蛋强度的非破坏性测量
Md Wadud Ahmed1, Sreezan Alam1, Alin Khaliduzzaman1
1The Grainger College of Engineering, College of Agricultural, Consumer and Environmental Sciences, Department of Agricultural and Biological Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois, USA.
Journal of the science of food and agriculture
|April 18, 2025
概括
带有可解释的人工智能 (AI) 的近红外光谱 (NIR) 为预测蛋强度提供了一种快速,非破坏性的方法. 这种方法提高了蛋行业的质量控制,提高了可持续性,减少了浪费.
科学领域:
- 农业科学 农业科学
- 频谱学是一种光谱学.
- 人工智能的人工智能
背景情况:
- 蛋的强度对于蛋的质量和消费者满意度至关重要.
- 传统的蛋强度测试具有破坏性,速度缓慢,并且对于大规模使用来说不切实际.
- 开发非破坏性方法对于蛋行业至关重要.
研究的目的:
- 评估近红外 (NIR) 光谱与可解释的人工智能 (AI) 结合用于非破坏性的蛋强度预测.
- 探索多变量分析技术,以提高预测准确度.
- 评估人工智能模型对实际应用的可解释性.
主要方法:
- 近红外 (NIR) 光谱法被用来收集来自蛋的光谱数据.
- 应用了各种多变量分析技术,包括主要组件分析 (PCA) 和部分最小平方差分分析 (PLSDA).
- 开发和验证了诸如随机森林 (RF) 和梯度增强等回归模型.
- 为了模型的解释性,采用了沙普利增量解释法 (SHAP).
主要成果:
- 通过NIR光谱学有效地根据使用PCA和PLSDA的贝强度对蛋进行分类.
- 随机森林 (RF) 模型使用仅14个光谱变量实现了高预测准确性 (Rp2 = 0.83).
- 该模型显示了低预测误差 (RMSEP = 1.49 N) 和良好的预测偏差比率 (RPD = 2.44).
- SHAP分析提供了对影响蛋强度预测的关键光谱变量的见解.
结论:
- 与可解释的人工智能集成的NIR光谱学提供了一个强大的,非破坏性的方法来预测蛋强度.
- 这种创新方法是环境可持续的,适合工业质量控制.
- 这些发现支持优化资源使用和提高卵子行业的质量保证.
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