使用SHAP值的机器学习模型可解释性:应用于分类和预测不同羊肉切片的营养含量任务
Li Wang1, Xuchun Sun2, Jing Liang3
1State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, PR China.
Food chemistry: X
|July 21, 2025
概括
本研究使用Vis-NIR光谱与支持矢量机 (SVM) 模型快速分类新鲜羊肉切片并预测营养含量,包括脂肪酸. 该方法可以准确地识别绵羊肉的质量和成分,而不是破坏性.
科学领域:
- 食品科学与技术 食品科学与技术
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 准确的新鲜肉的营养概况是至关重要的,但具有挑战性.
- 脂肪酸的成分在不同的羊肉切片中有很大差异.
- 为了快速评估肉类,需要使用非破坏性分析技术.
研究的目的:
- 使用光谱数据对新鲜的羊肉切片 (长腰,后腿,前腿) 进行分类.
- 预测关键的营养成分,包括原油脂肪,蛋白质和脂肪酸 (MUFA,PUFA).
- 开发和验证一种快速,非破坏性的羊肉质量评估方法.
主要方法:
- 利用可见和近红外 (Vis-NIR) 谱学来获取光谱数据.
- 员工支持矢量机 (SVM) 用于分类和预测任务.
- 嵌入式SHAP (夏普利添加式解释) 用于模型解释性和特征分析.
主要成果:
- 在不同羊肉切片的分类准确度达到了92.5%.
- 成功预测的营养参数 (EE,CP,MUFA,PUFA) 与 RPD> 2.7.7 的情况.
- 在切片中发现了多不和脂肪酸 (PUFA) 含量的显著差异,后腿是最高的.
- SHAP分析强调了与脂质相关的变量和NIR波长 (2300-2500nm) 作为关键预测因素.
结论:
- 基于Vis-NIR的SVM建模为新鲜羊肉评估提供了快速,非破坏性和准确的方法.
- 开发的模型可以有效地区分羊肉切片,并预测营养成分.
- 这项技术在肉类行业具有质量控制和可追溯性的潜力.
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