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使用激光诱导分解光谱技术的新鲜肉类分类,辅助由LightGBM和Optuna

Kaifeng Mo¹, Yun Tang¹, Yining Zhu²

¹Hunan Province Key Laboratory of Intelligent Sensors and Advanced Sensor Materials, School of Physics and Electronics Science, Hunan University of Science and Technology, Xiangtan 411201, China.

Foods (Basel, Switzerland)

|July 13, 2024

概括

本研究引入了一种使用激光诱导分解光谱 (LIBS) 识别新鲜肉类型的新方法. 与支持向量机器相比,使用Optuna优化的LightGBM模型显著提高了分类准确性.

科学领域:

分析化学分析化学
频谱学是一种光谱学.
机器学习机器学习

背景情况:

准确识别新鲜肉品种对于食品安全和质量控制至关重要.
传统的肉类分类方法可能是耗时和劳动密集的.
激光诱导分解光谱 (LIBS) 为元素和分子分析提供了一种快速,非破坏性的方法.

研究的目的:

开发和验证一种高精度的方法来使用LIBS.来分类新鲜肉品种 (猪肉,牛肉,肉).
为了比较LightGBM模型与支持矢量机 (SVM) 的性能,用于LIBS光谱数据分析.
使用Optuna算法优化LightGBM模型的超参数,以提高分类准确度.

主要方法:

新鲜肉组织样本 (猪肉,牛肉,肉) 在表面平整后使用LIBS进行分析.
从肉表面生成的血中收集了光谱数据 (900个光谱).
使用信息获取和峰值提取算法进行了特征选择.
使用Optuna优化了LightGBM模型,使用10倍交叉验证优化了SVM模型.

主要成果:

优化的LightGBM模型实现了0.9370的分类精度,0.9364的宏F1和0.9244.4的科恩卡帕系数.
该SVM模型实现了较低的性能指标:0.8888准确度,0.8881宏F1和0.8666卡帕系数.
轻GBM模型在区分新鲜肉品种方面表现出卓越的性能.

结论:

LIBS和优化的LightGBM模型的结合方法为快速新鲜肉类分类提供了一种新有效的方法.
这种技术有可能显著提高肉类检查过程的效率和准确性.
进一步的研究可以探索这种方法对更广泛的肉制品和潜在污染物的应用.

关键词:

轻GBMM 轻GBM 轻GBM 轻GBM 激光诱导的分解光谱学肉类的分类肉类的分类我们选择了Optuna.

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