在食品安全相关的歧视性任务中对光谱分析数据集的分类性分析
Yinsheng Zhang1, Xudong Yang2, Zhengyong Zhang3
1Zhejiang Food and Drug Quality & Safety Engineering Research Institute, Zhejiang Gongshang University, Hangzhou 310018, China.
Journal of food protection
|November 15, 2024
概括
本研究引入了一个新的框架来评估食品数据的分类性,指导食品安全机器学习模型的选择. 它帮助研究人员选择适当的模型和工具,以便更好地进行食物歧视.
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
- 机器学习应用 机器学习应用
背景情况:
- 歧视性任务 (食品识别) 对于食品安全至关重要.
- 用机器学习进行光谱分析是很受欢迎的,但模型选择是具有挑战性的.
- 目前的方法依赖于"试错"来进行模型选择.
研究的目的:
- 提出一个专门的两步分类分析框架.
- 为应对与食品相关的歧视性任务选择适当分类模型的挑战.
- 引导研究人员确定模型的复杂性和评估仪器的充分性.
主要方法:
- 开发了一个两步框架,用于数据集的分类分析.
- 步骤1:收集了超过90个指标来衡量数据集可分离性.
- 步骤2:利用基于meta-learner和分解的策略,将指标合成为定量得分.
主要成果:
- 使用两个拉曼光谱分析案例研究验证了框架.
- 对于易于分离的数据集 (例如,液体,~1.0) 实现了更高的分类分数.
- 对于更具挑战性的数据集 (例如食盐,<0.5) 获得较低的分数.
结论:
- 拟议的分类分析框架有效地指导了对食品差异化任务的模型选择.
- 定量得分有助于评估数据的分离性,并告知仪器的选择.
- 该框架预计将广泛应用于食品行业的各种机器学习应用.
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