基于机器学习算法和拉曼光谱的乳制品的歧视性特征分析
Jia-Xin Li1, Chun-Chun Qing1, Xiu-Qian Wang2
1School of Management Science and Engineering, Nanjing University of Finance and Economics, Nanjing, Jiangsu, 210023, PR China.
Current research in food science
|June 28, 2024
概括
拉曼光谱学与机器学习相结合,可以有效地区分食品. 最佳的光谱特征提高了质量控制的准确性和效率,为类似的样本分析提供了宝贵的见解.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 食品质量控制依赖于对类似样本进行准确的分辨分析.
- 拉曼光谱和机器学习为智能食品歧视提供了强大的方法.
- 了解光谱特征模式对于增强歧视技术至关重要.
研究的目的:
- 调查拉曼光谱特征,以区分三个品牌的乳制品.
- 评估支持向量机 (SVM),极端学习机 (ELM) 和卷积神经网络 (CNN) 算法的性能.
- 确定选定的光谱特征间隔对识别精度和计算效率的影响.
主要方法:
- 利用拉曼光谱法从乳制品样本中收集光谱数据.
- 应用机器学习算法,包括SVM,ELM和CNN用于数据分析.
- 进行特征光谱分析,以确定差别的最佳光谱间隔.
- 使用基于特征光谱的欧几里德距离分析了样本分布.
主要成果:
- 在SVM,ELM和CNN算法中,最佳的光谱特征间隔有所不同.
- 所有使用特定光谱范围的测试算法都实现了高识别精度 (100%).
- 与SVM (200s) 和CNN (80s) 相比,ELM显示出更高的计算效率 (<0.3s).
- 对光谱特征间隔的视觉分析提供了对样本分布的见解.
结论:
- 选择适当的光谱特征间隔是平衡识别精度和计算效率的关键.
- 不同的机器学习算法从不同的光谱特征范围中获益,以获得最佳性能.
- 这项研究为分析食品质量控制的差异性研究中的光谱特征提供了战略框架.
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