使用模式识别方法对豆起源进行歧视:拉曼和NIR光谱学的比较研究
Mingming Chen1, Zhigang Quan1, Xinyue Sun1
1College of Food Science, Heilongjiang Bayi Agricultural University, Daqing 163319, China.
Foods (Basel, Switzerland)
|January 11, 2025
概括
近红外 (NIR) 光谱比拉曼光谱更有效地追踪豆的起源. NIR分析取得了更高的歧视率,证实了其在确定来源方面的卓越有效性.
科学领域:
- 农业科学 农业科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 准确确定农产品原产地对于质量控制和打击欺诈至关重要.
- 光谱技术为分析食品提供了非破坏性的方法.
- 区分像豆这样的农作物的地理来源是一个持续的挑战.
研究的目的:
- 为了比较近红外 (NIR) 和拉曼光谱在确定豆的地理来源方面的有效性.
- 建立一个强大的可追溯模型来确定豆的来源.
- 评估每个光谱方法的分辨力.
主要方法:
- 在中国的三个不同地区采集了豆样本.
- 使用拉曼光谱和NIR光谱获得光谱数据.
- 使用主要组件分析 (PCA) 和K-最近邻居 (KNN) 算法开发了一个可追溯性模型.
主要成果:
- 尼尔射线光谱学解释了前三个主要组成部分的累积方差的99.01%,超过了拉曼光谱学6.71%.
- 使用NIR数据对豆来源的歧视率达到了98.67%,比使用拉曼光谱学实现的高出22.67%.
- 在应用到NIR光谱数据时,PCA-KNN模型表现出卓越的性能.
结论:
- 近红外 (NIR) 光谱是一种比拉曼光谱更有效的方法,用于追踪豆的来源.
- 开发的PCA-KNN模型为地理来源歧视提供了可靠的方法.
- NIR光谱为确保农产品的真实性和可追溯性提供了一个有前途的工具.
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