基于共聚焦显微镜拉曼光谱技术在黑龙江省的日本大米的原产地歧视模型的构建
Guifang Zhang1, Jinming Liu2, Zhiming Li3
1National Coarse Cereal Engineering Technology Research Center, Heilongjiang Bayi Agricultural University, Daqing, 163319, Heilongjiang, People's Republic of China.
Scientific reports
|February 18, 2025
概括
这项研究使用拉曼光谱和化学测量开发了一种使用大米原产地歧视模型. 最好的模型在识别来自不同黑龙江生产地区的米的准确率达到了86.67%.
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:
- 米 (Oryza sativa) 的原产地认证对于食品安全和质量控制至关重要.
- 地理原产地对大米的质量和特征有重大影响.
- 区分来自不同地区的米品种需要强大的分析方法.
研究的目的:
- 开发和验证来自黑龙江省五个生产地区的的准确原产地歧视模型.
- 为了优化样品处理,仪器参数和用于拉曼光谱的光谱数据采集.
- 确定最有效的化学测量方法来进行特征选择和分类.
主要方法:
- 孔焦拉曼光谱法应用于来自五个不同的生产区域的150个磨米样本.
- 拉曼光谱 (100-3200厘米−1) 通过正常化,萨维茨基-戈莱平滑和多变量散射校正进行了预处理.
- 特性波长的选择是使用竞争性自适应重量化采样 (CARS) 和离散二进制粒子群优化 (BPSO) 进行的.
- 构建了部分最小平方差分分析 (PLS-DA),支持向量机 (SVM) 和极端学习机 (ELM) 模型.
主要成果:
- 建立了最佳的样本处理和光谱采集技术.
- 确定了有效的光谱预处理方法.
- 汽车和BPSO算法分别选择了226个和1899个特征波长.
- BPSO-GA-SVM模型展示了最高的预测性能,测试集准确度为86.67%.
结论:
- 混焦拉曼光谱与化学测量相结合,为米原产地歧视提供了一种有效的方法.
- 根据BPSO-GA-SVM模型,在验证大米的地理来源方面有很大的潜力.
- 这种方法可以帮助确保大米产品的真实性和可追溯性.
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