采用机器学习和增强的中级数据融合技术对Chenpi的地理原产地歧视
Xin Kang Li1,2, Li Jun Tang1, Ze Ying Li1
1School of Pharmacy and Food Engineering, Wuyi University, Jiangmen, 529020, PR China.
NPJ science of food
|February 5, 2025
概括
这项研究使用气体染色学和中红外光谱学与机器学习来识别Chenpi (干燥的果皮) 的地理来源. 数据融合技术显著提高了这个有价值的传统成分的原产地歧视的准确性.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 计算化学计算化学
背景情况:
- 陈皮,或干燥的果皮,是一种传统的中国食材,具有公认的药用和价值.
- 陈皮的地理来源对其质量,活性化合物和市场价值有重大影响.
- 准确识别陈皮的原产地对于质量控制和认证至关重要.
研究的目的:
- 开发和验证基于其地理来源区分陈皮样本的策略.
- 为了评估机器学习方法与数据融合相结合的 Chenpi 起源分类的有效性.
主要方法:
- 分析了来自新区八个地区的39个陈皮样本,使用气相色谱 (GC) 和中红外 (MIR) 光谱.
- 应用四种机器学习算法 (K-最近邻居,人工神经网络等) 对于歧视而言.
- 实施两个中级数据融合策略,以整合GC和MIR数据.
主要成果:
- 数据融合策略显著提高了陈皮原产地歧视的准确性.
- 修改的中级数据融合与K-最近的邻居和人工神经网络模型相结合,实现了最高的性能.
- 最好的模型只错误地分类了一个样本,证明了高度的分类准确性.
结论:
- 机器学习方法,特别是当与修改的中级数据融合相结合时,提供了一种有效的方法来根据地理来源对陈皮样本进行分类.
- 这一战略为认证Chenpi的真实性和确保产品质量提供了一个强大的工具.
- 这些发现支持在传统成分认证中使用分析技术和计算方法.
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