1H核磁共振光谱学与机器学习算法结合,用于原产地识别中国著名的绿茶朗庆茶茶
Zhiwei Hou1, Yugu Jin1, Zhe Gu1
1College of Tea Science and Tea Culture, Zhejiang A & F University, 666 Wusu Street, Hangzhou 311300, China.
Foods (Basel, Switzerland)
|September 14, 2024
概括
核磁共振 (NMR) 和机器学习准确地确定了优质龙茶 (LJT) 的来源. 这种方法有助于打击食品欺诈,通过区分真实的LJT和受保护的原产地名称地区.
科学领域:
- 分析化学 分析化学
- 农业科学 农业科学
- 食品科学 食品科学 食品科学
背景情况:
- 优质的绿茶,如龙茶 (LJT),是一种高价值产品.
- 地理来源显著影响LJT的质量和真实性.
- 食品欺诈是令人担忧的,因为原产地对LJT价值的影响.
研究的目的:
- 通过使用化学指纹,根据地理来源区分龙茶 (LJT).
- 评估机器学习算法对LJT来源识别的有效性.
- 开发一种可靠的质量评估方法,并在LJT中打击食品欺诈.
主要方法:
- 核磁共振 (NMR) 光谱法用于对78个LJT样品进行化学指纹分析.
- 无监督的算法和热图被用于数据可视化.
- 线性 (LDA) 和非线性 (随机森林) 机器学习模型在原产地歧视方面进行了比较.
主要成果:
- 非线性随机森林模型达到92.2%的准确性,超过线性差异分析模型 (85.6%).
- 关键标记代谢物,包括凯姆菲罗尔糖化物,谷氨酸和表甲基酸盐 (ECG),被确定为地理来源.
- 证明了LJT受保护原产地名称 (PDO) 和非PDO地区之间的成功歧视.
结论:
- 融合NMR光谱和机器学习为LJT质量评估提供了有效的方法.
- 这种方法作为一个强大的工具来验证龙茶的地理来源.
- 这些发现支持使用先进的分析技术来确保食品完整性和防止欺诈.
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