质谱数据的分类,以协助识别新型合成大麻素
Kristopher C Evans-Newman1, Garion L Schneider1, Nuwan T Perera1
1Department of Chemistry and Physics, Western Carolina University, Cullowhee, NC 28723, USA.
Molecules (Basel, Switzerland)
|October 16, 2024
概括
这项研究引入了一种质谱法,用于识别新型合成大麻素 (NSC). 经过验证的二元分类系统有助于将这些化合物与其他药物区分开来,并根据结构对其进行分类.
科学领域:
- 法医化学 法医化学
- 分析化学 分析化学
- 药物发现 药物发现 药物发现
背景情况:
- 鉴定新合成的大麻素 (NSC) 由于有限的参考标准和化学数据,很难.
- 现有的分析方法与新型精神活性物质的快速出现作斗争.
研究的目的:
- 开发和验证用于识别未知的新合成大麻素 (NSCs) 的分类系统.
- 为了利用质谱数据和部分最小方形差异分析 (PLS-DA) 进行药物分类.
主要方法:
- 在质谱数据上开发了使用部分最小平方区分分析 (PLS-DA) 的二进制分类模型.
- 创建模型来区分大麻素与其他药物,古典大麻素与合成大麻素,以及特定的功能组 (英多尔,英达,阿扎英多尔,纳夫,FUB,5-F).
- 通过交叉验证和外部验证验证验证的模型预测能力.
主要成果:
- 所有开发的分类模型都显示出高精度.
- 隐性变量 (LVs) 提供了阶级歧视的关键质量到电荷 (m/z) 值.
- 该系统有效地帮助识别未知的药物分子的功能组.
结论:
- 经过验证的PLS-DA模型提供了一种可靠的方法来识别和表征新合成的大麻素 (NSC).
- 这种方法提高了新兴合成大麻素不同类别和结构特征的区分能力.
- 该方法可以根据质谱数据识别未知的药物分子.
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