使用质谱检测处理色谱数据的工具的趋势和创新:系统审查
Jelmir Craveiro de Andrade1, Gislaine Natiele Dos Santos Costa1, Celeste Yara Dos Santos Siqueira1
1Institute of Chemistry (IQ), Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Rio de Janeiro, Rio de Janeiro, Brazil.
计算工具的进步提高了复杂样品的染色学数据处理. 机器学习和深度学习方面的创新提高了质谱分析的峰值检测,对齐和解卷的准确性.
科学领域:
- 分析化学 分析化学
- 计算科学 计算科学
- 数据科学数据科学数据科学
背景情况:
- 染色图数据处理面临挑战,因为复杂的样本和大量数据来自诸如质谱 (MS) 等技术.
- 技术创新对于高效准确地分析这些复杂的数据至关重要.
研究的目的:
- 系统地审查过去六年染色数据处理计算工具的技术创新.
- 识别处理复杂分析数据的算法和软件的进步.
主要方法:
- 按照PRISMA协议进行的系统文献审查.
- 在五个主要的科学数据库 (SciFinder,Scopus,Web of Science,Embase,ScienceDirect) 进行的搜索.
- 根据分析工具的原创性,适用性和创新性,选择了33项研究.
主要成果:
- 在峰值检测,对齐和解卷算法方面取得了重大进展,特别是使用机器学习,深度学习和多变量分辨率.
- 自动化和可扩展工具的出现 (例如,DeepResolution,SeA-M2Net,SLAW) 提高了噪声过,基线校正和化合物识别的准确性.
- 增加开源软件的开发和采用,提高可访问性和互操作性.
结论:
- 最近的进展为染色图数据处理提供了更强大,更容易获得和更适应的解决方案.
- 持续面临的挑战包括需要注释数据和标准化.
- 这些创新扩大了科学和工业领域的分析能力.
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