SBIR Phase I: Automated Pattern Recognition in Images Produced by Comprehensive Two-Dimensional Gas Chromatography

SBIR 第一阶段：综合二维气相色谱生成的图像中的自动模式识别

• 批准号: 0231746
• 负责人: Stephen Reichenbach
• 金额: $ 10万
• 依托单位: GC Image, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-01-01 至 2003-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0231746&HistoricalAwards=false
• 关键词: SBIR; Phase; Automated; Pattern; Recognition

This Small Business Innovation Research Phase I project initiates rigorous investigation of automated pattern recognition in images produced by comprehensive two-dimensional gas chromatography. Comprehensive two-dimensional gas chromatography (GCxGC) is an emerging technology for chemical separation that provides a multiplicative increase in separation capacity over traditional GC. With this greatly increased performance, GCxGC generates data in significantly larger quantity and with significantly greater complexity. The quantity and complexity of GCxGC data makes human analyses of GCxGC images difficult and time-consuming and motivates the need for automated processing. This Phase I project undertakes both experimental and theoretical investigations into automating the process of matching observed patterns of chemical separations against previously recorded patterns annotated by human experts. The goals are to determine promising statistical models for GCxGC pattern matching and to demonstrate the feasibility of automated recognition. In this Phase I work, important anticipated results include statistical characterization of pattern variations and warping in GCxGC images, a catalog of useful annotations of previously observed pattern templates, and development of a prototype algorithm for automated pattern recognition. Phase I results, characterizing GCxGC patterns, cataloging annotations, and demonstrating the feasibility of automated processing, will provide a foundation for Phase II research aimed at developing commercially sign cant GCxGC methods.This research has high potential impact for a variety of applications. Commercial applications of GC include analyses of petroleum, environmental samples, foods and beverages, fragrances, and toxins (e.g., chemical warfare agents). The availability of software for automated recognition of chemical components from GCxGC images will facilitate adoption of GCxGC technology in laboratories using traditional GC and will contribute to the development of new markets, which require superior separation performance.

这个小型企业创新研究第一阶段项目启动了对全面二维气相色谱法产生的图像中自动模式识别的严格调查。全二维气相色谱（GCxGC）是一种新兴的化学分离技术，其分离能力比传统GC成倍增加。 随着性能的大幅提高，GCxGC生成的数据量明显增加，复杂性也明显提高。 GCxGC数据的数量和复杂性使得GCxGC图像的人工分析变得困难和耗时，并激发了对自动化处理的需求。 这个第一阶段项目进行实验和理论研究，以自动化匹配观察到的化学分离模式与人类专家注释的先前记录的模式的过程。我们的目标是确定有前途的GCxGC模式匹配的统计模型，并证明自动识别的可行性。在第一阶段的工作中，重要的预期结果包括GCxGC图像中的模式变化和翘曲的统计特征，以前观察到的模式模板的有用注释的目录，以及自动模式识别的原型算法的开发。第一阶段的结果，表征GCxGC模式，编目注释，并证明自动化处理的可行性，将提供一个基础，第二阶段的研究，旨在开发商业标志的GCxGC方法。这项研究具有很高的潜在影响的各种应用。 GC的商业应用包括分析石油、环境样品、食品和饮料、香料和毒素（例如，化学战剂）。 从GCxGC图像自动识别化学成分的软件的可用性将促进在使用传统GC的实验室中采用GCxGC技术，并将有助于开发需要上级分离性能的新市场。