Chromatography provides important detail on the composition of environmental samples and their chemical processing. However, the complexity of these samples and their tendency to contain many structurally and chemically similar compounds frequently results in convoluted or poorly resolved data. Data reduction from raw chromatograms of complex environmental data into integrated peak areas consequently often requires substantial operator interaction. This difficulty has led to a bottleneck in analysis that increases analysis time, decreases data quality, and will worsen as advances in field-based instrumentation multiply the quantity and informational density of data produced. In this work, we develop and validate an automated approach to fitting chromatographic data within a target retention time window with a combination of multiple idealized peaks (Gaussian peaks either with or without an exponential decay component). We compare this single-ion peak fitting approach to drawn baseline integration methods of more than 70,000 peaks collected by field-based chromatographs spanning across a wide range of volatilities and functionalities. Accuracy of peak fitting under real-world conditions is found to be within 10%. The quantitative parameters describing the fit (e.g. coefficients, fit residuals, etc.) are found to provide valuable information to increase the efficiency of quality control and provide constraints to accurately integrate peaks that are significantly convoluted with neighboring peaks. Implementation of the peak fitting method is shown to yield accurate integration of peaks otherwise too poorly resolved to separate into individual compounds and improved quantitative metrics to determine the fidelity of the data reduction process, while substantially decreasing the time spent by operators on data reduction.

色谱提供了有关环境样品的组成及其化学处理的重要细节。但是，这些样品的复杂性及其包含许多结构和化学上相似的化合物的趋势经常导致数据混乱或解析不佳。因此，将数据从复杂的环境数据的原始色谱图还原为积分的峰面积，通常需要操作员进行大量互动。这种困难已导致分析瓶颈，从而增加了分析时间，降低了数据质量，并且随着基于现场的仪器技术的进步使所生成数据的数量和信息密度成倍增加，这一瓶颈将进一步恶化。在这项工作中，我们开发并验证了一种自动方法，该方法可在目标保留时间窗口内结合多个理想化峰（具有或不具有指数衰减分量的高斯峰）来拟合色谱数据。我们将这种单离子峰拟合方法与绘制的基线积分方法进行了比较，该方法通过基于现场的色谱仪收集的超过70,000个峰，涵盖了广泛的挥发性和功能性。发现在实际条件下的峰拟合精度在10％以内。发现描述拟合的定量参数（例如系数，拟合残差等）可提供有价值的信息，以提高质量控制的效率，并提供约束条件，以准确积分与相邻峰显着卷积的峰。