Abstract：
Chemometric decomposition methods like multivariate curve resolution-alternating least squares (MCR-ALS) are often employed in gas chromatography-mass spectrometry (GC-MS) to improve analyte identification and quantitation. However, these methods can perform poorly for analytes with a low chromatographic resolution (Rs) and a high degree of spectral contamination from noise and background interferences. Thus, we propose a novel computational algorithm, termed mzCompare, to improve analyte identification and quantitation when coupled to MCR-ALS. The mzCompare method utilizes an underlying requirement that the retention time and peak shape between mass channels (m/z) of the same analyte should be similar. By discovering the selective m/z for a given analyte in a chromatogram, a pure elution profile can be generated and used as an equality constraint in MCR-ALS. The performance of the mzCompare methodology is demonstrated with both experimental and simulated chromatograms. Experimentally, unresolved analytes with a Rs as low as 0.05 could be confidently identified with mzCompare assisted MCR-ALS. Furthermore, application of the mzCompare algorithm to a complex aerospace fuel resulted in the discovery of 335 analytes, a 44 % increase compared to conventional peak detection methods. GC-MS simulations of target-interferent analyte pairs demonstrated that the performance of MCR-ALS deteriorated below a Rs of ∼0.25. However, mzCompare assisted MCR-ALS showed excellent identification and acceptable quantitative accuracy at a Rs of ∼0.02. These results show that the mzCompare algorithm can help analysts overcome modeling ambiguities resulting from the chemometric multiplex disadvantage.
摘要：
化学计量分解方法，如多元曲线分辨率交替最小二乘法（MCR-ALS）通常用于气相色谱-质谱（GC-MS）中，以改善分析物的识别和定量。然而,这些方法对于具有低色谱分辨率(Rs)和来自噪声和背景干扰的高度光谱污染的分析物可能表现不佳。因此，我们提出了一种新的计算算法，称为mzCompare，当与MCR-ALS偶联时，可以提高分析物的识别和定量。mzCompare方法利用了以下基本要求:相同分析物的质量通道(m/z)之间的保留时间和峰形状应当相似。通过发现色谱图中给定分析物的选择性m/z，可以生成纯洗脱谱并将其用作MCR-ALS中的相等约束。通过实验和模拟色谱图证明了mzCompare方法的性能。实验上,用mzCompare辅助的MCR-ALS可以可靠地鉴定Rs低至0.05的未解析分析物。此外，将mzCompare算法应用于复杂的航空航天燃料，发现了335种分析物，与传统的峰值检测方法相比增加了44%。目标干扰物分析物对的GC-MS模拟表明，MCR-ALS的性能下降到低于0.25的Rs。然而,mzCompare辅助的MCR-ALS表现出出色的识别能力和可接受的定量准确性，Rs为0.02。这些结果表明，mzCompare算法可以帮助分析师克服化学计量多重缺点导致的建模歧义。
