de la Mata A Paulina, McQueen Rachel H, Nam Seo Lin, Harynuk James J
Department of Chemistry, University of Alberta, 11227 Saskatchewan Drive NW, Edmonton, AB, T6G 2G2, Canada.
Department of Human Ecology, University of Alberta, 302 Human Ecology Building, Edmonton, AB, T6G 2N1, Canada.
Anal Bioanal Chem. 2017 Mar;409(7):1905-1913. doi: 10.1007/s00216-016-0137-1. Epub 2016 Dec 27.
Human axillary sweat is a poorly explored biofluid within the context of metabolomics when compared to other fluids such as blood and urine. In this paper, we explore the volatile organic compounds emitted from two different types of fabric samples (cotton and polyester) which had been worn repeatedly during exercise by participants. Headspace solid-phase microextraction (SPME) and comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) were employed to profile the (semi)volatile compounds on the fabric. Principal component analysis models were applied to the data to aid in visualizing differences between types of fabrics, wash treatment, and the gender of the subject who had worn the fabric. Statistical tools included with commercial chromatography software (ChromaTOF) and a simple Fisher ratio threshold-based feature selection for model optimization are compared with a custom-written algorithm that uses cluster resolution as an objective function to maximize in a hybrid backward-elimination forward-selection approach for optimizing the chemometric models in an effort to identify some compounds that correlate to differences between fabric types. The custom algorithm is shown to generate better models than the simple Fisher ratio approach. Graphical Abstract A route from samples and questions to data and then answers.
与血液和尿液等其他体液相比，人体腋窝汗液在代谢组学背景下是一种研究较少的生物流体。在本文中，我们探究了参与者在运动期间反复穿着的两种不同类型织物样本（棉和聚酯）所释放的挥发性有机化合物。采用顶空固相微萃取（SPME）和全二维气相色谱 - 飞行时间质谱联用仪（GC×GC - TOFMS）对织物上的（半）挥发性化合物进行分析。主成分分析模型应用于数据，以帮助直观呈现不同类型织物、洗涤处理以及穿着该织物的受试者性别的差异。将商业色谱软件（ChromaTOF）附带的统计工具以及基于简单费舍尔比率阈值的特征选择用于模型优化，与一种自定义编写的算法进行比较，该自定义算法使用聚类分辨率作为目标函数，以混合反向消除 - 正向选择方法最大化，用于优化化学计量模型，旨在识别一些与织物类型差异相关的化合物。结果表明，自定义算法生成的模型比简单费舍尔比率方法更好。图形摘要 从样本和问题到数据再到答案的路径。
