固相微萃取纤维检测挥发性有机化合物的选择性比较

Mark D. Woollam, Paul Grocki, Paula Angarita Rivera, Amanda P. Siegel, F. Deiss, Mangilal Agarwal
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引用次数: 1

摘要

挥发性有机化合物(VOCs)已被证明是疾病的生物标志物,通常通过气相色谱-质谱(GC-MS)或气体传感器阵列(电子鼻,电子鼻)进行鉴定。GC-MS具有解析VOC结构的优点,而电子鼻易于使用并提供护理点应用。目前的电子鼻设计利用传感器阵列来吸附多种挥发性有机化合物,其中一些是生物标志物。由于传感器元件没有针对生物标志物进行调整,这些设备必须依靠机器学习来识别疾病。因此,调整感应层的选择性来检测特定的生物标志物可能会提高诊断的准确性。固相微萃取(SPME)与气相色谱-质谱联用可能是一种快速、简便的评价不同传感层的方法。为了证明这一概念,制造了不同成分的SPME纤维(定制纤维),并与市售的聚丙烯酸酯(PAA)纤维进行了比较。采用傅里叶变换红外光谱和扫描电镜对涂层进行了表征。使用定制、PAA和未涂覆(阴性对照)SPME纤维提取标准VOC混合物,并通过GC-MS进行分析。PAA和定制纤维显著优于阴性对照,但PAA纤维更敏感。然而,定制纤维对某些挥发性有机化合物的选择性几乎是其两倍,而PAA纤维对其他挥发性有机化合物的选择性几乎是其两倍。结果表明,SPME气相色谱-质谱是一种有效的检测/比较传感层对多种VOCs的选择性的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparing the Selectivity of Solid Phase Microextraction Fibers to Detect Volatile Organic Compounds
Volatile organic compounds (VOCs) have been shown to be biomarkers of disease and are typically identified by gas chromatography-mass spectrometry (GC-MS) or gas sensor arrays (electronic nose, e-nose). GC-MS has the advantages of VOC structure elucidation, while the e-nose is easy to use and offers point of care applications. Current e-nose designs utilize a sensor array to adsorb a diverse set of VOCs, some of which are biomarkers. Because the sensor elements are not tuned for biomarkers, these devices must rely on machine learning to identify disease. Therefore, tuning the selectivity of the sensing layers to detect specific biomarkers may increase diagnostic accuracy. Solid phase microextraction (SPME) coupled to GC-MS may be a rapid and facile process to evaluate different sensing layers. To demonstrate this concept, SPME fibers of different compositions were fabricated (custom fibers) and compared to commercially available polyacrylate (PAA) fibers. Coatings were characterized by Fourier Transform Infrared spectroscopy and scanning electron microscopy. Custom, PAA and uncoated (negative controls) SPME fibers were utilized to extract a standard VOC mixture which was analyzed by GC-MS. PAA and custom fibers significantly outperformed the negative control, but PAA fibers were more sensitive. However, the custom fiber was nearly twice as selective to some VOCs and the PAA fiber was nearly twice as selective toward other VOCs. The results show that SPME GC-MS is an efficient method for testing/comparing the selectivity of sensing layers toward multiple VOCs in a single analytical run.
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