用于快速筛查和定量个人护理产品中有害挥发性杂质的顶空选择离子流管质谱工作流程

Analytica Pub Date : 2024-04-02 DOI:10.3390/analytica5020010
M. Perkins, Colin J. Hastie, V. Langford
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引用次数: 0

摘要

个人护理产品 (PCP) 是供消费者定期使用的,因此确保这些产品的安全性非常重要。最近,苯污染问题在某些个人护理品中凸显出来。本研究采用选择离子流管质谱法(SIFT-MS)对九种护发产品中的苯、1,4-二恶烷和甲醛(均为已知或疑似致癌物质)进行了同步顶空分析,并辅以气相色谱-质谱法(GC-MS)进行定性分析。Headspace-SIFT-MS 方法的开发与标准添加法兼容,而标准添加法对于这些复杂乳液中挥发性杂质的定量是必要的。在三种产品中,苯的定量高于低ng g-1 的定量限 (LOQ);在所有产品中,二恶烷的定量高于亚μg g-1 的定量限;在两种产品中,甲醛的定量高于低μg g-1 的定量限。这项研究促进了 SIFT-MS 方法开发和应用于五氯苯酚常规分析的通用工作流程的发展。与传统的气相色谱-质谱分析相比,对 SIFT-MS 工作流程的评估表明,使用 SIFT-MS 进行定量和筛选分析的吞吐量可提高 8 到 30 倍。
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Headspace-Selected Ion Flow Tube Mass Spectrometry Workflows for Rapid Screening and Quantitation of Hazardous Volatile Impurities in Personal Care Products
Personal care products (PCPs) are intended for regular application by consumers and therefore assuring the safety of these products is very important. Recently, benzene contamination has been highlighted in certain PCPs. The present study applies selected ion flow tube mass spectrometry (SIFT-MS) to a simultaneous headspace analysis of benzene, 1,4-dioxane, and formaldehyde—all known or suspected carcinogens—in nine haircare products with supporting qualitative analysis by gas chromatography–mass spectrometry (GC-MS). Headspace-SIFT-MS method development is compatible with the method of standard additions, which is necessary for the quantitation of volatile impurities in these complex emulsions. Benzene was quantified above the low-ng g−1 limit of quantitation (LOQ) in three products, dioxane above the sub-μg g−1 LOQ in all products, and formaldehyde above the low-μg g−1 LOQ in two products, providing a quantitative analysis at concentrations relevant to consumer safety. This study facilitated the development of generic workflows for SIFT-MS method development and application in routine analysis of PCPs. The assessment of workflows for SIFT-MS compared to a conventional GC-MS analysis suggests that 8- to 30-fold throughput enhancements may be possible for quantitative and screening analysis using SIFT-MS.
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