In the present contribution, a novel non-targeted volatilomic study based on headspace GC-IMS (HS-GC-IMS) was developed for the authentication and geographical origin discrimination of saffron. In this regard, multivariate curve resolution-alternating least squares (MCR-ALS) was employed to recover the pure GC elution and IMS profiles of saffron metabolites. Iranian saffron samples from seven important areas were analyzed by HS-GC-IMS. The resulting second-order GC-IMS datasets were organized in a augmented matrix and processed using MCR-ALS with various constraints. The MCR-ALS resolved GC profiles were analyzed by different pattern recognition techniques; principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and data driven-soft independent modeling of class analogy (DD-SIMCA). The saffron samples were assigned to their seven geographical origins with an accuracy of 89.0 %. Additionally, four adulterants (style, safflower, madder and calendula) were reliably detected with over 94.0 % accuracy. In this context, GC-IMS substantially outperformed the commonly used FT-NIR spectroscopy approach.

中文翻译：

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非靶向挥发组学，用于通过气相色谱-离子淌度光谱和多变量曲线分辨率鉴定藏红花


在本贡献中，开发了一种基于顶空 GC-IMS （HS-GC-IMS） 的新型非靶向挥发性研究，用于藏红花的鉴定和地理来源区分。在这方面，采用多变量曲线分辨率交替最小二乘法 （MCR-ALS） 来回收藏红花代谢物的纯 GC 洗脱和 IMS 曲线。HS-GC-IMS 分析了来自 7 个重要地区的伊朗藏红花样品。生成的二阶 GC-IMS 数据集被组织在一个增强矩阵中，并使用具有各种约束的 MCR-ALS 进行处理。通过不同的模式识别技术分析 MCR-ALS 分辨的 GC 曲线;主成分分析 （PCA）、偏最小二乘判别分析 （PLS-DA） 和数据驱动的类比软独立建模 （DD-SIMCA）。藏红花样品被分配到其七个地理原产地，准确率为 89.0 %。此外，四种掺杂物（掺杂物、红花、茜草和金盏花）被可靠地检测到，准确率超过 94.0%。在这种情况下，GC-IMS 的性能大大优于常用的 FT-NIR 光谱方法。