Aliakbarzadeh Ghazaleh, Sereshti Hassan, Parastar Hadi
Department of Chemistry, Faculty of Science, University of Tehran, P.O.Box 14155-64555, Tehran, Iran.
Department of Chemistry, Sharif University of Technology, P.O. Box 11155-3516, Tehran, Iran.
Anal Bioanal Chem. 2016 May;408(12):3295-307. doi: 10.1007/s00216-016-9400-8. Epub 2016 Feb 27.
Chromatographic fingerprinting is an effective methodology for authentication and quality control of herbal products. In the presented study, a chemometric strategy based on multivariate curve resolution-alternating least squares (MCR-ALS) and multivariate pattern recognition methods was used to establish a gas chromatography-mass spectrometry (GC-MS) fingerprint of saffron. For this purpose, the volatile metabolites of 17 Iranian saffron samples, collected from different geographical regions, were determined using the combined method of ultrasound-assisted solvent extraction (UASE) and dispersive liquid-liquid microextraction (DLLME), coupled with GC-MS. The resolved elution profiles and the related mass spectra obtained by an extended MCR-ALS algorithm were then used to estimate the relative concentrations and to identify the saffron volatile metabolites, respectively. Consequently, 77 compounds with high reversed match factors (RMFs > 850) were successfully determined. The relative concentrations of these compounds were used to generate a new data set which was analyzed by multivariate data analysis methods including principal component analysis (PCA) and k-means. Accordingly, the saffron samples were categorized into five classes using these techniques. The results revealed that 11 compounds, as biomarkers of saffron, contributed to the class discrimination and characterization. Eleven biomarkers including nine secondary metabolites of saffron (safranal, α- and β-isophorone, phenylethyl alcohol, ketoisophorone, 2,2,6-trimethyl-1,4-cyclohexanedione, 2,6,6-trimethyl-4-oxo-2-cyclohexen-1-carbaldehyde, 2,4,4-trimethyl-3-carboxaldehyde-5-hydroxy-2,5-cyclohexadien-1-one, and 2,6,6-trimethyl-4-hydroxy-1-cyclohexene-1-carboxaldehyde (HTCC)), a primary metabolite (linoleic acid), and a long chain fatty alcohol (nanocosanol) were distinguished as the saffron fingerprint. Finally, the individual contribution of each biomarker to the classes was determined by the counter propagation artificial neural network (CPANN) method.
色谱指纹图谱是一种用于草药产品鉴定和质量控制的有效方法。在本研究中，采用基于多元曲线分辨-交替最小二乘法（MCR-ALS）和多元模式识别方法的化学计量学策略，建立藏红花的气相色谱-质谱（GC-MS）指纹图谱。为此，使用超声辅助溶剂萃取（UASE）和分散液液微萃取（DLLME）相结合的方法，并结合GC-MS，测定了从不同地理区域采集的17个伊朗藏红花样品的挥发性代谢物。然后，利用扩展MCR-ALS算法得到的解析洗脱图谱和相关质谱，分别估算相对浓度并鉴定藏红花挥发性代谢物。结果成功测定了77种具有高反向匹配因子（RMFs＞850）的化合物。这些化合物的相对浓度用于生成一个新数据集，并通过主成分分析（PCA）和k均值等多元数据分析方法进行分析。据此，利用这些技术将藏红花样品分为五类。结果表明，有11种化合物作为藏红花的生物标志物，有助于类别区分和特征描述。11种生物标志物包括9种藏红花次生代谢物（藏红花醛、α-和β-异佛尔酮、苯乙醇、酮异佛尔酮、2,2,6-三甲基-1,4-环己二酮、2,6,6-三甲基-4-氧代-2-环己烯-1-甲醛、2,4,4-三甲基-3-羧醛-5-羟基-2,5-环己二烯-1-酮、2,6,6-三甲基-4-羟基-1-环己烯-1-羧酸醛（HTCC））、1种初级代谢物（亚油酸）和1种长链脂肪醇（正二十九烷醇），被确定为藏红花指纹图谱。最后，通过反向传播人工神经网络（CPANN）方法确定了每种生物标志物对各类别的个体贡献。
