Pattern recognition analysis of chromatographic fingerprints of Crocus sativus L. secondary metabolites towards source identification and quality control

Abstract

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.

Egg product freshness evaluation: A metabolomic approach

“…In consideration of the ageing process, chances in the volatile profile of egg products can be expected. Therefore, among possible analytical techniques, gas chromatography–mass spectrometry could be used for the identification of these changes, in agreement with previous applications in the field of food frauds, ie, tomato cultivars discrimination, honey authenticity, or geographical origin of saffron …”

“…Therefore, among possible analytical techniques, gas chromatography-mass spectrometry could be used for the identification of these changes, in agreement with previous applications in the field of food frauds, ie, tomato cultivars discrimination, 10 honey authenticity, 11 or geographical origin of saffron. 12 However, the nonvolatile fraction of food may be of great interest for the discrimination of ageing.…”

Development and Validation of a Rapid and Reliable RP-HPLC-PDA Method for the Quantification of Six Diterpenes in Copaifera duckei, Copaifera reticulata and Copaifera multijuga Oleoresins

“…[37][38][39] Additionally, the World Health Organization (WHO) and several international regulatory agencies, such as the European Medicines Agency (EMEA), the State Food and Drug Administration of China (SFDA), and the Food and Drug Administration (FDA) of the United States of America have accepted the analysis of chemical markers for the evaluation of quality of medicinal plants, as well as their respective preparations. 37,38 Aiming to obtain a simple chemical profile focusing the diterpenes present in the oleoresins of C. duckei, C. reticulata and C. multijuga, it was firstly performed the hydrodistillation of OCd, OCr and OCm using a Clevenger apparatus to separate the volatile and non-volatile fractions.…”

Application of mass spectrometry-based metabolomics approaches for food safety, quality and traceability

“…In any case, to prevent any substantial loss of possible relevant metabolites, minimum sample preparation is preferable. Even though simple solvent-based extraction procedures have been the method of choice during the last years (see Table 2), certain GC-MS methodologies have required the use of other sample preparation techniques such as ultrasound-assisted extraction in tandem with dispersive liquid-liquid microextraction (UAE-DLLME) [98], solid-phase extraction (SPE) [101], static headspace extraction (HS) [108] or headspace solid-phase micro-extraction (HS-SPME) [115], in order to improve the extraction of volatile compounds or to achieve a preconcentration effect, thus, increasing method sensitivity and efficiency.…”

“…Regarding GC-MS, in spite of the need to include a derivatization step in the sample treatment to increase the range of metabolites that can be analyzed, GC-MS metabolomics approaches have been broadly used to evaluate food quality as it can be observed in Table 2. In these cases, GC has been hyphenated to a great variety of mass analyzers including simpler MS instruments, like quadruple (Q) working at electron ionization mode [98,102,103,112,115], or ion trap (IT) [114], as well as high resolution instruments [93,95,105,109,110], and even hybrid analyzers [96,101,107]. An interesting work based on the use of GC coupled to TOF-MS has been employed to develop a non-targeted metabolomics approach capable to establish differences between wine grape cultivars [93].…”