Authentication of PDO saffron of L'Aquila (Crocus sativus L.) by HPLC-DAD coupled with a discriminant multi-way approach

Dissecting the binding effect of Crocetin glucosyltransferase 2 in crocetin biotransformation in saffron (Crocus sativus L.) from different origins

Crocus sativus L. is a both medicinal and food bulbous flower whose qualities are geographically characterized. However, identification involving different places of origin of such substances is currently limited to single-omics mediated content analysis. Integrated metabolomics and proteomics, 840 saffron samples from six countries (Spain, Greece, Iran, China, Japan, and India) were analyzed using the QuEChERS extraction method. A total of 77 differential metabolites and 14 differential proteins were identified. The limits of detection of the method were 1.33 to 8.33 μg kg-1, and the recoveries were 85.56% to 105.18%. Using homology modeling and molecular docking, the Gln84, Lys195, Val182 and Pro184 sites of Crocetin glucosyltransferase 2 were found to be the targets of crocetin binding. By multivariate statistical analysis (PCA and PLS-DA), different saffron samples were clearly distinguished. The results provided the basis for the selection and identification of high quality saffron from different producing areas.

An overview of analytical methods employed for quality assessment of Crocus sativus (saffron)

This paper reviews qualitative and quantitative analytical methodologies used for the appraisal of saffron quality, as the most expensive spice. Due to the chemical diversity of biologically active compounds of the Crocus genus, analytical methods with different features are required for their complete analysis. However, screening of the main components, such as carotenoids and flavonoids, appears to be sufficient for quality control, a more precise examination needs evaluation of minor compounds, including anthocyanins and fatty acids. High-performance liquid chromatography (HPLC), gas chromatography-mass spectrometry (GC-MS), ultraviolet-visible spectroscopy (UV), nuclear magnetic resonance spectroscopy (NMR), and thin-layer chromatography (TLC), are elementary and applicable methods in quality control analysis, whereas HPLC provides metabolite fingerprint and monitoring multi-compound instances at preparative and analytical levels. Combination approaches like metabolomics using different methods could classify saffron types, identify its adulterations, contaminants and provide a comprehensive metabolite map for quality control of selected compounds.

Geographical Classification of Saffron (Crocus Sativus L.) Using Total and Synchronous Fluorescence Combined with Chemometric Approaches

There is an increasing interest in food science for high-quality natural products with a distinct geographical origin, such as saffron. In this work, the excitation-emission matrix (EEM) and synchronous fluorescence were used for the first time to geographically discriminate between Moroccan saffron from Taroudant, Ouarzazate, and Azilal. Moreover, to differentiate between Afghan, Iranian, and Moroccan saffron, a unique fingerprint was assigned to each sample by visualizing the EEM physiognomy. Moreover, principal component analysis (LDA) and linear discriminant analysis (LDA) were successfully applied to classify the synchronous spectra of samples. High fluorescence intensities were registered for Ouarzazate and Taroudant saffron. Yet, the Azilal saffron was distinguished by its low intensities. Furthermore, Moroccan, Afghan, and Iranian saffron were correctly assigned to their origins using PCA and LDA for different offsets (Δλ) (20-250 nm) such that the difference in the fluorescence composition of the three countries' saffron was registered in the following excitation/emission ranges: 250-325 nm/300-480 nm and 360-425 nm/500-550 nm. These regions are characterized by the high polyphenolic content of Moroccan saffron and the important composition of Afghan saffron, including vitamins and terpenoids. However, weak intensities of these compounds were found in Iranian saffron. Furthermore, a substantial explained variance (97-100% for PC₁ and PC₂) and an important classification rate (70-90%) were achieved. Thus, the non-destructive applied methodology of discrimination was rapid, straightforward, reliable, and accurate.

Detection of Saffron’s Main Bioactive Compounds and Their Relationship with Commercial Quality

This review aims to evaluate the state of saffron’s main bioactive compounds and their relationship with its commercial quality. Saffron is the commercial name for the dried red stigmas of the Crocus sativus L. flower. It owes its sensory and functional properties mainly to the presence of its carotenoid derivatives, synthesized throughout flowering and also during the whole production process. These compounds include crocin, crocetin, picrocrocin, and safranal, which are bioactive metabolites. Saffron’s commercial value is determined according to the ISO/TS3632 standard that determines their main apocatotenoids. Other techniques such as chromatography (gas and liquid) are used to detect the apocarotenoids. This, together with the determination of spectral fingerprinting or chemo typing are essential for saffron identification. The determination of the specific chemical markers coupled with chemometric methods favors the discrimination of adulterated samples, possible plants, or adulterating compounds and even the concentrations at which these are obtained. Chemical characterization and concentration of various compounds could be affected by saffron’s geographical origin and harvest/postharvest characteristics. The large number of chemical compounds found in the by-products (flower parts) of saffron (catechin, quercetin, delphinidin, etc.) make it an interesting aromatic spice as a colorant, antioxidant, and source of phytochemicals, which can also bring additional economic value to the most expensive aromatic species in the world.

Detection and Quantitation of Adulterated Paprika Samples Using Second-Order HPLC-FLD Fingerprints and Chemometrics

Paprika is a widely consumed spice in the world and its authentication has gained interest considering the increase in adulteration cases in recent years. In this study, second-order fingerprints acquired by liquid chromatography with fluorescence detection (HPLC-FLD) were first used to detect and quantify adulteration levels of Chinese paprika samples. Six different adulteration cases, involving paprika production region, cultivar, or both, were investigated by pairs. Two strategies were employed to reduce the data matrices: (1) chromatographic fingerprints collected at specific wavelengths and (2) fusion of the mean data profiles in both spectral and time dimensions. Afterward, the fingerprint data with different data orders were analyzed using partial least squares (PLS) and n-way partial least squares (N-PLS) regression models, respectively. For most adulteration cases, N-PLS based on second-order fingerprints provided the overall best quantitation results with cross-validation and prediction errors lower than 2.27% and 20.28%, respectively, for external validation sets with 15-85% adulteration levels. To conclude, second-order HPLC-FLD fingerprints coupled with chemometrics can be a promising screening technique to assess paprika quality and authenticity in the control and prevention of food frauds.

Multiway resolution of spectrochromatographic measurements for the quantification of echinuline in marine-derived fungi Aspergillus chevalieri using parallel factor analysis

A multiway resolution of incomplete chromatographic separation was presented for spectrochromatographic quantification of echinuline in marine-derived fungi Aspergillus chevalieri. Two-dimensional spectrochromatographic maps of calibration, validation and real samples were recorded as a function of time and wavelength using UPLC-PDA instrument under non-optimized chromatographic conditions, which gave rise to co-elution of echinuline and the constituents of sample matrix. A three-way array was obtained by concatenating the data matrices of the spectrochromatographic maps. Then, parallel factor analysis was applied to the multiway array to extract the individual contribution of echinuline in three modes (time, wavelength and sample). While time and wavelength profiles were used for the characterization of echinuline, the sample profile was used for its quantitative determination of the analyte in validation set and in real samples. Validity of the analytical method was evaluated by analyzing the validation set, which consist of test samples, standard addition samples, intra-day and inter-day samples. The proposed multiway analysis method was then applied to marine-derived fungi extracts and echinuline content was found to be 31.9 µg/g based on the average of ten assay results. The assay results provided by PARAFAC model were statistically compared with those obtained by a newly developed classical UPLC method, which ensured the complete separation of echinuline in a run time of nine minutes. The assay results were found to be comparable due to the fact that there was no significant difference between the analysis results (F = 1.63, Fcrit = 3.17; t = 0.69, tcrit = 2.11) at the significance level of 95%). Consequently, the PARAFAC method permitted the accurate determination of echinuline in fungal extracts despite the partial chromatographic separation with a run time of only three minutes.

Geographical Classification of Italian Saffron (Crocus sativus L.) by Multi-Block Treatments of UV-Vis and IR Spectroscopic Data

One-hundred and fourteen samples of saffron harvested in four different Italian areas (three in Central Italy and one in the South) were investigated by IR and UV-Vis spectroscopies. Two different multi-block strategies, Sequential and Orthogonalized Partial Least Squares Linear Discriminant Analysis (SO-PLS-LDA) and Sequential and Orthogonalized Covariance Selection Linear Discriminant Analysis (SO-CovSel-LDA), were used to simultaneously handle the two data blocks and classify samples according to their geographical origin. Both multi-block approaches provided very satisfying results. Each model was investigated in order to understand which spectral variables contribute the most to the discrimination of samples, i.e., to the characterization of saffron harvested in the four different areas. The most accurate solution was provided by SO-PLS-LDA, which only misclassified three test samples over 31 (in external validation).