Saffron Adulteration Detection By High-Resolution Mass Spectrometry

Introduction

Saffron, the most valuable spice in the world, is obtained by drying the stigma of saffron flowers (Crocus sativus L.) [1]. The high price of saffron is due to the traditional collection method, where the stigma must be collected by hand, and the collection yield is minimal [2, 3]. Considering the high economic value and the limited raw material sources, saffron is vulnerable to fraud. One of the most frequent fraudulent practices is dilution by low-priced plant materials such as safflower petals (Carthamus tinctorius), calendula petals (Calendula officinalis), turmeric powder (Curcuma longa), beet fibers (Beta vulgaris), pomegranate fibers (Punica granatum), arnica flowers (Arnica Montana), and achiote seeds (Bixa Orellana) [4–6] with similar color and morphology to saffron [3, 5].

Like other spices, saffron's chemical composition is complex, containing many secondary metabolites responsible for characteristic bioactivity, ties, and overall quality. The significant phytochemicals distinct from saffron are crocetin glycosidic esters associated with a typical golden-yellow tinge of flavored food [1, 5], safra- nal, the critical component of volatiles fraction, and picrocrocin, which is responsible for the characteristic bitter taste of this spice. Picrocrocin, the unique secondary metabolite, is a known molecular marker of saffron [1, 3]. Regarding saffron aroma, isophorone-related compounds (structurally similar to safranal), C13-nor isoprenoids derived from lipophilic carotenoids, and saturated hydrocarbons such as octadecane, docosane, tricosane, and pentacosane are typically represented [5, 7, 8]. Quality and classification of saffron is defined by ISO 3632–2 2010 (microscopic analysis for detection of extraneous material in saffron) and ISO 362–1 2011 (UV–Vis), which is based on measurement of the absorption maxim values of aqueous saffron extracts at 257 nm (detection of picrocrocin), 330 nm (detection of safranal) and 440 nm (detection of crocins)[1, 6]. However, the UV mentioned above–Vis spectrophotometric method has been shown as insufficient when saffron is adulterated by plant material with a similar color and morphology. The study was performed by Sabatino et al. [9] and Demonstrated that the UV–Vis method did not enable the detection of the addition of impurities (safflower, calendula, and turmeric) at a level lower than 20% w/w. Besides the molecular biology-based approach employing polymerase chain reaction (PCR) followed by sequencing [10, 11], several instrumental strategies such as nuclear magnetic resonance (NMR) [2, 12, 13], liquid chromatography coupled with mass spectrometry (LC–MS) [3, 14, 15], infrared spectroscopy (IR) [16–18] were used for saffron authentication. In this study, we aimed to investigate in depth the application potential of headspace sampling of saffron volatiles by solid-phase microextraction extraction followed by gas chromatography coupled to high-resolution mass spectrometry (HS-SPME-GC-HRMS) for authentication purposes. There was only one study employing SPME-GC-HRMS for the detection of plant-derived pollutants [6], and we were wondering whether our approach using chemometric data handling would be able to identify similar markers and which lowest concentration of plant adulterants (safflower, calendula, capsicum, and turmeric) in the admixture could be recognized.

Materials and methods

Samples

In total, 38 authentic saffron (Crocus sativus L.) samples from different origins (Iran, Spain, Greece, and Italy) were obtained for this study in collaboration with the Czech Agriculture and Food Inspection Authority. Also, 25 samples of spices and plant material, which could be used for an adulteration of saffron, were collected for this study. They were Turmeric (Curcuma longa L., n = 6), Calendula (Calendula officinalis L., n = 9), Capsicum (Capsicum annum L., n = 6) and Safflower (Carthamus tinctorius L., n = 4). Some of the samples were supplied as ground material (powder). An acceptable homogenization was required in some samples (mostly saffron obtained as dried stigma). To avoid the loss of volatiles, the homogenization was manually performed in the ceramic grinding bowl. All samples were stored in gas-tight containers in the freezer at -18 °C.

Chemicals and materials

SPME fibers coated with divinylbenzene/carboxy/poly- methylsiloxane (DVB/CAR/PDMS, 50/30 μm, 1 cm) were purchased from Supelco (Bellefonte, USA). Sodium chloride was purchased from Penta (Prague, Czech Republic). Water purified by a Milli-Q® Integral system supplied by Merck was used throughout the study. C7–C30 saturated alkanes mixture (certified reference material, 1000 μg/mL each component in hexane), used for unknown compounds retention index (RI) based identification, was obtained from Sigma-Aldrich (Steinheim, Germany).

Sample preparation

Non‑target screening of volatile profiles of saffron and adulterants

Finely ground samples (50 mg) were placed in SPME crimp cap vials (10 mL), and 2 mL of a saturated solution of NaCl (26.4% w/w) was added. The vials were immediately crimped with a gas-tight SPME cap. Each sample was prepared in one repetition. In addition, samples for background measurement (blanks) were prepared (2 mL of a saturated solution of NaCl), as well as quality control samples (QC). For the QC sample, 50 mg of the pooled samples were used, and the QC sample was prepared in six repetitions. It should be noted that during the development of the SPME-GC- HRMS method, the different amounts of the sample (25 mg, 50 mg, and 100 mg) were considered, as well as the addition of a saturated solution of NaCl. Finally, the combination of 50 mg of the sample with the 2 mL of saturated solution of NaCl was chosen.

Artificially adulterated saffron samples

In this part of the experiment, saffron was artificially adulated and treated by mixing it with selected plant materials in different proportions (percentage by mass – % w/w). Specifically, for the mixtures of saffron with turmeric, paprika, safflower, and calendula, the amounts of saffron/adulterant were as follows: 80/20% w/w, 90/10% w/w, 96/4% w/w, and 98/2% w/w. The artificially adulterated, authentic saffron and pure adulterant samples were prepared as described in Sect. "Non-target screening of volatile profiles of saffron and adulterants." In two repetitions.

SPME‑GC‑HRMS

For the extraction of head-spaced volatiles, the HS-SPME technique was used. The SPME parameters were first optimized to achieve the most efficient extraction of volatiles to the SPME fiber; simultaneously, it was crucial to test different desorption times to ensure the complete desorption of volatiles from the fiber to avoid cross-contamination. For the extraction temperature, 40 °C, 45 °C, and 50 °C were tested. Furthermore, the extraction time was tested at 10 minutes, 15 minutes, and 20 minutes. As for desorption time, 1 min and 3 min were considered. The final parameters of the SPME method are described below. The temperature of incubation and extraction was 45 °C, sample incubation time was 20 min, extraction time was 10 min, and sample desorption to the GC system was 3 min (desorption temperature was 240 °C). The Agilent 7200B system consists of an Agilent 7890B gas chromatograph equipped with a multimode inlet, PAL RSI 85 for automated headspace–solid-phase microextraction (HS–SPME) and direct injection, and quadrupole – time of flight mass spectrometer (Q-TOF) (Agilent Technologies, Palo Alto, California, USA) was employed. Mass-Hunter GC/MS Acquisition (Agilent Technologies, Palo Alto, California, USA) software (B.10.0.384.1) was used for instrument control and data acquisition. Sample components were separated on a 30 m HP–5MS capillary column (0.25 mm id, film thickness: 0.25 µm; Agilent Technologies, Palo Alto, California, USA). Samples were injected using split mode (5:1), the injector temperature was 240 °C, and the oven temperature program was as follows:

40 °C (2 min), 2 °C/min to 64.5 °C, 0.5 °C

min to 70 °C, 2 °C/min to 80 °C, 5 °C/min to 100 °C, 0.5 °C

min to 114 °C, 2 °C/min to 120 °C, 5 °C/min to 184 °C

20 °C/min to 240 °C (5 min)

The mass spectrometric detector was operated in the electron ionization (EI) mode. The temperature of the transfer- line was 280 °C, and the ion source temperature was 230 °C. The mass range setting was 40–550 m/z, the acquisition speed was five specter/s, and the resolution of the mass analyzer was set > 12,500 (FWHM).

GC–MS data processing and chemometric analysis

First, data obtained by SPME-GC-HRMS was transferred to the SureMass format. Then, our SureMass data was subjected to peak detection, deconvolution, and library search (NIST17.L). The data was then converted to CEF files (.CEF). All these data processing steps were performed in MassHunter Unknowns Analysis (Version B.10.1). Based on the converted QC CEF file in MassHunter Quantitative Analysis (Version.B.10.1) was created a data processing method and performed a recursive analysis. All software was from Agilent Technologies, Palo Alto, California, USA. After completing a recursive analysis, data were exported to the MetaboAnalyst 5.0 software (https://www.metab oanalyst.ca). Employing this software, the following data treatment operations were performed: sum-normalization (relative intensities of each signal obtained by division of the sum of all signals), log transformation, and Pareto scaling. These data processing operations ensure low abundant compounds' normal distribution and higher significance. In addition, in MetaboAnalyst, principal component analysis (PCA) was performed to oversee exported data. Analysis of Variance (ANOVA) or t-test (False Discovery Rate (FDR), p-value < 0.05) was employed to filter out the statistically insignificant compounds. The filtered data were normalized by a sum in MS Excel and loaded to SIMCA (Sartorius, Göttingen, Germany), where unsupervised principal component analysis (PCA) and supervised partial least squares discriminant analysis (PLS-DA) were performed. R2 (cumulative) and Q2 (cumulative) parameters were obtained for each model by fourfold internal cross-validation and were used to determine the validity of the models. R2 (cum) shows the variation described by all components in the model, and Q2 (cum) is a criterion of how accurately the model can predict class membership. The prediction and recognition abilities of the models were also obtained by fourfold internal cross-validation. The significant volatile compounds ('markers') were selected based on their Variable Importance in Projection (VIP) score in PLS-DA models. This study considered the most significant compounds with a VIP score of more than 1.0. The Pattern Search function of MetaboAnalyst software, which allows the specification and search for specific patterns of interest in the data, was also used to detect characteristic 'markers.' This function uses a template matching method, and the results are expressed as a ranked list of variables with the Spearman correlation coefficient and p-value. This study contemplated compounds with the Spearman correlation coefficient ≥ 0.80. Identification of selected 'markers' was performed using mass spectra NIST 17 library; for tentative identification, only compounds with match factor ≥ 750 and relevant Kovats retention indexes (RI) relative to n-alkanes (C7–C30) were considered. The compliance of the exact mass of detected ions (mass error < 5 ppm) and isotopic pattern were used to confirm the identification.

Results and discussion

Optimization of SPME‑GC‑HRMS

For this study, HS-SPME was the technique of choice. Sample preparation, extraction temperature, and desorption conditions in the GC injector are described in detail in the sections. "Chemicals and Materials "Sample Preparation" "SPME-GC-HRMS" Regarregard different patterns of volatiles in the analyzed plant species, dendrimeric and ric, were used for method optimization. To control the repeatability of measurements, the QC sample (described in Sect. "Non-target screening of volatile profiles of saffron and adulterants") containing all the tested plants was analyzed throughout the study (the retention time tolerance was 0.2 min, and the repeatability of the peaks areas 5%).

Non‑target screening of saffron and plant adulterants

In the first phase of the experiments, 38 authentic safety samples and 25 samples of different plant materials representing four plant species were analyzed. The chromatograms in Fig. 1 demonstrate the difference in the main volatile profiles between the saffron samples and potential contaminants. While volatiles with retention times not exceeding 45 min under used GC conditions on HP–5MS capillary column dominated in saffron (A), capsicum (D), and safflower (E), volatiles with relatively high intensities with retention times up to 60 min were present in turmeric (B) and calendula (C). When using the m/z value range 40–500, a high number of compounds ranging on average from 150 (capsicum) to 550 (turmeric) was detected after applying the deconvolution function.

Results of chemometric data evaluation for all samples

To investigate the differences and get an in-depth insight into the volatile profiles of saffron and its potential contaminants, a chemometric analysis starting with PCA and followed by PLS-DA was performed. The score plot in Fig. 2 shows the distinct separation of saffron samples from other plant matrices. The clustering was relatively tight despite the diverse geographic origin of measured saffron samples, their harvest time, and types (both powders and stigmas were contained in the experimental set). Regarding matrices of other botanical origin, samples of turmeric were well separated from all other plant adulterants, which overlapped to some extent; no improvement was obtained by supervised PLS-DA analysis. The tight cluster of QC samples documents a good reproducibility of the measurements.

 

Fig 1 GC–HRMS total ion chromatograms (40–550 m/z) of saffron (A), turmeric (B), calendula (C), capsicum (D), and safflower (E) samplesFig. 1 GC–HRMS total ion chromatograms (40–550 m/z) of saffron (A), turmeric (B), calendula (C), capsicum (D), and safflower (E) samples

 

Chemometric analysis of saffron vs. single plant species (binary models)

Although PCA would allow the identification of 'pure' authentic saffron, total replacement by other plant species is improbable under actual life conditions; dilution is more common. On these terms, knowing the adulteration's specific 'markers' is essential for fraud detection. To simplify their selection, binary PLS-DA classification models for saffron and potential adulterants were constructed (see Fig. 3), and then, internal fourfold cross-validation was performed. Table 1. shows the models' excellent (100%) prediction and recognition abilities, as well as high values of Q2 and R2 parameters (the lowest but still very high as in the case of the 'Saffron and Safflower' binary model).

Selection and identification of characteristic 'markers'

In the next phase of experiments, deconvoluted peaks characterized by particular m/z value and retention time with VIP-scores higher than 1.0 were considered candidate significant' markers.' The other (complementary) tool that was used for the potential 'markers' selection was the Pattern search function of MetaboAnalyst software; in that case, the criterion was the value of the Pearson correlation coefficient (≥ 0.8 required). The identification of compounds that met at least one of these selection criteria was based on the match of (i) measured spectra with the records on the NIST library, (ii) accurate mass of detected m/z (mass error < 5 ppm) corresponding to the expected elemental composition, and (iii)

The compliance with Kovats indexes is on the respective capillary column. It should be noted that we identified several unique volatile components [19–21] characteristic of saffron aroma (see Table 2); nevertheless, as the aim of this study was to find out 'markers' of impurities, more emphasis is placed on the identification of volatiles occurring in potential plant adulterants. The results of their tentative identifications are summarized in Table 3. Some of them, such as α-phellandrene, β-myrcene, α-terpinene, p-cymene, D-limonene, and eucalyptol, were reported to be contained also in saffron [19, 20]. Nevertheless, their concentration in samples available in this study did not exceed the detection threshold.

Detection of plant adulterants in saffron samples

As mentioned in the Introduction, saffron is a high-value commodity susceptible to fraud, typically by dilution by a cheaper plant material. Several admixtures were prepared to verify the function of 'markers' selected in the first phase of experiments to detect this type of fraud; the authentic saffron samples were artificially adulterated by turmeric, calendula, capsicum, and safflower. The sample preparation procedure is described in Sect. "Artificially adulterated saffron samples." Using markers summarized in Tables 2 and 3 for PCA and PLS-DA model construction, additions of potential plant contaminants as low as 2% w/w could be reliably identified. The paragraphs below provide more detailed information.

The PLS-DA score plot (see Fig. 4A) obtained for safe- from-turmeric admixtures shows good separation of the sample groups. The classification model was built based on 27 volatile compounds (16 'markers' of turmeric and 11 'markers' of saffron). The specific 'markers' of tur- meric were α-turmerone, ar-turmerone, cis-γ-atlantone, β-turmerone, 6S, 7R-bisabolene, and trans-α-atlantone, an example of specificity of β-turmerone characteristic 'marker' ion m/z = 105.0695 ± 5 ppm is illustrated in Fig. 4B.

 

Fig 3 Binary models for saffron and potential contaminants, PLS-DA score plots: A saffron and turmeric samples; B saffron and calendula samples; C saffron and capsicum samples; D saffron and safflower samplesFig. 3 Binary models for saffron and potential contaminants, PLS-DA score plots: A saffron and turmeric samples; B saffron and calendula samples; C saffron and capsicum samples; D saffron and safflower samples

 

Table 1 Parameters of the PLS-DA binary classification models for saffron and adulterants

Binary model                                          No. of samples R2 Q2                    Recognition ability Prediction         (%) ability (%) 'Saffron and Turmeric' 44 0.996 0.991 100 100 'Saffron and Calendula' 47 0.996 0.988 100 100 'Saffron and Capsicum' 44 0.996 0.985 100 100 'Saffron and Safflower' 42 0.989 0.973 100 100

Another plant material used for artificial adulteration was calendula. The statistical evaluation was performed based on 19 volatiles (8 'markers' of calendula and 11 'markers' of saffron). In the case of this classification model, the statistical difference was observed at all % w/w levels of the addition of calendula, which could be observed from the PLS-DA score plot (see Fig. 5A). The specific 'markers' of calendula were α-calcarine and thymoquinone. Interestingly, the latter compound's presence has not yet been reported in Calendula officinalis. Figure 5B, showing the extracted ion chromatograph (EIC) of thymoquinone char- characteristic 'marker' ion m/z = 164.0827 ± 5 ppm, documents its specificity.

Table 2. Identified volatile 'markers' of saffron with VIP value > 1.0, based on PLS-DA binary models and the Pearson correlation coefficient ≥ 0.8

Saffron' markers'   Retention time (min) Tentative identification by NIST Match factor* CAS Kovats index exp References NIST   2.15 2-Methyl furan 94.4 534–22-5 – 606 [5, 19–21] 8.32 2(5H)-Furanone 89.2 497–23-4 915 915 [5, 19–21] 10.01 6-Methyl-2-heptanone 75.7 928–68-7 954 957 [5, 19–21] 11.61 6-Methyl-5-hepten-2-one 90.0 110–93-0 993 988 [5, 19–21] 14.71 Dihydroisophorone 83.9 873–94-9 1037 1080 [19] 14.98 Isophorone 75.2 78–59-1 1045 1086 [20, 21] 23.50 4-Oxoisophorone 77.9 1125–21-9 1152 1145 [20, 21] 23.68 2-Hydroxy-4-oxo isophorone 80.4 4883–60-7 1230 1237 [22] 27.14 Safranal 75.8 116–26-7 1199 1207 [19] 33.83 Mint furanone 75.3 13,341–72-5 1302 1314 [23] 40.95 4-Hydroxy-2,6,6-trimethyl-3-oxy- clohexa-1,4-dienecarbaldehyde 81.4 35,692–95-6 1382 1396 [20]

Retention Tentative identification by NIST Match factor* CAS Kovats index Plant adulterants** References time (min)       exp NIST     2.58 3-Methyl butanal 93.4 590–86-3 – 669 SF [24] 3.71 3-Methyl-1-butanol 92.8 123–51-3 – 760 SF [24] 6.44 (E)-2-Hexenal 89.9 6728–26-3 857 850 CL, SF [24] 8.74 Methyl hexanoate 77.1 106–70-7 925 925 CL, SF [24] 9.02 α-Thujene 87.0 2867–05-2 932 929 TU, CL, CP, SF [24–34] 10.93 Sabinene 86.6 3387–41-5 974 975 TU, CP [25, 31, 32, 35] 11.83 β-Myrcene 89.5 123–35-3 991 984 TU, CP [25, 31, 32, 35] 12.47 α-Phellandrene 93.3 99–83-2 1006 998 TU [25] 12.8 3-Carene 89.8 13,466–78-9 1008 1003 CP [31] 13.23 α-Terpinene 79.0 99–86-5 1015 1010 TU, SF [24, 25] 13.75 p-Cymene 88.4 99–87-6 1022 1028 TU, CL, CP [25, 30, 31] 14.01 D-Limonene 76.2 5989–27-5 1026 1023 TU, CL, CP, SF [24–34] 14.14 Eucalyptol 87.2 470–82-6 1030 1022 TU, CP [25, 31, 32, 35] 14.42 2,2,6-trimethylcyclohexanone 84.7 2408–37-9 1032 1026 CP [32] 16.27 γ-Terpinene 80.1 99–85-4 1057 1056 SF [36] 18.75 α-Terpinolene 79.8 586–62-9 1086 1089 TU, CP [25, 31, 32, 35] 19.36 2-Nonanone 89.3 821–55-6 1092 1090 TU [25] 25.65 Terpinen-4-ol 82.8 562–74-3 1180 1182 CL, SF [30, 36] 29.48 D-Carvone 87.4 2244–16-8 1237 1238 CP [31] 29.91 Thymoquinone 81.6 490–91-5 1244 1249 CL – 58.69 α-Calacorene 78.1 21,391–99-1 1545 1546 CL [30, 37] 62.7 α-Tumerone 75.2 180,315–67-7 1591 1632 TU [25] 66.15 ar-Turmerone 75.4 532–65-0 1665 1672 TU [25, 35] 66.28 cis-γ-Atlantone 87.4 108,549–48-0 1668 1679 TU [35] 67.44 β-Turmerone 94.6 87,440–60-6 1696 1701 TU [25] 68.88 6S,7R-Bisabolone 86.3 72,441–70-4 1746 1754 TU [25] 69.72 trans-α-Atlantone 97.2 108,645–54-1 1774 1785 TU [25, 35]

*Match factor is a comparison of the unknown's mass spectrum's peaks the peaks in the NIST library;

**Plant adulterants: TU turmeric, CL calendula, CP capsicum, SF safflower

Fig 4 PLS-DA score plot A for saffron-turmeric admixtures; extracted ion chromatograph (EIC) m/z = 105.0695 ± 5 ppm B for β-turmerone (RT = 67.44 min)

 

 

 

 

Fig 5 PLS-DA score plot A for saffron-calendula admixtures; extracted ion chromatograph (EIC) m/z = 164.0827 ± 5 ppm B for thymoquinone (RT = 29.91 min)

 

 Fig 6 PLS-DA score plot A for saffron-capsicum admixtures; extracted ion chromatograph (EIC) m/z = 108.0929 ± 5 ppm B for D-carvone (RT = 29.48)

 

Fig7 PLS-DA score plot A for saffron-safflower admixtures; extracted ion chromatograph (EIC) m/z = 136.1241 ± 5 ppm B for γ-terpinene (RT = 16.27 min)

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