Detailed qualitative analysis of honeybush tea (Cyclopia spp.) volatiles by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry and relation with sensory data

Accepted Manuscript Title: Detailed qualitative analysis of honeybush tea (Cyclopia spp.) volatiles by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry and relation with sensory data Authors: Gaalebalwe Ntlhokwe, Magdalena Muller, Elizabeth Joubert, Andreas G.J. Tredoux, Andr´e de Villiers PII: DOI: Reference:

S0021-9673(17)31235-9 http://dx.doi.org/10.1016/j.chroma.2017.08.054 CHROMA 358799

To appear in:

Journal of Chromatography A

Received date: Revised date: Accepted date:

31-12-2016 25-5-2017 20-8-2017

Please cite this article as: Gaalebalwe Ntlhokwe, Magdalena Muller, Elizabeth Joubert, Andreas G.J.Tredoux, Andr´e de Villiers, Detailed qualitative analysis of honeybush tea (Cyclopia spp.) volatiles by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry and relation with sensory data, Journal of Chromatography Ahttp://dx.doi.org/10.1016/j.chroma.2017.08.054 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Detailed qualitative analysis of honeybush tea (Cyclopia spp.) volatiles by comprehensive two-dimensional gas chromatography

coupled

to

time-of-flight

mass

spectrometry and relation with sensory data

Gaalebalwe Ntlhokwe1, Magdalena Muller2, Elizabeth Joubert2,3, Andreas G.J. Tredoux1*, André de Villiers1*

1

Department of Chemistry and Polymer Science, Stellenbosch University, Private Bag X1,

Matieland 7602, South Africa. 2

Department of Food Science, Stellenbosch University, Private Bag X1, Matieland 7602,

South Africa. 3

Post-Harvest and Wine Technology Division, Agricultural Research Council (ARC),

Infruitec-Nietvoorbij , Private Bag X5026, Stellenbosch 7566, South Africa.

*Corresponding authors. Tel.: +27 21 808 3351 (A.G.J. Tredoux), +27 21 808 3351 (A. de Villiers); fax +27 21 808 3360. E-mail addresses: [email protected] (A.G.J. Tredoux, [email protected] (A. de Villiers).

Highlights

   

HS-SPME-GC×GC-TOF-MS was used for detailed analysis of honeybush tea volatiles. 287 compounds were identified, 101 using authentic standards. Tentative identification of 147 compounds for the first time in honeybush tea. Likely contribution of (E)-cinnamaldehyde to C. maculata aroma elucidated.

Abstract The volatile composition of honeybush (Cyclopia) species was studied by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass 1

spectrometry (GC×GC-TOF-MS). Headspace-solid phase micro-extraction (HSSPME) was used to extract the volatile compounds from tea infusions prepared from the three species C. genistoides, C. maculata and C. subternata. A total of 287 compounds were identified, 101 of which were confirmed using reference standards, while the remainder were tentatively identified using mass spectral and retention index (RI) data. The identification power of TOF-MS enabled the tentative identification of 147 compounds for the first time in honeybush tea. The majority of the compounds identified were common to all three Cyclopia species, although there were differences in their relative abundances, and some compounds were unique to each of the species. In C. genistoides, C. maculata and C. subternata 265, 257 and 238 compounds were identified, respectively. Noteworthy was the tentative identification of cinnamaldehyde in particular C. maculata samples, which points to the likely contribution of this compound to their distinct sensory profiles. This study emphasises the complexity of honeybush tea volatile composition and confirms the power of GC×GC combined with TOF-MS for the analysis of such complex samples.

Keywords:

comprehensive

honeybush

tea;

thermal

two-dimensional modulation;

gas

sensory

chromatography profiling;

(GC×GC);

time-of-flight

mass

spectrometry

1. Introduction Traditional honeybush tea, prepared through a high-temperature oxidation process (“fermentation”) from the plant material of Cyclopia Vent. species (family Fabaceae, tribe Podalyrieae), is considered South Africa’s sweetest herbal tea [1]. Honeybush shrubs are endemic to the coastal plains and mountain regions of the Western and Eastern Cape provinces of South Africa. Most of the honeybush teas available on the market constitute more than one Cyclopia species. Cyclopia genistoides, C. intermedia and C. subternata are currently mostly used for commercial production. However, as the industry is rapidly expanding, other species such as C. maculata and C. longifolia are also being investigated for commercial production [1]. Blending of species can have an important effect on the sensory properties of the final product [2]. It is therefore important to characterise each of the individual species before 2

blending to preserve or enhance specific aroma notes predominant in certain species, especially when aiming for niche markets. Until recently, there have been no formal terminologies to describe the characteristic honeybush tea aroma, nor has a method to determine the differences between Cyclopia species been established. This has resulted in teas of different qualities being sold on the market. As part of ongoing efforts to improve and standardise production, a honeybush ‘sensory wheel’, similar to that previously reported for rooibos tea [3], has recently been developed for use as a quality control tool in sensory studies involving honeybush tea [2]. Aroma is one of the most important properties used for assessment of tea quality [4]. For this reason, information on the volatile composition of honeybush tea is essential, and has recently been studied for the first time by gas chromatography hyphenated to mass spectrometry (GC-MS) and olfactometry (GC-O). More than 200 compounds were identified, with terpenoids constituting the highest percentage of compounds, followed by aldehydes, esters, ketones, hydrocarbons, alcohols and furans and others [5-7]. Important odour-active compounds were identified in C. subternata by means of GC-O. To attain these results, the authors used sorptive extraction by means of the sample enrichment probe (SEP) [8], separation on multiple columns, retention index (RI) comparison as well as purchased and synthesised standards to identify compounds, and thereby provided the first insight into the complexity of honeybush tea volatile composition. However, despite the use of multiple columns, it is not possible to completely resolve all the volatile components of honeybush tea by one-dimensional (1D) GC. Comprehensive two-dimensional gas chromatography (GC×GC) is increasingly being used as a more powerful alternative separation method that offers higher resolving power than 1D GC. This is a result of sequential separation of analytes on two different GC columns [9,10]. Indeed, GC×GC has found extensive application in the analysis of beverages [11], including tea [12]. We have recently reported the application of GC×GC using a new single-stage thermal modulator in combination with flame ionisation detection (FID) for the analysis of the volatile constituents of C. subternata, C. maculata and C. genistoides [13]. While this system demonstrated the applicability of GC×GC to honeybush analysis, and allowed differentiation of the three species based on volatile data in combination with multivariate data analysis, only 84 compounds could be identified using a limited number of authentic 3

standards. This did not allow for identification of important odourants responsible for the sensory differentiation of the same tea samples. The aim of the work reported here was therefore to use GC×GC combined with timeof-flight (TOF)-MS for the detailed qualitative analysis of the volatile constituents of honeybush tea. A commercial GC×GC-TOF-MS system equipped with a dual-stage cryogenic modulator was used for this purpose in combination with headspace-solid phase micro-extraction (HS-SPME) for the extraction of volatile compounds. The volatile composition of three Cyclopia species, namely C. genistoides, C. maculata and C. subternata, were elucidated and compared, with the emphasis on attempting to identify compounds potentially responsible for the observed sensory differences between the same tea samples.

2. Material and methods 2.1. Chemicals and materials A standard mixture consisting of 110 volatile organic compounds was kindly supplied by Laboratory of Ecological Chemistry (LECUS, Stellenbosch University, SA, Table 1). Standards were synthesised where commercial products were not available [7], and the rest were purchased from Sigma-Aldrich or Fluka (St. Louis, MO, USA). SPME was performed using a 65 µm polydimethylsiloxane/divinylbenzene (PDMS/DVB) fibre purchased from Supelco (Bellefonte, PA, USA). The C 6-C40 linear alkane mixture and sodium chloride were obtained from Sigma-Aldrich and AAAChemicals (La Marque, TX, US), respectively. Dichloromethane (DCM) used to dilute the linear alkane mixture was also purchased from Sigma-Aldrich. 2.2. Tea samples Five batches of plant material of each species C. genistoides, C. subternata and C. maculata were harvested in 2010; C. genistoides and C. subternata were harvested from commercial plantations located in the Western Cape Province (South Africa), and C. maculata from natural stands in the Overberg region of the Western Cape Province [14]. All plant material was fermented at 80°C for 24 h according to the procedure reported in [14]. The infusions at ‘cup of tea’ strength were prepared as described elsewhere [14]. Briefly, this entailed infusing 12.5 g plant material in 1000 4

mL freshly boiled deionised water for 5 min, whereafter the infusion was strained into pre-heated stainless steel flasks for sensory analysis and glass bottles for GC×GC analysis. The latter infusions samples were cooled to room temperature and frozen (18°C) until GC×GC analysis. 2.3. Headspace-solid phase micro-extraction (HS-SPME) procedure The PDMS/DVB SPME fibre was conditioned according to the specifications of the manufacturer prior to use (30 min at 250°C). Tea infusions were defrosted at room temperature, and 10 mL was placed in a 20 mL headspace vial containing 2 g NaCl. The sample was pre-incubated at 30°C for 3 min while being agitated at 500 rpm. The SPME fibre was exposed to the headspace at 30°C for 30 min at an agitation speed of 100 rpm; subsequently the analytes were desorbed in the GC injection port at 240°C for 10 min. All analyses were performed in duplicate. 2.4. GC×GC conditions Analyses were carried out on a LECO Pegasus® 4D instrument (LECO Corp., St. Joseph, MI, USA) consisting of an Agilent 7890 GC (Agilent Technologies, Palo Alto, CA, USA) equipped with a split/splitless injector, Gerstel MPS (multi-purpose sampler) autosampler (Mulheim ad Ruhr, Germany) and a dual stage cryogenic modulator (LECO) coupled to a Pegasus IV TOF-MS detector (LECO). Helium was used as a carrier gas at a constant flow of 1.14 mL/min. The injector was operated at 240°C in split mode (1:10 split ratio) for liquid injections and in splitless mode for SPME injections (splitless for 2 min). The solvent delay for liquid injections was 5 min. The column set consisted of a low polarity 30 m × 0.25 mm i.d. (internal diameter) × 0.25 μm df (film thickness) Rxi-5Sil MS column (Restek, Penn Eagle Park, CA, USA) in the first dimension (1D) and a polar 0.8 m × 0.25 mm i.d. × 0.25 μm df Stabilwax column (Restek) in the second dimension (2D). A modulation period of 5 sec was used for all analyses with the cryogenic trap cooled to -196°C using liquid nitrogen. The hot pulse duration set to 0.75 sec. The temperature of the GC oven was programmed from 40°C (2 min hold time) to 240°C (5 min hold time) at 5°C/min. The secondary oven offset temperature was +10°C relative to the GC oven. The transfer line and ion source were set to 250°C and 200°C, respectively, and the detector voltage was 1650 V. Data were acquired at a rate of 100 spectra/sec with mass scan range of 45-400 amu.

5

2.5. Data processing method Data processing was performed using ChromaTOF®-GC software (LECO, version 4.50.8.0) incorporating an algorithm for peak deconvolution. The 1D and 2D peak widths were set to 25 s and 0.4 s, respectively. The percentage match required to combine the modulated peaks was set to 65%, with a minimum signal-to-noise (S/N) of 50 for all sub-peaks. Identification was performed using reference standards (110 compounds), and where not available tentative identification was by MS library search using the NIST 11 library and comparison of calculated 1D linear retention indices (LRICal) with literature values (LRILit). The maximum difference between measured and literature RI values was set to 25 for screening purposes, and the minimum similarity match factor for spectral matching was set to 70%. 2.6. Descriptive sensory analysis Each of the tea infusions were subjected to descriptive sensory analysis (DSA) by a panel consisting of 10 members experienced in the sensory assessment of honeybush tea [14]. A range of aroma descriptors associated with the studied honeybush species were generated during panel training. During testing, the samples (labelled with three-digit codes and presented in a randomised order) were rated for the intensities of the aroma attributes on unstructured line scales (low = 0, prominent = 100) using Compusense® five software (Compusense, Guelph, Canada). Analyses were conducted in a sensory laboratory fitted with individual tasting booths under standard lighting and controlled temperature (21ºC) conditions. Testing sessions were performed in triplicate in three consecutive sessions to test judge reliability. Panel performance was evaluated using Panelcheck software (Nofima, Ås, Norway) for each individual sample. The data were subjected to test-retest analysis of variance (ANOVA) using SAS® software (Version 9.2, SAS Institute Inc., Cary, USA). Residuals were tested for non-normality using the Shapiro-Wilk test, and outliers were removed in the event of significant non-normality (p ≤ 0.05). Principal component analysis (PCA) with mean centering was performed using XLStat software (version 2015, Addinsoft, France) to provide a graphical representation of the relationship between the samples and their sensory attributes.

6

3. Results and discussion 3.1. Selection of experimental conditions To explore the volatile composition of honeybush tea and investigate potential correlation with sensory data for the same samples, ‘cup of tea’ infusions were used, since this is the form in which honeybush tea is consumed and for which the sensory data were obtained. For the extraction of honeybush volatiles, HS-SPME using a PDMS/DVB fibre was selected, based on our previous work in which this fibre was found to provide the best results for these samples [13]. An identical sample preparation and extraction procedure as reported previously [12] was used. Compared to SEP enrichment, which was used in previous work for the GC-MS analysis of honeybush tea volatiles [5-7], SPME is less sensitive, although this shortcoming is compensated for to some extent by the focusing capabilities of the GC×GC modulator and the good detectability of the TOF-MS detector used here [15,16]. Furthermore, an important consideration for the current work was the compatibility of HS-SPME with automation, which also minimises retention time shifts potentially associated with the manual injection procedure required for SEP, especially for highly volatile analytes. This is a significant benefit in terms of compound identification and comparison of volatile profiles between different samples, which is facilitated by reproducible retention times in both dimensions. For the separation of honeybush volatile compounds, a low polarity × polar column set was selected, with a WAX column in the second dimension. This choice was based on previous work where this column combination was found to provide optimal results for honeybush tea samples [13]. While it is common practice to use a narrowbore column in the second dimension to achieve better performance for very fast separations, this typically results in non-optimal carrier gas velocities (below and above the optimum values in the 1D and 2D, respectively) and may also result in overloading of the 2D column [17-19]. In this work, a 2D column of the same diameter and film thickness (0.25 mm, 0.25 μm) as the 1D column was used to minimise overloading of the second dimension column, which is especially a concern in natural product analysis, where compounds concentrations may span several orders of magnitude [20]. Having a highly polar column in 2D increases the retention of compounds, especially the highly polar compounds such as acids and alcohols (see further), at the expense of increasing the risk of wraparound. Wraparound was 7

indeed observed for some polar compounds. This could be reduced by increasing the modulation period or by increasing the secondary oven offset temperature, but was not attempted in this study, since wraparound did not compromise separation performance (see further).

3.2. Identification of honeybush tea volatiles Honeybush tea volatiles were identified first of all based on comparison of retention times and MS spectra with 110 authentic standards. An example of the contour plot obtained for the analysis of the standards is presented in Figure 1, with peak labels corresponding to Table 1. One hundred and one compounds were unambiguously identified in this manner using standards. In addition, tentative identification of compounds for which standards were not available was performed by comparing MS spectra with the NIST 11 library and experimental retention indices (RIs) with NIST and PHEROBASE databases. The minimum MS match factor was set to 700 (out of 1000) and RI differences of < 25 were allowed for initial screening purposes to include as many likely compounds as possible. It should be noted that in GC×GC, relatively large discrepancies between experimental and literature RI values may be obtained due to the phasing of modulation, and therefore it is common practice to use relatively large RI windows for initial screening purposes [21]. Attempts to utilise the first moment of each first dimension peak instead of the ‘slice’ of maximum intensity to obtain more accurate RI data showed some promise. However, this procedure was extremely time-consuming due to the requirement to export the data manually in order to determine the first moment, and was therefore not attempted for all compounds. The experimental RI values reported in Table 1 are therefore based on the peak ‘slice’ of maximum intensity in all cases. Despite this approach being less accurate, the average RI difference between literature and experimental values for the compounds identified here was 3 (Table 1). Using these processing conditions, more than a thousand peaks were obtained using an automated data processing method. To improve identification certainty, the number of peaks processed was limited to 1000 with a minimum S/N of 100 for peak detection; further processing parameters used are outlined in Section 2.5. While automated processing, including deconvolution, peak identification and library searching, was relatively fast (~2 min per data file), extensive subsequent manual intervention was 8

required. For non-standard compounds, tentative identification of each compound was manually confirmed. This step was performed at first for one sample of each of the three species, followed by manual reprocessing of all other samples. Although this was an extremely tedious process, which required ~4 months, it enabled tentative identification of 186 compounds. A total of 287 volatile compounds were therefore identified in the studied tea infusions (Table 1). Of these, 147 compounds were tentatively identified in honeybush for the first time, using a criterion of a minimum occurrence in 2 of the 30 analyses to be considered reliable.

Figure 1. Table 1.

3.3. GC×GC method performance Automated HS-SPME extraction using an autosampler provided good retention time reproducibility: two replicate analyses of each of the three Cyclopia species (n=6) provided average relative standard deviations (%RSD) for retention times of 0.02 and 0.43% in 1D and 2D, respectively (Table S1, Supporting Information (SI)). This corresponds to ±1 modulation period in the first dimension, whereas retention time shifts in the 2D, typically associated with the timing of the modulator [22], were also good. Therefore, shifts in the positions of the compounds on the two-dimensional chromatographic space were inconsequential, and despite the small number of repeat analyses of each sample, identification of compounds was simplified by the good reproducibility. The benefit of GC×GC separation, as well as the chromatographic performance of the method used here, are evident from Figure 2, which depicts a portion of the contour plot obtained for the analysis of a honeybush tea sample. Compounds like myrcene (38) and (E,E)-2,8-decadiene (42) (which is a newly identified compound in honeybush) are separated in the 2D column whilst co-eluting on the 1D column. Conversely, several monoterpene hydrocarbons such as limonene (56), myrcene (38), phellandrene (49), α-terpinene (54), (Z)-and (E)-β-ocimene (59 and 64), γ-

9

terpinene (69) and terpinolene (79) are mainly separated due to differences in vapour pressures in the first dimension.

Figure 2.

Peak shapes for the compounds presented in Figure 2 are generally good, with peak widths in the second dimension in the order of 100 msec (Table S2). This observation generally holds true for the majority of the terpenoids, hydrocarbons, aldehydes and esters identified in honeybush tea. On the other hand, the highly polar compound classes, such as acids, some alcohols and ketones were characterised by noticeably broader 2D peak widths. In several cases, this is due to wraparound, as confirmed by plotting the 2D peak width as a function of 2D retention time (Figure S1). Compounds which are wrapped around deviate significantly from the general trend and are observed in the left top of Figure S1 (e.g. compounds 97, 148, 174, 179, 184, 185, 194, 219, 223, 270 and 280). This can be explained by the fact that the 2D separation is performed under essentially isothermal conditions, and therefore peak-widths increase linearly with retention time. Compounds displaying wraparound can then easily be identified based on the much broader peak widths than expected for their observed 2D retention times. Other compounds showing broader 2D peak widths than suggested by the general trend observed in Figure S1, but are not wrapped around, mostly comprise highly polar molecules such as acids and alcohols, as well as relatively large sesquiterpenoid alcohols and aromatic aldehydes and ketones. The occurrence of wraparound is not unexpected for highly polar compounds and is due to the use of a WAX column in the second dimension. For the majority of the compounds identified in honeybush tea, especially the terpenoids, hydrocarbons and ketones, the use of a WAX column in 2D is beneficial in the sense that they are well separated from co-eluting compounds in the first dimension. Furthermore, a modulation period of 5 seconds not only kept wraparound to an acceptable level, but also fortuitously resulted in the majority of wrapped around compounds eluting in unoccupied space in the 2D separation plane (often in the void time of the 2D 10

separation, which is beneficial from the perspective of maximising the utilisation of the available separation space). GC×GC also offers the advantage, for some samples, of providing structured contour plots, where one will find compounds belonging to the same class typically grouped together [23,24]. Owing to the complexity of the honeybush samples, no clear indication of such grouping is evident from their GC×GC contour plots (refer to Figure 3 below). Nevertheless, some clustering of compounds according to their chemical classes is evident from Figure S2, which demonstrates the partially structured two-dimensional plots obtained for terpenoids, ketones and aldehydes. Ketones and aldehydes are grouped primarily according to their degree of saturation in the second dimension (wrapped around compounds are not shown in Figure S2). Terpenoids are grouped into monoterpenoid and sesquiterpenoid clusters. Five groups of monoterpenoids can be distinguished: monoterpene hydrocarbons were less retained in the 2D than the oxygen containing monoterpenoids, of which the alcohols showed the highest 2D retention, and ketones were distinguished from aldehydes by higher 1D retention. Monoterpenoids eluted in the 1D in the following order: monoterpene hydrocarbons, followed by monoterpene alcohols, aldehydes and ketones. Similarly, sesquiterpenoids alcohols were retained longer on the Stabilwax column (2D) than the sesquiterpene hydrocarbons (Figure S2B). The relatively orderly distribution of compounds according to their chemical nature, which is a consequence of the divergent retention mechanisms in the two columns used here, was found to be beneficial as an additional means of confirming tentative compounds identification.

3.4. GC×GC-TOF-MS analysis of C. subternata, C. maculata and C. genistoides volatiles and relation with sensory data Figure 3 presents typical GC×GC contour plots obtained for each of the three Cyclopia spp. analysed. As is evident from this figure, there are considerable differences in volatile composition of the three species, mainly in terms of the relative abundance of compounds, but also in the volatile compounds identified. 238, 257 and 265 compounds were identified according to the criteria outline in Section 3.2 in C. subternata, C. maculata, and C. genistoides samples, respectively. In total 7, 12 11

and 1 unique compound(s) were identified in C. genistoides, C. maculata and C. subternata samples, respectively, although it should be noted that these conclusions cannot be confirmed based on the data for the relatively small sample set analysed here. It would be of interest to investigate a larger batch of samples to verify these findings. A summary of number of compounds identified in each species as a function of chemical class is represented in Figure S3, while Table S2 lists the compounds identified in the present work, including aroma descriptors for selected compounds from literature. Sensory data for the samples analysed in the current study are summarised in the form of a PCA bi-plot in Figure 4, which shows the differentiation of the 15 samples and their association with particular sensory attributes as obtained from descriptive sensory analysis [14]. It is clear from this plot that the C. maculata samples are distinguished from samples of the other species by their positive scores on PC1, in particular associating with the positive attributes ‘caramel’, ‘woody’, ‘cooked apple’ and ‘cinnamon’. The two C. maculata samples MAC2 and MACC5 are differentiated by their strong association with ‘cinnamon’ and ‘cooked apple’ descriptors. The following discussion of the volatile compounds identified in the analysed samples focuses on the compounds that may potentially be responsible for the observed differences in the sensory profiles of the same samples depicted in Figure 4. The majority of the compounds identified were terpenoids (98 compounds), which include hydrocarbon terpenoids, terpene alcohols, aldehydes, ketones, ethers and acids. Geraniol (183), likely a contributing compound to the ‘rose geranium’ aroma attribute, was identified as a major compound in all samples [5]. Other important terpenes contributing to the aroma of honeybush tea [5] include (E)--damascenone (231),

linalool

(88),

(E)-β-damascone

(240),

(E)-β-ionone

(250)

and

megastigmatrienone (274), which were detected in all samples using reference standards. 35 terpenes were tentatively identified in honeybush tea for the first time in this work. Of these, several compounds may potentially contribute to ‘woody’ (19, 186, 223, 227 and 248), ‘lemon’ (39) or ‘pine’ (53) sensory attributes. In addition to terpenes, the other major classes of compounds identified include ketones (35 compounds), esters (34) and aldehydes (33). Of these, 19, 23 and 17 are tentatively identified for the first time in honeybush tea. Benzeneacetaldehyde 12

(63) may contribute to the ‘honey’ aroma descriptor which is primarily associated with C. genistoides and C. subternata samples (Figure 4), although this compound was detected in all samples. Other compounds of potential interest include maltol (3-hydroxy-2-methylpyrone, 97), identified in honeybush tea for the first time. This compound is associated with a sweet caramel aroma, and might contribute to the ‘caramel’ aroma attribute largely associated with C. maculata samples (Figure 4). Interestingly, 3-methoxy-2isobutylpyrazine (139) was also detected for the first time; this compound is a well known contributor of green/bell pepper aromas, although its contribution to the aroma profile of honeybush tea is unclear. Benzyl propanoate (188), detected only in C. maculata samples, might contribute to the ‘cooked apple’ aroma attribute of these samples. Of particular interest is the compound eugenol (219) which was detected in relatively high abundance in C. genistoides and C. maculata, whereas in C. subternata much lower levels were observed. In a sensory study by Theron et al. [2], which included the same set of samples as analysed in the present work, it was found that the aroma profiles of the C. maculata were distinctly different from the other five Cyclopia species studied (C. intermedia, C. genistoides, C. subternata, C. sessiliflora, C. longifolia). The main sensory attribute which contributed to the difference was ‘cassia cinnamon’. According to GC-O data obtained for the same samples, eugenol, which is characterised by a spicy aroma, was hypothesised to account for the difference in the sensory profile of C. maculata, as this compound was found in relatively high concentrations in this species compared to C. subternata [2]. In the present study, methyleugenol (235) and isoeugenol (246), both associated with similar aroma descriptors as eugenol (Table S2), were also identified for the first time in honeybush tea, only in C. genistoides and C. maculata samples. Interesting to note is the fact that for the current sample set (which is a subset of the samples used in [2]) the C. maculata samples were again clearly differentiated from the samples of the other two species (specifically on PC1) based on the sensory data (Figure 4). Furthermore, the sensory attributes largely responsible for this differentiation include ‘cassia cinnamon’ (abbreviated as ‘cinnamon’ in Figure 4), ‘cooked apple’, ‘caramel’ and ‘woody’. Two C. maculata samples in particular 13

(labelled MAC2 and MACC5 in Figure 4) were strongly associated with the sensory attribute ‘cassia cinnamon’. However, in the present study it was found that the levels of eugenol were also relatively high in C. genistoides (based on relative peak height, cf. Figure 5, compound 219). This observation seems to imply that eugenol is not responsible for the characteristic ‘cassia cinnamon’ sensory attribute responsible for the differentiation of the C. maculata samples. Of interest in this regard is the compound (E)-cinnamaldehyde (194), tentatively identified here for the first time in honeybush tea based on RI and MS data. This compound is a major constituent of cinnamon essential oil and is also associated with cinnamon, sweet and rose-apple flavour descriptors [25, 26]. Cinnamaldehyde was detected at higher levels in the C. maculata samples, and especially in samples MAC2

and

MACC5

cinnamaldehyde

is

(Figure likely

5).

This

responsible

provides for

the

a

strong

stronger

indication

‘cassia

that

cinnamon’

characteristics of the C. maculata samples, and these two samples in particular. Of course, further GC-O and sensory experimental confirmation of this hypothesis would be required. A likely explanation for the fact that this compound was not detected in previous GC-O experiments is that it elutes relatively close to geranial (191), nonanoic acid (196), as well as several other unidentified compounds in the first dimension apolar column (Figures 3 and 5), which is similar to the phase used for GC-O analyses [2]. In the GC×GC analyses, cinnamaldehyde is in fact wrapped around, and elutes in an empty region of the contour plot, which facilitated its identification in the present work. Although (E)-cinnamaldehyde was only detected in the two C. maculata samples referred to above, raw extracted ion chromatograms show the presence of a peak of much lower intensity at the same retention times in all the other samples (Figure 5AC). This seems to indicate that the concentration of (E)-cinnamaldehyde in the other samples is below the effective odour threshold of this compound in tea. Perhaps fortuitously, this compound was only detected as a chromatographic peak by GC×GC-TOFMS analysis in the samples where the cinnamon aroma was perceived to be significant. It would be therefore be informative to quantify cinnamaldehyde, as well as other potentially aroma-active compounds identified here for the first time, to ascertain their sensory contribution to honeybush tea.

14

Our results further support the finding that the importance of the ‘cassia cinnamon’ attribute is not species-specific: sensory data for a different set of honeybush samples of the same species indicated that C. subternata samples were strongly associated with this sensory descriptor [13]. The likely explanation for this is that the levels of the responsible compound(s), possibly (E)-cinnamaldehyde, depend on other aspects such as production parameters and vintage, such that this compound attains levels sufficient to affect sensory properties in particular samples. For example, Erasmus et al. [14] recently showed how the aroma attributes of honeybush samples are altered as a function of fermentation conditions. It is important to note that the (tentative) identification of new odour-active compounds reported here represents only the first step in assessing their potential contribution to honeybush tea aroma. This work should be followed by quantitative analysis to determine the concentrations of compounds relative to their odour thresholds, as well as GC-O determination of odour properties and reconstitution experiments to confirm the contribution of additional compounds to honeybush tea aroma. Considering that previous GC-O studies proved inconclusive in identifying the compound(s) responsible for particular aroma attributes, such future work could be informed by the results reported in the current contribution, which point to several new potentially important odourants.

4. Conclusions The results reported in the present contribution not only confirm the separation power of GC×GC, in this case using a commercial GC×GC system with a cryogenic modulator, but also demonstrate the amount of information this technique can provide when coupled to a powerful detector such as TOF-MS. HS-SPME combined with GC×GC-TOF-MS analysis using a low polarity × polar column combination provided excellent separation and enabled the tentative identification of 147 compounds in honeybush tea for the first time. Although the time-intensive nature of GC×GC-TOF-MS data analysis hampers the application of the technique to the routine analysis of large numbers of samples, its performance for the detailed qualitative analysis for a relatively small sample set in this work shed new light on honeybush tea volatiles. In total 287 compounds were identified in the three Cyclopia 15

species studied, and important differences in the volatile composition between species was observed. The number of compounds identified in C. genistoides C. maculata, and C. subternata were 265, 257 and 238, respectively. A limited number of compounds seem to be species-specific, although this should be confirmed by analysing a larger batch of samples. Especially the observation that (E)cinnamaldehyde was detected only in C. maculata samples is noteworthy, since this may in part be responsible for the unique sensory profiles of these samples. While the present work shed new light on the volatile composition of honeybush tea, further quantitative analysis should be performed on a larger set of samples to substantiate the sensory contribution of the compounds identified.

5. Acknowledgements The authors gratefully acknowledge Restek (RASP grant to AdV), Sasol (Collaborative grant to AdV and SASOL-STELL 2000 grant to GEN), Technology and Human Resources for Industry Programme (THRIP, grant TP1207112589 to AdV) and the National Research Foundation (NRF, grant 81830 to AdV, 91436 to AGJT, and 97769 and 86211 to GEN) for financial support, as well as Prof. B.V. Burger and Dr. M. le Roux for the kind donation of standards and their helpful comments. The authors would especially like to thank Alvaro Viljoen and Guy P. Kamatou (Tswhane University of Technology) for providing access to their instrumentation as part of an Equipment related Travel and Training Grant (87334 to AdV) from the NRF.

16

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[8]

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G.

Ntlhokwe, A.G.J. Tredoux, T. Górecki, M. Edwards, J. Vestner, M. Muller, L. Erasmus, E. Joubert, J.C. Cronje, A. de Villiers, Analysis of honeybush tea (Cyclopia

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volatiles

by

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Guilhaus,

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19

Figure captions 18

30

67

48

169

11

147

29

4 21

9

196

71 99

220

124

256

129

12

116

62

183

133 168

52

3

34 13

76 86 90 103

132 153

238 210

273 191 122 252 176 109 28 243 143 163 203 231 250 61 240 211 74 80 85 130 37 271 199 162 58 230 264 239 213 47 27 159 206 89 106 112 60 40 55 237 247 56 216 228 242 32 157 257 272 236 83 185 107 22 209 167 69 79 219 202 59 212 171 35 46 88

8 16 5

137 226

288

296 295

Figure 1.

Figure 1: Total ion chromatogram (TIC) contour plot obtained for the GC×GC-TOFMS analysis of honeybush tea reference standards. Peak numbers correspond to Table 1. Experimental conditions: 1D column: 30 m × 0.25 mm i.d. × 0.25 μm d f Rxi5Sil MS; 2D column: 0.8 m × 0.25 mm ID × 0.25 μm d f Stabilwax; temperature program: 40°C (2 min) to 240°C (5 min) at 5°C/min; modulation period: 5 sec; detection: TOF-MS, 100 spectra/sec, 45-400 amu; injection: split (1:10).

20

Unknown 2

47 Octanal 42 (E,E)-2,8-decadiene

35 (6Z)-2,6-dimethyl2,6-octadiene

51 (Z,Z)-2,8-decadiene

38 myrcene

64 (E)-β-Ocimene 69 γ-Terpinene

54 α-Terpinene

49 α-Phellandrene

Unknown 1

55 p-Cymene

60 2,2,6-trimethyl cyclohexanone

56 Limonene

79 terpinolene

59 (Z)-β-Ocimene

45 (6E)-2,6-dimethyl-2,6octadiene

Figure 1.

Figure 2: Section of the TIC GC×GC contour plot illustrating the separation performance of the GC×GC method for selected volatile compounds present in C. genistoides. Peak numbers correspond to Table 1. Compounds were extracted by HS-SPME and injected in splitless mode; other experimental conditions as specified in Figure 1.

21

A

293 268 183

63

29

196 71

9

294

168 99 153

72 62

76 86

34 57

2

37

82

74

20

1

14

6

22

238

274 251

143 163176

80

65

5

91 90 123 136 191

231

88 121 155 47 199 51 60 85 42 55 64 31 179 197 174 83 89 59 69 56 79 157 54 38 49 45 184

296

250 295

219

B

293 268 183

63

29

196 71

9

294

99 168

72 62

76 86

34 57

2

37

82

74

20

1

14

6

22

238

231

88 121 155

47 51 60 85 42 55 64 31 83 89 59 69 56 79 54 38 49 45

274 251

176 163 191

143

80

65 5

153 91 90 123 136

199

296 250 295

179 197 157 219

184

194

C

293 268 183

63

29

196 71

9

294

168 99

72

153

62 76 86

34 57

2

37

74 65

5

1 6

20 14

22

82

80

91 90 123 136

143

274

238 251 176 163

191

88 121 155 47 199 51 60 85 42 55 64 197 31 179 83 89 59 69 174 56 79 157 54 38 49 184 45

231

296 250

219

295

Figure 3. 22

Figure 3: Representative GC×GC-TOF-MS TIC contour plots obtained for the analysis of honeybush tea species: (A) C. genistoides, (B) C. subternata and (C) C. maculata. Peak numbers correspond to Table 1. Experimental conditions as outlined in Figure 2.

Biplot (axes F1 and F2: 64.08 %) 2.5 MAC2 2 FynbosSweet

1.5

Cinnamon Coconut MACC5

FynbosFloral

SUBB5

RosePerfume

F2 (29.86 %)

1

CookedApple Woody

GENB5

FruitySweet

SUB8

Pine

0.5

Caramel

GENC5

RoseGeranium Honey

0

Cooked Vegetables

Walnut

SUB2 -0.5

GEN4

Apricot

Lemon

GEN8 GEN6

-1

BurntCaramel

SUB4

Rotting Plantwater

SUBC5 -2.5

-2

-1.5

-1

-0.5

MAC17

Hay/DriedGrass

GreenGrass

-1.5

Dusty

MACB5

0

0.5

1

1.5

MAC12 2

2.5

F1 (34.23 %) Figure 4.

Figure 4: PCA bi-plot showing the differentiation of the 15 honeybush tea samples analysed on the basis of their sensory attributes. The abbreviations GEN, MAC, and SUB refer to C. genistoides, C. maculata and C. subternata, respectively. Sensory data were obtained as outlined in Section 2.6 and [14].

23

A 18000 16000 14000 12000 191

10000 8000

197 199

6000 4000

206

194

2000

219

0 1st Time (s) 1135 2nd Time (s) 1

1135 3 131 Gen 8

1140 0 131 Gen 6

B

131 Gen 4

1140 2 131 Gen-C5

1140 4 131 Gen-B5

1145 1

194 18000 16000 14000

12000 10000

191

8000 6000

197 199

4000 2000

206

194

0 1st Time (s) 1135 2nd Time (s) 1

1135 3

131 Mac-C5

131 Mac 2

1140 0

1140 2

1140 4

131 Mac-B5

131 Mac2012-12

219

1145 1

131 Mac2012-17

C 18000 16000

14000 12000 10000 8000

191

6000

4000

197 199

194

206

2000 0 1st Time (s) 1135 2nd Time (s) 1

219 1135 1140 3 0 131 Sub 8 131 sub 4 131 Sub 2

1140 2 131 Sub-C5

1140 4 131 Sub-B5

1145 1

Figure 5.

Figure 5: Selected regions of the GC×GC contour plots illustrating the differences in the relative levels of particular volatile compounds between (A) C. genistoides, (B) C. maculata, and (C) C. subternata. The left hand figures show the corresponding raw extracted ion chromatograms for m/z 131 (the unique ion for cinnamaldehyde) for each of the five samples of each species. The samples showing the highest peak areas for cinnamaldehyde (compound 194) in (B) are MACC5 and MAC2 (refer to 24

Figure 4). Note also the relatively high levels of eugenol (compound 219) in both C. genistoides and C. maculata samples.

25

Table captions Table 1: Volatile compounds identified in honeybush tea by GC×GC-TOF-MS, arranged according to chemical class.

No.

Compound name

Identification methodf

RICalc

RILitd

GENe

MACe

SUBe

800

799







MS,RI

838

840







MS,RI

Ref.g

Hydrocarbons 6

2-Octenea,b

12

1,2,5,5-Tetramethyl-1,3cyclopentadienea (E,E)-1,3,6-Octatriene

876

880







MS,RI

14

2,6-Dimethyl-1,5-heptadienea

879

882







MS,RI

42

(E,E)-2,8-Decadiene

990

995







MS,RI

46

Decane

1000

1000







STD,MS,RI

51

(Z,Z)-2,8-Decadiene

1006

1001







MS,RI

65

2-methyl-6-methylene-2-Octene

1052

1039







MS,RI

95

2,6-Dimethyl-1,3,5,7-octatetraenea,b

1115

1134







MS,RI

101

2,6-Dimethyl-1,3,5,7-octatetraenea,b

1120

1134







MS,RI

1123

1134







MS,RI

7

110

(E,E)-2,6-Dimethyl-1,3,5,7octatetraenea,b 2,6-Dimethyl-1,3,5,7-octatetraenea,b

1137

1137







MS,RI

117

3-Phenylbut-1-enea

1148

1148







MS,RI

157

Dodecanea

1200

1200







STD,MS,RI

180

α-Ionenea

1255

1255







MS,RI

201

1-Methyl-naphthalenea

1299

1299







MS,RI

1356

1355







MS,RI

104

236

1,2-Dihydro-1,1,6-trimethylnaphthalenea Tetradecanea

1401

1400







STD,MS,RI

238

2,6-Dimethyl-naphthalenea

1405

1408







STD

244

1,7-Dimethyl-naphthalenea

1422

1419







MS,RI

257

Pentadecane

1501

1500







STD,MS,RI

218

260

Butylated

1501

1504







MS,RI

292

2,6-Diisopropylnaphthalenea

1717

1717







MS,RI

No.

Compound name

RICalc

RILitd

GENe

MACe

SUBe

Hydroxytoluenea

[28]

Identification methodf

2

Alcohols 4-Methyl-2-pentanola

764

760







MS,RI

3

1-Pentanol

774

771







STD,RI,MS

4

(Z)-2-Penten-1-ol

778

774







STD

9

(Z)-3-Hexen-1-ol

852

853







STD,MS,RI

11

1-Hexanol

867

871







STD,RI,MS

34

1-Octen-3-ol

982

981







STD,MS,RI

44

3-Octanola

998







MS,RI

998

[28]

26

Ref.g

50

Carbitola

1008

1006







MS,RI

57

2-Ethyl-1-hexanola

1031

1030







MS,RI

62

Benzyl alcohol

1045

1041







STD,MS,RI

72

(E)-2-Octen-1-ola

1069

1069







MS,RI

76

1-octanol

1074

1074







STD,MS,RI

93

2,6-Dimethyl-cyclohexanola

1112

1112







MS,RI

99

1119

1118







STD,MS,RI

1157

1157







MS,RI

136

2-Phenylethanol 2,3,3-Trimethyl-bicyclo[2.2.1]heptan2-ola 1-Nonanola

1173

1171







MS,RI

165

7-Methyl-3-methylene-6-octen-1-ola

1220

1221







MS,RI

219

Eugenol

1358

1358







STD,MS,RI

241

2,4,7,9-Tetramethyl-5-decyn-4,7-diola

1409

1407







MS,RI

246

Isoeugenola

1451

1452







MS,RI

204

Phenols 4-(1-Methylpropyl)-phenola

1308

1314







MS,RI

208

2-Methoxy-4-vinylphenola

1317

1317







MS,RI

261

2,4-bis(1,1-dimethylethyl)-Phenola

1506

1512







MS,RI

270

Melleina

1544

1549







MS,RI

No.

Compound name

RICalc

RILitd

GENe

MACe

SUBe

1

Aldehydes Pentanal

722.5

722







MS,RI

5

Hexanal

794.5

793







STD,MS,RI

8

(E)-2-Hexenal

851

855







STD,MS,RI

16

(Z)-4-Heptenal

898

895







STD,MS,RI

17

Heptanal

901

901







MS,RI

28

(E)-2-Heptenal

957

957







STD,MS,RI

29

Benzaldehyde

961

960







STD,MS,RI

47

Octanal

1003

1004







STD,MS,RI

52

(E,E)-2,4-Heptadienal

1012

1012







STD,MS,RI

63

Benzeneacetaldehydea

1045

1045







MS,RI

68

(E)-2-Octenala

1058

1057







MS,RI

70

α-Methyl-benzeneacetaldehydea

1066

1080







MS,RI

73

2-Methyl-benzaldehydea

1069

1067







MS,RI

89

Nonanal

1106

1107







STD,MS,RI

94

(E,E)-2,4-Octadienala

1112

1113







MS,RI

122

(E,Z)-2,6-Nonadienal

1154

1153







STD,MS,RI

127

(E)-2-Nonenal

1159

1160







STD,MS,RI

140

3,5-Dimethyl-benzaldehydea

1177

1169







MS,RI

158

Benzylidenemalonaldehydea

1202

1215







MS,RI

159

Decanal

1207

1207







STD,MS,RI

160

(E,E)-2,4-Nonadienala

1216

1216







MS,RI

1258

1258







MS,RI

1258

1250







STD,MS,RI

126

184

4-Methoxy-benzaldehyde

185

p-Anisaldehyde

a

Identification methodf

27

Ref.g

189

(Z)-2-Decenala

1265

1263







MS,RI

192

2-Phenylbut-2-enala

1272

1274







MS,RI

194

(E)-Cinnamaldehydea

1276

1270







MS,RI

No. 206

Compound name Undecanal

RICalc 1310

RILitd 1310

GENe 

MACe 

SUBe 

210

(E,E)-2,4-Decadienala

1319

1319







STD,MS,RI

215

Piperonala

1341

1347







MS,RI

224

2-Undecenala

1365

1368







MS,RI

239

Dodecanal

1407

1409







STD,MS,RI

254

5-Methyl-2-phenyl-2-hexenala

1485

1486







MS,RI

1525

1535







MS,RI

a

Identification methodf STD,MS,RI

267

Lilial

15

Ketones 2-Heptanone

885

884







MS,RI

27

6-Methyl-2-heptanone

954

956







STD,MS,RI

33

1-Octen-3-onea

976

975







MS,RI

36

3-Octanonea

984

989







MS,RI

37

6-Methyl-5-hepten-2-one

984

985







STD,MS,RI

41

2-Octanonea

990

989







MS,RI

60

2,2,6-Trimethyl-cyclohexanone

1036

1036







STD,MS,RI

61

(E)-3-Octen-2-one

1039

1040







STD,MS,RI

71

Acetophenone

1067

1067







STD,MS,RI

75

(E,E)-3,5-Octadien-2-one

1071

1072







MS,RI

83

2-Nonanone

1090

1090







STD,MS,RI

86

3,5-Octadien-2-oneb

1093

1092







STD,MS,RI

90

(E)-6-Methyl-3,5-heptadien-2-one

1107

1106







STD,MS,RI

96

(Z)-6-Methyl-3,5-heptadiene-2-onea

1115

1108







MS,RI

103

Isophoronea

1121

1120







STD,MS,RI

109

4-Acetyl-1-methylcyclohexene

1132

1131







STD,MS,RI

112

(E)-3-Nonen-2-one

1140

1144







STD,MS,RI

113

5-Ethyl-6-methyl-3E-hepten-2-onea,b

1142

1143







MS,RI

116

4-Ketoisophorone

1143

1145







STD,MS,RI

No. 133

Compound name Propiophenone

RICalc 1165

RILitd 1165

GENe 

MACe 

SUBe 

145

1-Acetyl-4-methylbenzenea

1185

1183







MS,RI

149

2-Decanonea

1192

1192







MS,RI

1256

1251







MS,RI

1268

1274







MS,RI

1274

1276







MS,RI



STD,MS,RI

Identification methodf STD,MS,RI

193

2-Isopropyl-5-methyl-3-cyclohexen-1onea 2-Hydroxy-3-isopropyl-6methylcyclohex-2-enonea 4,8-Dimethyl-nona-3,8-dien-2-onea

199

2-Undecanone

1295

1295





217

4-Acetylanisolea

1355

1348







MS,RI

234

2-Dodecanonea

1392

1391







MS,RI

237

6,10-Dimethyl-2-undecanonea

1400

1400







STD,MS,RI

181 190

28

Ref.g

Ref.g

1489

1480







MS,RI

1525

1535







MS,RI

1578

1589







MS,RI

283

Apocynina 6,10-dimethyl-(E,Z)-3,5,9Undecatrien-2-onea (E,E)-6,10-dimethyl-3,5,9Undecatrien-2-onea Benzophenonea

1626

1625







MS,RI

286

Zingeronea

1642

1645







MS,RI

296

Hexahydrofarnesylactone

1839

1845







STD,MS,RI

18

Furans 2-Acetylfuran

908

912







STD,MS,RI

961

964







STD

255 266 276

furfurala

30

5-Methyl

40

2-Pentylfuran

990

993







STD

58

3,4-Dimethyl-2,5-furandione

1033

1038







MS,RI

66

5-Ethyl-2-furaldehydea

1056

1032







MS,RI

114

Lilac aldehyde A

1143

1155







MS,RI

119

Lilac aldehyde C

1151

1163







MS,RI

161

4,7-Dimethyl-benzofurana

1217

1220







MS,RI

262

Dihydroagarofuran

1507

1504







MS,RI

263

Dibenzofurana

1516

1517







MS,RI

No.

Compound name

RICalc

RILitd

GENe

MACe

SUBe

10

Carboxylic acids 3-Methyl butanoic acid

859

858







STD,MS,RI

13

2-Methyl butanoic acid

876

875







STD

43

Hexanoic acid

996

995







MS,RI

1086

1083







MS,RI

1130

1128







MS,RI

1189

1186







MS,RI





STD,MS,RI

78 108 148

Heptanoic

acida

2-Ethyl hexanoic Octanoic

acida

acida

Identification methodf

196

Nonanoic acid

1284

1283



227

Decanoic acida

1374

1374







MS,RI

1464

1465







MS,RI

1561

1559







STD,MS,RI

1757

1758







MS,RI

923

922







MS,RI

acida

249

Undecanoic

272

Dodecanoic acid acida

293

Tetradecanoic

20

Esters (saturated, unstaurated, aromatic) Methyl hexanoate

931

933







MS,RI

87

(Z)-3-Methyl hexanoatea Methyl benzoatea

1096

1096







MS,RI

106

Methyl octanoate

1126

1126







STD,MS,RI

129

Benzyl acetate

1163

1163







STD,MS,RI

134

Ethyl benzoatea

1171

1171







MS,RI

1174

1176







MS,RI

1184

1185







MS,RI

1193

1193







MS,RI

1195

1195







MS,RI

1199

1186







MS,RI

24

138 144 150

2-Phenethyl

formatea

(3Z)-Hexenyl Methyl

butanoatea

salicylatea

octanoatea

151

Ethyl

156

(3E)-Hexenyl butanoatea

29

Ref.g

167

Methyl nonanoate

1225

1224







STD,MS,RI

171

(Z)-3-Hexenyl isovalerate

1231

1235







STD,MS,RI

172

(Z)-3-Hexenyl-(E)-2-butenoatea

1234

1231







MS,RI

No. 175

Compound name (E)-3-Hexenyl isovaleratea

RICalc 1237

RILitd 1237

GENe 

MACe 

SUBe 

182

2-Phenethyl acetate

1256

1256







MS,RI

188

Benzyl

propanoatea

1259

1257







MS,RI

197

Bornyl

acetatea

1289

1289







MS,RI

203

1301

1300







STD,MS,RI

1322

1325







STD

212

Geranyl formate (Z)-Hex-3-enyl-(E)-2-methylbut-2enoate Methyl decanoate

1325

1326







STD,MS,RI

213

Hexyl tiglate

1331

1333







STD,MS,RI

1337

1338







MS,RI

1362

1362







MS,RI

1367

1368







MS,RI





STD

211

214 222

(E)-2-Hexenyl Butyl carbitol

tiglatea

acetatea

225

4-tert-Butylcyclohexyl

226

benzoatea

acetatea

Identification methodf MS,RI

1374

1377

1375

1373







MS,RI

230

Butyl Benzoic acid, 4-methoxy-, methyl estera Geranyl acetate



1377

1376







STD,MS,RI

232

Methyl cinnamatea

1384

1389







MS,RI

1485

1489







MS,RI

228

isovaleratea

253

Phenethyl

264

Methyl dodecanoate

1521

1526







STD,MS,RI

273

(Z)-3-Hexenyl benzoate

1568

1580







STD,MS,RI

281

Isopropyl dodecanoatea

1620

1618







MS,RI

Methyl

dihydrojasmonatea

1644

1648







MS,RI

294

Benzyl

benzoatea

1764

1766







MS,RI

295

Isopropyl myristate

1822

1812







STD,MS,RI

21

Lactones γ-Butyrolactone

924

922







STD,MS,RI

67

5-Hexanolide

1057

1056







STD,MS,RI

220

Nonan-4-olide

1360

1358







STD,MS,RI

256

5-Decanolidea

1492

1492







STD,MS,RI

No.

Compound name

RICalc

RILitd

268

Dihydroactinidiolide

1529

1525

GENe 

MACe 

SUBe 

931

934







MS,RI

287

Identification methodf MS,RI

970

971







MS,RI

124

Ethers 2,7-Dimethyl-oxepinea 2,2,6-Trimethyl-6vinyltetrahydropyranb 4-Vinylanisole

1154

1153







STD,MS,RI

198

Anetholea

1289

1290







MS,RI

207

Edulan

Ia

1313

1314







MS,RI

235

Methyleugenola

1399

1399







MS,RI

23 31

30

Ref.g

Ref.g

48

Other compounds 2-Formyl-1-methylpyrrole

1005

1022







STD,MS,RI

77

2-Acetyl-1-methylpyrrolea

1075

1096







MS,RI

82

Dimethylanilinea

1088

1086







MS,RI

97

Maltola

1117

1114







MS,RI

111

4-Methylindan

1140





MS,RI

139

1176

1151 1179



3-Methoxy-2-isobutylpyrazinea







MS,RI

146

3,9-Epoxy-p-menth-1-enea

1187

1178







MS,RI





STD,MS,RI

169

Benzothiazole

1227

1227



173

3,9-Epoxy-1-p-menthenea 3-Ethyl-4-methyl-1H-pyrrole-2,5dionea Theaspirane isomer 1b

1234

1236







MS,RI

1249

1235







MS,RI

1301

1288







STD,MS,RI

[7]

1319

1304







STD,MS,RI

[7]

179 202

2b

209

Theaspirane isomer

19

Terpene hydrocarbons 3-Thujenea

923

923







MS,RI

22

α-Pinene

931

939







STD,MS,RI

25

Citronellenea

942

943







MS,RI

No. 26

Compound name Camphene

RICalc 948

RILitd 948

GENe 

MACe 

SUBe 

32

β-Pinene

976

979







STD,MS,RI

35

(6Z)-2,6-Dimethyl-2,6-octadiene

984

990







STD,MS,RI

38

Myrcene

989

991







MS,RI

45

(6E)-2,6-Dimethyl-2,6-octadiene

1000

1004







MS,RI

49

α-Phellandrene

1006

1005







MS,RI

54

α-Terpinene

1017

1017







MS,RI

55

p-Cymene

1025

1025







STD,MS,RI

56

Limonene

1030

1031







STD,MS,RI

59

(Z)-β-Ocimene

1036

1038







STD,MS,RI

64

(E)-β-Ocimene

1047

1044







MS,RI

69

γ-Terpinene

1060

1060







STD,MS,RI

79

Terpinolene

1087

1088







STD,MS,RI

85

p-Cymenene

1090

1090







STD,MS,RI

107

Allo-ocimene

1128

1132







STD,MS,RI

216

α-Cubebene

1349

1351







STD

221

Megastigma-4,6(E),8(E)-triene

1361

1360







MS,RI

229

α-Copaene

1376

1376







STD,MS,RI

233

β-Bourbonene

1385

1374







MS,RI

242

(E)-Caryophyllene

1421

1419







STD,MS,RI

247

α- Humulene

1457

1455







STD

248

cis-β-Santalenea

1461

1461







MS,RI

258

α-Muurolene

1501

1499







MS,RI

259

trans-calamenenea

1501

1508







MS,RI

265

Calamenene

1521

1521







MS,RI

269

α-Calacorene

1541

1541







MS,RI

Identification methodf MS,RI

31

Ref.g

290

Cadalenea

1671

1671







No.

Compound name

RICalc

RILitd

GENe

MACe

SUBe

74

Terpene alcohols cis-Linalool oxide (furanoid)

1071

1071







STD,MS,RI

80

trans-Linalool oxide (furanoid)

1087

1087







STD,MS,RI

81

2,6-Dimethyl-1,7-octadien-3-ol

1088

1095







MS,RI

88

Linalool

1101

1101







STD,MS,RI

MS,RI

Identification methodf

91

Hotrienol

1107

1108







MS,RI

102

Myrcenola

1121

1118







MS,RI

105

(Z)-p-Menth-2-en-1-ola

1126

1126







MS,RI

115

(E)-p-Menth-2-en-1-ola

1143

1142







MS,RI

123

(Z)-Ocimenol

1154

1155







MS,RI

131

(E)-Ocimenol

1165

1153







MS,RI

132

Isoborneol

1165

1162







STD,MS,RI

137

Borneol

1174

1175







STD,MS,RI

142

dl-Menthola

1179

1178







MS,RI

143

Terpinen-4-ol

1182

1182







STD,MS,RI

147

p-Cymen-8-ol

1188

1183







STD

153

α-Terpineol

1196

1195







STD,MS,RI

166

trans-Carveola

1223

1224







MS,RI

168

Nerol

1226

1230







STD,MS,RI

170

Citronellola

1229

1226







MS,RI





MS,RI

174

Linalool

1237

1237



183

Geraniol

1256

1253







STD,MS,RI

200

p-Cymen-7-ola

1298

1295







MS,RI

205

Carvacrola

1308

1306







MS,RI

271

(E)-Nerolidol

1558

1563







STD,MS,RI

278

Epicedrola

1605

1608







MS,RI

282

γ-Eudesmol

1621

1620







MS,RI

285

α-Cadinol

1640

1640







MS,RI

No. 288

Compound name β-Eudesmol

RICalc 1655

RILitd 1651

GENe 

MACe 

SUBe 

291

α-Bisabolol

1686

1683







MS,RI

98

Terpene aldehydes α-Cyclocitrala

1117

1123







MS,RI

121

Citronellala

1154

1153







MS,RI

152

Myrtenala

1196

1194







MS,RI

155

Safranal

1198

1197







MS,RI

162

(+)-p-Menth-1-en-9-al isomer 1

1219

1198







STD,MS,RI

163

β-Cyclocitral

1219

1219







STD,MS,RI

164

(+)-p-Menth-1-en-9-al isomer 2

1219

1200







STD,MS,RI

176

Neral

1241

1238







STD,MS,RI

hydratea

Identification methodf STD,MS,RI

Ref.g

Ref.g

[7]

32

186

β-Homocyclocitrala

1259

1261







MS,RI

191

Geranial

1271

1271







STD,MS,RI

195

Phellandrala

1280

1276







MS,RI

84

Terpene ketones Fenchonea

1090

1092







MS,RI

100

Thujan-3-one

1120

1124







MS,RI

118

Camphora

1148

1147







MS,RI

125

Menthonea

1156

1166







MS,RI

128

Pinocarvonea

1162

1164







MS,RI

130

iso-Menthonea

1165

1163







MS,RI

154

trans-p-Menth-8-en-2-onea

1198

1200







MS,RI

177

Carvotanacetonea

1247

1251







MS,RI

178

Carvonea

1247

1243







MS,RI

187

Carvenone

1259

1258







MS,RI

231

(E)-β-Damascenone

1380

1380







STD,MS,RI

No. 240

Compound name (E)-β-Damascone

RICalc 1408

RILitd 1408

GENe 

MACe 

SUBe 

243

α-Ionone

1421

1426







STD,MS,RI

245

Geranyl acetone

1448

1448







MS,RI

250

(E)-β-Ionone

1478

1477







STD,MS,RI

251

3,4-Dehydro-γ-ionone

1478

1485







MS,RI

252

5,6-Epoxy-β-ionone

1481

1497







STD,MS,RI

274

Megastigmatrienoneb

1572

1591







MS,RI

275

(E,E)-Pseudoinonea

1578

1578







MS,RI

279

1606 1608

1604







MS,RI

280

Megastigmatrienoneb 3-Hydroxy-β-damasconea

1617







MS,RI

284

3-Oxo-α-ionola

1639

1630







MS,RI

289

3-Keto-β-iononea

1657

1661







MS,RI

39

Terpene ethers 2,3-Dehydro-1,8-cineolea

989

990







MS,RI

53

1,4-Cineola

1014

1015







MS,RI

1112

1117







MS,RI

oxideb

Identification methodf STD,MS,RI

92

trans-Rose

120

Nerol oxide

1154

1155







MS,RI

135

cis-Linalool oxide (pyranoid)

1171

1167







MS,RI

141

trans-Linalool oxide (pyranoid)

1177

1173







MS,RI

277

Caryophyllene oxide

1581

1581







MS,RI







MS,RI

Terpene acids 223 Neric acida 1364 1365 a Compounds identified in honeybush tea for the first time b The stereochemistry of the compound was not determined c Calculated retention index (RI) d Retention index (RI) from literature e GEN: C. genistoides, MAC: C. maculata and SUB: C. subternata

[7]

33

Ref.g

f Methods

used for identification of compounds; STD: reference standards, MS: mass spectral, and RI-retention index data g References used to extract retention index (RI) data

34