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Profiling Volatile Compounds in Blackcurrant Fruit using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography-Mass Spectrometry

doi: 10.3791/62421 Published: June 9, 2021
Delphine M. Pott1, José G. Vallarino1, Sonia Osorio1


There is an increasing interest in measuring volatile organic compounds (VOCs) emitted by ripe fruits for the purpose of breeding varieties or cultivars with enhanced organoleptic characteristics and thus, to increase consumer acceptance. High-throughput metabolomic platforms have been recently developed to quantify a wide range of metabolites in different plant tissues, including key compounds responsible for fruit taste and aroma quality (volatilomics). A method using headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS) is described here for the identification and quantification of VOCs emitted by ripe blackcurrant fruits, a berry highly appreciated for its flavor and health benefits.

Ripe fruits of blackcurrant plants (Ribes nigrum) were harvested and directly frozen in liquid nitrogen. After tissue homogenization to produce a fine powder, samples were thawed and immediately mixed with sodium chloride solution. Following centrifugation, the supernatant was transferred into a headspace glass vial containing sodium chloride. VOCs were then extracted using a solid-phase microextraction (SPME) fiber and a gas chromatograph coupled to an ion trap mass spectrometer. Volatile quantification was performed on the resulting ion chromatograms by integrating peak area, using a specific m/z ion for each VOC. Correct VOC annotation was confirmed by comparing retention times and mass spectra of pure commercial standards run under the same conditions as the samples. More than 60 VOCs were identified in ripe blackcurrant fruits grown in contrasting European locations. Among the identified VOCs, key aroma compounds, such as terpenoids and C6 volatiles, can be used as biomarkers for blackcurrant fruit quality. In addition, advantages and disadvantages of the method are discussed, including prospective improvements. Furthermore, the use of controls for batch correction and minimization of drift intensity have been emphasized.


Flavor is an essential quality trait for any fruit, impacting consumer acceptance and thus significantly affecting marketability. Flavor perception involves a combination of the taste and olfactory systems and depends chemically on the presence and concentration of a wide range of compounds that accumulate in edible plant parts, or in case of VOCs, are emitted by the ripe fruit1,2. While traditional breeding has focused on agronomic traits such as yield and pest resistance, fruit quality trait improvement, including flavor, has long been neglected due to the genetic complexity and the difficulty to properly phenotype these characteristics, leading to consumer discontent3,4. Recent advances in metabolomic platforms have been successful in identifying and quantifying key compounds responsible for fruit taste and aroma5,6,7,8. Furthermore, the combination of metabolite profiling with genomic or transcriptomic tools allows the elucidation of the genetics underlying fruit flavor, which in turn will help breeding programs develop new varieties with enhanced organoleptic characteristics2,4,9,10,11,12,13,14.

Blackcurrant (Ribes nigrum) berries are highly appreciated for their flavor and nutritional properties, being widely cultivated across the temperate zones of Europe, Asia, and New Zealand15. Most of the production is processed for food products and beverages, which are very popular in the Nordic countries, mainly due to the berries' organoleptic properties. The intense color and flavor of the fruit are the result of a combination of anthocyanins, sugars, acids, and VOCs present in the ripe fruits16,17,18. The analysis of blackcurrant volatiles goes back to the 1960s19,20,21. More recently, several studies have focused on blackcurrant VOCs, identifying important compounds for fruit aroma perception and assessing the impact of genotype, environment, or storage and processing conditions on VOC content5,17,18,22,23.

Because of its numerous advantages, the technique of choice for high-throughput volatile profiling is HS-SPME/GC-MS24,25. A silica fiber, coated with a polymeric phase, is mounted on a syringe device, allowing the adsorption of the volatiles in the fiber until an equilibrium phase is reached. Headspace extraction protects the fiber from the nonvolatile compounds present in the matrix24. SPME can successfully isolate a high number of VOCs present at highly variable concentrations (parts per billion to parts per million)25. In addition, it is a solvent-free technique that requires limited sample processing. Other advantages of HS-SPME are the ease of automation and its relatively low cost.

However, its success can be limited, depending on the chemical nature of the VOCs, the extraction protocol (including time, temperature, and salt concentration), sample stability, and the availability of sufficient fruit tissue26,27. This paper presents a protocol for blackcurrant VOCs isolated by HS-SPME and analyzed by gas chromatography coupled with an ion trap mass spectrometer. A balance between the quantity of plant material, sample stability, and duration of extraction and chromatography was achieved to be able to process high numbers of blackcurrant samples, some of them presented in this study. In particular, VOC profiles and/or chromatograms of five cultivars ('Andega', 'Ben Tron', 'Ben Gairn', 'Ben Tirran', and 'Tihope') will be presented and discussed as example data. Furthermore, the same protocol has been successfully put into practice for VOC measurement in other fruit berry species such as strawberry (Fragaria x ananassa), raspberry (Rubusidaeus), and blueberry (Vaccinium spp.).

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1. Fruit harvesting

  1. Grow between 4 to 6 plants per genotype and/or treatment to ensure sufficient fruit material and variability.
  2. If possible, harvest the samples on the same date; if there is not enough fruit material, pool together samples harvested on different dates.
    NOTE: It is recommended that the harvest time (morning, noon, afternoon) remains approximately identical as VOC profiles are affected by daytime/circadian rhythm28,29,30,31.
  3. Assess fruit ripening stage by visual observation32. Pool fruits from the same ripening stage, as ripening status strongly impacts VOC emission. Discard any damaged or pathogen-infected fruits.
    NOTE: To better assess fruit ripeness, texture analysis can be performed33. In addition, counting days after flowering can be used to ensure that pooled fruits belong to a similar ripening stage.
  4. Include a minimum of 10-15 fruits per biological replicate (3 to 5) for VOC analysis.
    NOTE: Here, three separate pools of 13-20 fruits (biological replicates) of 'Andega', 'Ben Tron', 'Ben Gairn', 'Ben Tirran', and 'Tihope' cultivars were harvested in two locations (Poland and Scotland) in summer 2018 and directly frozen in liquid nitrogen. Samples were then sent to the laboratory and processed as described below.
  5. Once harvested, freeze all fruits immediately in liquid nitrogen, and subsequently store them at -80 °C until processing.
    ​NOTE: If possible, fruits can be directly processed after harvest. In this case, fresh fruits can be homogenized in a mixer, weighed, and directly analyzed (step 3.1 onwards). However, to prevent fruits from further postharvest degradative processes, the fresh material should be stored in a cooler (4 °C) and processed as rapidly as possible. If not properly handled, liquid nitrogen can produce cold burns and can cause asphyxiation in poorly ventilated spaces.

2. Fruit sample and reagent preparation

  1. Grind the fruits into a fine powder, taking care to always keep them frozen with the help of liquid nitrogen. Use a cryogenic mill, bead mill, or a mortar and pestle for homogenization. Precool stainless grinding jars or mortar and pestle with liquid nitrogen to avoid sample thawing.
    NOTE: It is critical to homogenize samples to a fine powder to ensure proper VOC extraction.
  2. Weigh 1 g of frozen material (from step 2.1.) in a 5 mL tube that is previously cooled in liquid nitrogen, and note the exact weight. Keep the material at -80 °C until processing step 3.1.
  3. Include 'reference' or 'control' samples in the analysis to check technical variation, including VOC extraction and HS-SPME/GC-MS performance. For this purpose, pool together a mixture of randomly chosen fruit samples, and include at least one control sample per day for VOC analysis. In addition, use an internal standard, as described in step 2.5., to minimize the impact of intensity drift.
  4. Prepare 20% (w/v) sodium chloride solution in high-performance liquid chromatography (HPLC) grade water (hereafter, referred to as NaCl solution). Dissolve NaCl with the help of a magnetic stirrer; ensure the availability of 1 mL of the solution per sample.
  5. Prepare a 1 ppm solution in HPLC grade methanol of N-pentadecane (D32, 98%) from pure commercial standard (hereafter, referred to as the internal standard).
    NOTE: N-pentadecane-d32 will be used as an internal standard, and 5 µL per sample will be needed. Methanol should be manipulated under a fume hood.
  6. Prepare 1 ppm solutions in HPLC grade methanol of pure commercial standards for VOC identification (see Table 1 for the list of commercial standards used in this study).
  7. Prepare 10 mL screw-cap headspace vials by adding 0.5 g NaCl in each needed vial. Ensure that screw caps include a septum composed of a soft material, i.e., silicone, with a thin polytetrafluoroethylene film on the inner side, to avoid contamination.

3. Sample preparation

  1. Add 1 mL of NaCl solution to the 5 mL tube containing the weighed frozen sample. Shake the tube until the sample is completely thawed and homogenized.
  2. Centrifuge at 5000 × g for 5 min at room temperature.
  3. Transfer the supernatant with a 1000 µL pipette tip to the NaCl-containing headspace vial. Cut the end of the tip to facilitate this process.
  4. Add 5 µL of internal standard to each sample-containing headspace vial.

4. HS-SPME/GC-MS data acquisition

  1. Place the closed headspace vial in a GC-MS autosampler at room temperature, for an automated HS-SPME/GC-MS run, which is described in section 4. Do not place biological replicates in successive positions in the autosampler; instead, randomly distribute them to minimize the impact of intensity drift.
    NOTE: Approximately 10-12 vials can be placed at once in the autosampler, without affecting sample stability.
  2. Preincubate the headspace vials 10 min at 50 °C with agitation at 17 x g.
  3. Insert an SPME device into the vial to expose the fiber to the headspace for VOC extraction for 30 min at 50 °C with agitation at 17 x g.
  4. Introduce the fiber into the injection port for 1 min at 250 °C in splitless mode for volatile desorption.
  5. Clean the fiber in an SPME cleaning station with nitrogen (1 bar N2, ≥ 99.8% pure) for 5 min at 250 °C. Reuse the fiber approximately 100x.
  6. Analyze VOCs with a gas chromatograph coupled to an ion trap mass spectrometer (see the Table of Materials), and perform chromatography under a constant flow of helium (He ≥ 99.9999% purity) of 1 mL/min, with a column that has dimensions of 60 m x 0.25 mm x 1 μm thickness. Use an oven temperature program that is isothermal at 40 °C for 3 min, followed by an 8 °C/min ramp to 250 °C and holding at 250 °C for 5 min. For mass spectrometry, set the transfer line and ion source temperatures to 260 °C and 230 °C, respectively. Set the ionization energy to 70 eV and the recorded mass range to m/z 35-220 at 6 scans per s.
  7. Extract and analyze 1 ppm solutions of commercial standards as described above. In addition, run a mixture containing all the diluted commercial standards mixed with 300 µL NaCl solution and 900 µL HPLC grade water before sample data acquisition to check the correct calibration of the equipment. Furthermore, include a blank sample containing NaCl solution alone in every batch.

5. Analysis of GC-MS profile chromatograms: VOC identification and semi-quantification

  1. Open raw GC-MS profile files with the software provided by the manufacturer. To identify compounds, compare their retention times and mass spectra and Kovats linear retention indices determined from the chromatograms of the samples with retention indices obtained from authentic standards. For each commercial standard, annotate retention time and the most abundant m/z ions. Then, select a specific m/z ion for each VOC (Table 1).
  2. Automatically integrate VOC peaks based on standard retention times and chosen m/z ions of the selected GC-MS raw files. For this, provide a list for each VOC with retention time and selected m/z ion. Although the software automatically integrates peak area corresponding to the same retention time and m/z ion as provided in the sequence setup, check the correct integration of each peak and correct it manually if necessary.
  3. Calculate the peak area of each VOC relative to that of the internal standard to minimize instrumental variation and intensity drift.
    NOTE: When analyzing fruit from different genotypes or growth and storage conditions, it is highly recommended to determine the VOC content relative to the fruit dry weight content to rule out dilution effects due to differences in water content.
  4. For batch effect correction, normalize the VOC peak area of each sample to the corresponding peak area in the control sample analyzed in the same run.
    NOTE: A relative VOC quantification is obtained; however, for the purpose of the experiment, VOC content can be then determined relative to any sample (e.g., untreated fruits to compare the effect of storage upon VOC levels).

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Representative Results

High-throughput VOC profiling in a large set of fruit crops grown under different conditions or locations or belonging to distinct genotypes is necessary for accurate aroma phenotyping. Here, a fast and semi-automated HS-SPME/GC-MS platform for relative VOC quantification in blackcurrant cultivars is presented. VOC detection and identification were based on a library that was developed to profile berry fruit species (Table 1). A typical ripe blackcurrant fruit volatile profile (total ion chromatogram) obtained by HS-SPME/GC-MS in the aforementioned conditions is shown in Figure 1A. In total, 63 VOCs were identified, belonging to several chemical classes, the majority being esters (27), aldehydes (12), alcohols (8), ketones (7), terpenes (5), and furans (3) (Table 1).

Terpenoid compounds, esters, and C6 compounds have been described to dominate the blackcurrant volatilome and to be important for the aroma of the fresh fruit5,17. In agreement with these previous studies, some of the most abundant peaks observed in Figure 1A correspond to two monoterpenes (linalool and terpineol) and two C6 compounds ((E)-2-hexenal and (Z)-3-hexenal). Example mass spectra obtained from blackcurrant profiles and their comparison with spectra of pure commercial standards are shown for (E)-2-hexenal and terpineol in Figure 1B and Figure 1C, respectively.

Figure 1
Figure 1: Representative chromatograms from ripe blackcurrant fruit obtained by HS-SPME/GC-MS (from 'Andega' cultivar). (A) Total ion chromatogram. (Z)-3-hexenal (Retention time 14.33 min), (E)-2-hexenal (15.86 min), linalool (21.65 min), and terpineol (24.01 min) peaks are indicated with the numbers 1, 2, 3, and 4, respectively. (B) Mass spectrum corresponding to (E)-2-hexenal peak from a blackcurrant profile and comparison with a pure commercial standard. (C) Mass spectrum corresponding to terpineol peak from a blackcurrant profile and comparison with a pure commercial standard. Abbreviation: HS-SPME/GC-MS = headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry. Please click here to view a larger version of this figure.

While terpenes have been depicted to be indicators of blackcurrant fruit freshness, C6 compounds are known as 'green leaf volatiles', imparting 'green' notes to fruit and vegetable aroma34. Thus, the semi-quantification of these VOCs emitted by ripe fruits of different blackcurrant varieties can be the first step in improving flavor-related traits. Furthermore, as the environment and plant growth conditions strongly impact fruit VOC content, which is one of the main drawbacks for aroma breeding, one of the objectives of this study was to validate the hypothesis that the semi-quantification of the identified VOCs in the same cultivars ('Ben Tron', 'Ben Gairn', 'Ben Tirran', and 'Tihope') was reproducible in diametrically opposed European locations such as Poland and Scotland. As expected, principal component analysis (PCA) of the VOC profiles of four different blackcurrant cultivars showed that the environment strongly impacts volatile content, as principal component (PC) 1 separates samples based on their location (Figure 2). However, the effect of genotype can be observed with PC2, as 'Ben Tirran' is clearly separated from the remaining cultivars (Figure 2).

Figure 3 shows the relative content of linalool and (E)-2-hexenal in the four assessed blackcurrant cultivars. For both locations, VOC content was normalized to the same control sample, for which the semi-quantification confirmed that linalool content was generally higher in Poland than in Scotland, whereas (E)-2-hexenal shows the opposite trend (Figure 3). This result demonstrates the environmental impact on VOC content in blackcurrant fruits, although the proportion of the two volatiles present in the four assessed cultivars was constant, with 'Ben Tirran' and 'Ben Tron' cultivars showing the highest amounts of linalool and (E)-2-hexenal, respectively (Figure 3). Taken together, these results indicate that the proposed method is valid to phenotype VOC content, and combined with genetic approaches, may be used for the purpose of fruit quality breeding.

Figure 2
Figure 2: PCA to assess the variance among VOC profiles in the four blackcurrant cultivars grown in Poland and Scotland. PC1 (environment) explains 46.2% of the variability, while PC2 (genotype) contributes 24.8% of the variance in the dataset. Abbreviations: PCA = principal component analysis; PC1 = first principal component; PC2 = second principal component; VOC = volatile organic compound. Please click here to view a larger version of this figure.

Figure 3
Figure 3: Relative content of two representative VOCs in blackcurrant aroma profiles-linalool and (E)-2-hexenal, harvested in Scotland and Poland. Four different blackcurrant cultivars were assessed ('Ben Gairn', 'Ben Tirran', 'Ben Tron', and 'Tihope'). The bars represent the mean values of two biological replicates, and error bars represent the standard deviation. Statistical comparisons were performed by one-way ANOVA followed by Tukey's post-hoc test to determine significant differences in VOC content between cultivars and countries. For VOC contents with the same lowercase letters (a, ab, b), no significant differences were observed at P < 0.05. Abbreviations: VOCs = volatile organic compounds; ANOVA = analysis of variance. Please click here to view a larger version of this figure.

Table 1: List of VOCs identified by HS-SPME/GC-MS in blackcurrant fruits. Retention time (min), selected m/z ion for VOC identification and semi-quantification, aroma description, chemical class and formula, and CAS number are indicated. Abbreviations: HS-SPME/GC-MS = headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry; VOCs = volatile organic compounds; KRI = Kovats retention index; CAS number = Chemical Abstracts Service registry number. Please click here to download this Table.

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Breeding for fruit aroma has long been hindered by the complex genetics and biochemistry underlying the synthesis of volatile compounds and the lack of technologies for proper phenotyping. However, recent advances in metabolomic platforms, combined with genomic tools, are finally allowing the identification of the metabolites responsible for consumer preferences and to breed crops with improved flavor3. While most progress has been achieved in the model fruit, tomato9,10, similar results could be achieved in other economically relevant crop species such as strawberry, apple, or blueberry2,12,35,36.

This paper presents a fast and reproducible HS-SPME/GC-MS-based platform that has been successfully used for measuring VOC content in different berry species, including blackcurrant, a fruit highly appreciated for its delicate flavor and remarkable nutritional value. Compared to previously published methods, the main improvement was achieved by decreasing the total chromatographic run time. Indeed, it was possible to increase the temperature ramp from 5 °C/min to 8 °C/min with adequate resolution, reducing the chromatographic time from 50 min to 35 min (Figure 1A)27. Furthermore, the high amount of NaCl added to the samples (1 mL of 20% NaCl solution + 0.5 g of solid NaCl) seems to positively impact sample stability over time. Indeed, volatile profiles were stable over time, and combined with faster chromatography, allowed the measurement of up to 20-22 samples per day.

The use of an internal standard, such as N-pentadecane-d32, together with a proper distribution of the biological replicates along the run, is necessary to prevent intensity drift37. In addition, control or reference samples must be run at least once per day of analysis for batch correction. Variations between batches are mainly caused by changes in detector sensitivity or by fiber aging27. While this protocol enabled the detection of more than 60 VOCs present in the headspace of ripe blackcurrant fruits, readers must take into account that this number can be easily increased by adding pure commercial standards in the proposed library (Table 1). For example, published studies detected a high number of terpenoid compounds that were not included in this analysis5,17. In this sense, a more blackcurrant-aroma-specific VOC library may be readily put together, if necessary. However, the goal of this study was to adapt a previously established library27 for VOC measurement in different berries, including raspberry, strawberry, and blackcurrant fruits.

It is noteworthy that the protocol presented here has several advantages and disadvantages, like other HS-SPME/GC-MS platforms, which have already been discussed elsewhere25,26,38. While it offers ease of automation, making it the technique of choice when large number of samples are required to be analyzed, its main drawback is its susceptibility to matrix effects38. In addition, special caution should be taken during SPME fiber-coating selection and with sampling conditions depending on the chemical nature of the targeted VOCs25,27. To conclude, a rapid and semi-automated protocol for VOC profiling in berry fruit headspace is presented here and could be easily adapted for use with an increased library size, if required. It is expected that this platform can be adapted to other fruit species and when combined with genomic studies and/or sensory analysis panel will help crop aroma profiling and improvement.

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The authors declare no conflict of interest.


The authors thank the Servicios Centrales de Apoyo a la Investigación from University of Malaga for HS-SPME/GC-MS measurements. We acknowledge the assistance of Sara Fernández-Palacios Campos in volatile quantification. We also thanks GoodBerry´s consortium members for providing the fruit material.


Name Company Catalog Number Comments
10 mL screw top headspace vials Thermo Scientific 10-HSV
18 mm screw cap Silicone/PTFE Thermo Scientific 18-MSC
5 mL Tube with HDPE screw cap VWR 216-0153
Centrifuge Thermo Scientific 75002415
Methanol for HPLC Merck 34860-1L-R
N-pentadecane (D32, 98%) Cambridge Isotope Laboratories DLM-1283-1
Sodium chloride Merck S9888
SPME fiber PDMS/DVB Merck 57345-U
Stainless grinding jars for TissueLyser Qiagen 69985
TissueLyser II Qiagen 85300 Can be subsituted by mortar and pestle or cryogenic mill
Trace GC gas chromatograph-ITQ900 ion trap mass spectrometer Thermo Scientific
Triplus RSH autosampler with automated SPME device Thermo Scientific 1R77010-0450
Water for HPLC Merck 270733-1L
Xcalibur 4.2 SP1 Thermo Scientific software



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Cite this Article

Pott, D. M., Vallarino, J. G., Osorio, S. Profiling Volatile Compounds in Blackcurrant Fruit using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography-Mass Spectrometry. J. Vis. Exp. (172), e62421, doi:10.3791/62421 (2021).More

Pott, D. M., Vallarino, J. G., Osorio, S. Profiling Volatile Compounds in Blackcurrant Fruit using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography-Mass Spectrometry. J. Vis. Exp. (172), e62421, doi:10.3791/62421 (2021).

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