Profiles of Volatile Compounds in Blackcurrant ( Ribes nigrum) Cultivars with a Special Focus on the Influence of Growth Latitude and Weather Conditions.

The volatile profiles of three blackcurrant ( Ribes nigrum L.) cultivars grown in Finland and their responses to growth latitude and weather conditions were studied over an 8 year period by headspace solid-phase microextraction (HS-SPME) followed by gas-chromatographic-mass-spectrometric (GC-MS) analysis. Monoterpene hydrocarbons and oxygenated monoterpenes were the major classes of volatiles. The cultivar 'Melalahti' presented lower contents of volatiles compared with 'Ola' and 'Mortti', which showed very similar compositions. Higher contents of volatiles were found in berries cultivated at the higher latitude (66° 34' N) than in those from the southern location (60° 23' N). Among the meteorological variables, radiation and temperature during the last month before harvest were negatively linked with the volatile content. Storage time had a negative impact on the amount of blackcurrant volatiles.

Introduction

Blackcurrant (Ribes nigrum L.) is widely cultivated across the temperate zone in Europe, with the total annual production of blackcurrant close to 160 000 tons.[1] In countries outside Europe, there has been increasing interest in the cultivation and consumption of blackcurrant and other berries of Ribes species, with New Zealand being among the leading countries in the cultivation and processing of blackcurrants.[2] Various health-promoting properties of blackcurrant have been shown by both traditional use and modern research to be likely due to the high contents of phytochemicals such as phenolic compounds and vitamin C.[3−5] Blackcurrant berries are highly popular in the Nordic countries, where they are appreciated for their flavor and nutritional properties. According to the Natural Resources Institute Finland (Luke), almost 15 000 tons of berries were produced in Finland in 2016, with blackcurrant berries being the third most produced berry (950 tons) after strawberry and raspberry (http://statdb.luke.fi). The composition of blackcurrants has been widely studied in regard to several phytochemicals, such as phenolic compounds,[6,7] carotenoids and phytosterols,[8] and vitamin C,[9] both in fresh berries and in berry-derived products such as juices.[10,11]

Blackcurrant berries are consumed as fresh berries in households, and they are industrially processed into a wide range of products such as juices, jam, jelly, yogurt, and fruit bars. The unique aroma profile is an essential element of the blackcurrant flavor. Several studies on blackcurrant berries[7−9] and one recently study published by Jung et al.[12] have focused on volatile compounds. These studies have been devoted to characterizing the aroma compounds[13] as well as the impact of the cultivar,[14] ripening stage,[9] thermal[15] and enzymatic treatments,[16] and freezing.[12] Altogether, these studies have characterized a vast number of compounds as constituents of the volatilome of blackcurrants, including compounds of various chemical classes such as alcohols, aldehydes, esters, and terpenes. However, to the best of the authors’ knowledge, no research has been reported on the contribution of the harsh Nordic environment and associated meteorological data to the volatile contents and compositions in blackcurrant cultivars.

The influence of environmental conditions on the emitted volatile compounds in plants has been highly examined from a biochemical point of view, especially regarding terpene biosynthesis. The methyl erythritol phosphate pathway (MEP) is known to be responsible for the formation of the basic C5 units of isopentenyl diphosphate (IDP) and dimethylallyl diphosphate (DMADP). In addition to the above-mentioned MEP, in plants, such basic structures are formed via the mevalonic acid pathway (MVA).[17] Although the environmental-stress-induced emission of volatiles has been widely reported, it is not clear which ecological actions should be attributed to volatile terpenes. Moreover, in fruits grown under conventional agricultural practices, the influence of environmental conditions on the volatiles has been scarcely studied. It has been reported that sunlight and UV exposure in grapes affect terpene alcohols, C13-norisoprenoids, and other volatiles in wine, depending on the compound.[18] The volatile composition of strawberry cultivars was found to be more dependent on the genotype than on the environmental conditions.[19] UV-C preharvest treatment of strawberry showed no significant effects on any of the volatiles.[20] Finally, it has been reported that the volatile compositions of essential oils from aromatic plants are extremely dependent on the weather conditions, although the effects are not clearly stated.[21]

In the current research, we aim to study the volatile compositions of three commercial, Nordic blackcurrant berry cultivars, ‘Mortti’, ‘Ola’, and ‘Melalahti’, and their variations according to the growth latitude and the weather conditions in open test fields in southern and northern Finland. The study samples included berries harvested annually during 2010–2017 from two latitudes.

Headspace (HS) solid-phase microextraction (SPME) coupled with gas chromatography–mass spectrometry (GC-MS) was used as a reliable technique to sample the volatile fractions of complex matrices because of its high throughput and the possibility of automation. A large data set was produced from the GC-MS analyses and used for the identification and quantitation of the volatile compounds. Further data analysis was carried out with multivariate techniques to classify the samples and to detect the association between cultivars, growth latitudes, and environmental factors and specific metabolite profiles.

This study is a subproject of an ongoing larger study, in which we are investigating the impact of growth latitude and environmental factors on the metabolomic profiles of berry crops using blackcurrant as one of the model species. Hence, the research will produce new information on volatile compounds to add to our previous research on the impact of growth environment on the composition and quality of blackcurrants.

Materials and Methods

Blackcurrant Samples

Three cultivars of blackcurrant (Ribes nigrum L.), ‘Mortti’, ‘Ola’, and ‘Melalahti’, were cultivated by MTT Agrifood Research Finland, Natural Resources Institute Finland (Luke), who applied identical farming practices in Piikkiö, Kaarina, southern Finland (latitude 60° 23′ N, longitude 22° 33′ E, altitude 5–15 m) and Apukka, Rovaniemi, northern Finland (66° 34′ N, 26° 01′ E, 100–105 m). ‘Melalahti’ is an old local cultivar from Paltamo, northern Finland.[22] ‘Mortti’ is a crossing of ‘Öjebyn’ (from Sweden) × ‘Wellington XXX’ (from Great Britain),[23] and ‘Ola’ is a crossing of ‘Wellington XXX’ × ‘Lepaan Musta’ (from Finland).[24] Both ‘Mortti’ and ‘Ola’ were developed in Finland. Twelve bushes of each cultivar were planted in four field blocks in May of 2002. A ditch, 10 cm deep and 20 cm wide, was plowed through every block. The ditches were filled with white Sphagnum peat (pH 6) mixed with 8 kg of dolomite lime and 1.5 kg·m–3 NPK basic fertilizer. The seedlings were planted, and the peat was covered with the local fine-sand soil. Little irrigation was applied during the study period, and fertilization and other growing methods were carried out according to Finnish standard guidelines.[22] The berries were harvested in quadruplicate, one sample (ca. 500 g) from each of the four field blocks, from both southern and northern Finland in consecutive years from 2010 to 2017. No berries were collected in 2015 from either location, and in 2014 and 2016 no berries were collected from Apukka (N). The berries were picked when optimally ripe for harvesting as defined by experienced horticulturists. This was based on sensory evaluation of the intensity of the surface color, the flavor having the typical sweetness-acidity ratio, the aroma, and the firmness of the berries. The berries were frozen and stored at −20 °C immediately after harvesting until being analyzed.

Chemicals and Reagents

Hexanal, nonanal, undecane, α-pinene, camphene, β-pinene, myrcene, α-phellandrene, δ-3-carene, α-terpinene, p-cymene, limonene, cis-β-ocimene, trans-β-ocimene, γ-terpinene, terpinolene, eucalyptol, terpinen-4-ol, bornyl acetate, terpinyl acetate, β-caryophyllene, n-nonane, neryl acetate, and a homologous series of n-alkanes (C9–C30) of analytical purity were purchased from Sigma-Aldrich (St. Louis, MO). Glucose, fructose, and sucrose were obtained from Merck (Darmstadt, Germany), citric acid was obtained from J.T. Baker (Deventer, The Netherlands), and pectin was obtained from Herbstreith & Fox KG (Neuenburg, Germany). Sodium chloride (99% purity) was from Sigma-Aldrich. Methanol and acetone (HPLC grade) were purchased from J.T. Baker. The aforementioned sugars, organic acids, and pectin were employed to prepare a synthetic blackcurrant juice containing no volatiles following the composition detailed in Food Composition and Nutrition Tables.[25]

HS-SPME-GC-MS Profiling

Frozen berries were thawed overnight at 4 °C. Next, 50 g were homogenized in 50 mL of H2O saturated with sodium chloride with a Bamix mixer (Bamix M13, Mettlen, Switzerland). Water was added to help the homogenization process, and sodium chloride had an effect in reducing enzymatic activity, thus helping preserve the samples from enzymatic degradation. Furthermore, salts have an enhancing effect on the extraction efficiency of volatile compounds as a result of the salting-out effect. From the slurry, 2 g was transferred to a 20 mL headspace vial and spiked with 10 μL of the internal-standard mixture containing 250 μg·mL–1n-nonane and 100 μg·mL–1 neryl acetate. The internal standards fulfilled three criteria: (1) they were not initially present in the samples, (2) their retention times were free of possible coelutions, (3) they proved to be robust and stable for use in a long sample sequence.

Collection of the volatiles by HS-SPME was carried out with a 2 cm SPME fiber of carboxen/polydimethylsiloxane/divinylbenzene (CAR/PDMS/DVB, 50/30 μm) from Supelco (Bellefonte, PA) at 45 °C for 30 min under agitation, and a TriPlus RSH multipurpose autosampler (Thermo Scientific, Reinach, Switzerland) was employed.

After extraction, GC-MS analyses were performed with a Trace 1310 (Thermo Scientific) gas chromatograph coupled to a TSQ 8000 EVO mass spectrometer (Thermo Scientific). Volatiles were desorbed from the fiber into the injection port equipped with an SPME liner at 240 °C for 3 min. Compounds were separated with a DB-5MS column (30 m × 0.25 mm i.d. × 0.25 μm film thickness) from Agilent (Palo Alto, CA) using helium as the carrier gas (1.2 mL·min–1). The oven temperature was programmed to increase from 40 °C (held for 1 min) to 160 °C at 5 °C·min–1, then to rise to 240 °C at 12 °C·min–1 (held for 1 min). Mass spectra were recorded in electron-impact (EI) mode at 70 eV within the mass range m/z 40–300. The transfer line and the ionization source were thermostated at 250 and 220 °C, respectively. The system was operated using Xcalibur 4.0 (Thermo Scientific). All analyses were carried out in triplicate.

Identification of volatile compounds was based on authentic reference compounds, when available. Tentative identifications were based on comparison of the experimental mass spectra with those of the Wiley 7 and Essential Oils mass spectral libraries (Wiley, New York, NY). The identifications were then further confirmed by linear retention indices (RI) calculated using an n-alkane mixture (C9–C30);[26] these were then compared to those reported in Adams’s database[27] and the NIST Chemistry WebBook.[28]

TraceFinder 4.1. (Thermo Scientific) was used to carry out peak detection and integration by using an extracted ion for each detected compound (Table ). Areas obtained were then normalized using n-nonane from the internal-standard mixture to correct any possible analytical deviation caused by variations in the performance of the fiber and instrumentation. Normalized area values were further used for statistical and multivariate data analysis. On the other hand, neryl acetate was used to check the repeatability and response of n-nonane in consecutive analyses. The results showed a relatively constant ratio between n-nonane and neryl acetate, with a relative standard deviation (% RSD) of 13% when calculating the interday repeatability (n = 12).

Table 1

Volatiles Detected by HS-SPME-GC-MS Profiling of the Blackcurrant Samples

peak no.	compound	code	RICala	RILitb	quantification ion (m/z)	identification criteria
Esters
1	methyl 2-methylbutanoate	E1	793	780	88	MS, RIc
3	ethyl butanoate	E2	808	805	88	MS, RI
4	ethyl isovalerate	E3	860	858	88	MS, RI
26	methyl benzoate	E4	1102	1102	105	STDd
Aldehydes
2	hexanal	Ad1	804	800	82	STD
27	nonanal	Ad2	1108	1102	98	STD
Alkanes
25	undecane	H1	1100	1100	156	STD
Internal Standards
5	n-nonane	IS1	900	900	128
42	neryl acetate	IS2	1369	1362	121
Nonoxygenated Monoterpenes
6	α-thujene	MT1	926	930	93	MS, RI
7	α-pinene	MT2	931	939	93	STD
8	camphene	MT3	946	953	93	STD
9	verbenene	MT4	970	968	119	MS, RI
10	β-pinenee	MT5	975	979	93	STD
11	myrcene	MT6	993	991	93	STD
12	pseudo-limonene	MT7	1001	1004	93	MS, RI
13	α-phellandrene	MT8	1006	1003	93	STD
14	δ-3-carene	MT9	1010	1010	93	STD
15	α-terpinene	MT10	1017	1017	121	STD
16	o-cymene	MT11	1022	1020	119	MS, RI
17	p-cymene	MT12	1025	1026	119	STD
18	limonene	MT13	1028	1029	93	STD
20	cis-β-ocimene	MT14	1039	1040	93	STD
21	trans-β-ocimene	MT15	1050	1050	93	STD
22	terpenoid (MW = 136)	MT16	1057	—	93	MS
23	γ-terpinene	MT17	1064	1060	93	STD
24	terpinolene	MT18	1089	1089	136	STD
Oxygenated Monoterpenes
19	eucalyptol	OMT1	1034	1031	154	STD
28	cis-rose oxide	OMT2	1112	1108	139	MS, RI
29	campholenal	OMT3	1132	1126	108	MS, RI
30	borneol	OMT4	1162	1169	95	MS, RI
31	pinocarvone	OMT5	1169	1164	108	MS, RI
32	terpinen-4-ol	OMT6	1183	1182	136	STD
33	p-cymen-8-ol	OMT7	1188	1183	135	MS, RI
34	p-cymen-9-ol	OMT8	1193	1200	135	MS, RI
35	α-terpineol	OMT9	1198	1192	136	MS, RI
36	myrtenol	OMT10	1200	1196	107	MS, RI
37	cumaldehyde	OMT11	1248	1242	133	MS, RI
38	bornyl acetate	OMT12	1290	1289	136	STD
39	terpinen-4-ol acetate	OMT13	1338	1340	93	MS, RI
40	terpinyl acetate	OMT14	1355	1350	121	STD
41	citronellyl acetate	OMT15	1358	1353	123	MS, RI
Sesquiterpenes
43	β-caryophyllene	ST1	1422	1419	133	STD
44	α-caryophyllene	ST2	1456	1455	93	MS, RI

Retention indices (RI) calculated according to the Van den Dool and Kratz equation.[41]

RI from the literature.

Tentatively identified by comparison of the mass-spectral (MS) and retention-index (RI) data with those from databases.

Identified on the basis of comparison of the GC and mass spectra with those of the reference compounds.

In the cultivar ‘Melalahti’, β-pinene coelutes with sabinene.

Quantitation of the main volatiles in the 2017 samples was carried out with response factors (RF, Supplementary Table 1), which were calculated by spiking the individual standard reference compounds together with the internal standards at the same concentration. To simulate real blackcurrant samples, a synthetic blackcurrant juice with no initial content of volatiles was prepared following the composition detailed in ref (25) (2.4 g of glucose, 3.2 g of fructose, 0.6 g of sucrose, 2.35 g of citric acid, 2.0 g of cellulose, and 1.7 g of pectin in 100 mL of water). This was used to calculate the response factors of the commercial standard volatiles respective to the internal standard in a volatile-free matrix.

Meteorological Data

Meteorological data from meteorological stations in Piikkiö, Kaarina (latitude 60° 23′ N, longitude 22° 33′ E, altitude 6 m) and Rovaniemi Airport (66° 33′ N, 25° 50′ E, 195 m) from 2010 to 2017 were provided by the Finnish Meteorological Institute (Helsinki, Finland). The data provided included the following weather parameters: daily values of the maximum, minimum, and average temperatures (°C), precipitation (mm), relative humidity (%), and global radiation (kJ·m2). The weather variables and the corresponding abbreviations used in this study are shown in Table . Complete weather data can be found in Supplementary Table 2.

Table 2

Weather Variables and the Corresponding Abbreviations Used in the Study

abbreviation	weather variable	abbreviation	weather variable
∑Tgh	temperature sum over 5 °C in the growth season (°C)	DHu20to30gh	percentage of the days with relative humidity of 20–30% from the start of the growth season until the day of harvest (%)
∑Tmon	temperature sum over 5 °C in the last month of the growth season (°C)	DHu30to40gh	percentage of the days with relative humidity of 30–40% from the start of the growth season until the day of harvest (%)
HDgh	hot days (temperature >25 °C) from the start of the growth season until the day of harvest (days)	DHu40to50gh	percentage of the days with relative humidity of 40–50% from the start of the growth season until the day of harvest (%)
HDmon	hot days (temperature >25 °C) in the last month before harvest (days)	DHu50to60gh	percentage of the days with relative humidity of 50–60% from the start of the growth season until the day of harvest (%)
Tmon	average temperature in the last month before harvest	DHu60to70gh	percentage of the days with relative humidity of 60–70% from the start of the growth season until the day of harvest (%)
Tw	average temperature in the last week before harvest (°C)	DHu70to80gh	percentage of the days with relative humidity of 70–80% from the start of the growth season until the day of harvest (%)
ΔTmon	mean daily temperature difference in the last month	DHu80to90gh	percentage of the days with relative humidity of 80–90% from the start of the growth season until the day of harvest (%)
MinTmon	minimum temperature of the last month	DHu90to100gh	percentage of the days with relative humidity of 90–100% from the start of the growth season until the day of harvest (%)
LoTmon	lowest daily temperature average of the last month	DHu<70gh	percentage of the days with relative humidity below 70% from the start of the growth season until the day of harvest (%)
MaxTmon	highest temperature of the last month	DHu>70gh	percentage of the days with relative humidity above 70% from the start of the growth season until the day of harvest (%)
HiTmon	highest daily average temperature of the last month	DHu20to30m	percentage of the days with relative humidity of 20–30% in the last month before harvest (%)
∑Rgh	radiation sum from the start of the growth season until the day of harvest	DHu30to40m	percentage of the days with relative humidity of 30–40% in the last month before harvest (%)
∑Rmon	radiation sum from the start of the last month until the day of harvest	DHu40to50m	percentage of the days with relative humidity of 40–50% in the last month before harvest (%)
∑Rw	radiation sum from the start of the last week until the day of harvest	DHu50to60m	percentage of the days with relative humidity of 50–60% in the last month before harvest (%)
Pregh	precipitation sum from the start of the growth season until the day of harvest	DHu60to70m	percentage of the days with relative humidity of 60–70% in the last month before harvest (%)
Premon	precipitation sum from the start of the last month until the day of harvest	DHu70to80m	percentage of the days with relative humidity of 70–80% in the last month before harvest (%)
PreW	precipitation sum from the start of the last week until the day of harvest	DHu80to90m	percentage of the days with relative humidity of 80–90% in the last month before harvest (%)
Hugh	average humidity from the start of the growth season until the day of harvest	DHu90to100m	percentage of the days with relative humidity of 90–100% in the last month before harvest (%)
Humon	average humidity for the last month before harvest
Huw	average humidity for the last week before harvest

Statistical Analyses

Univariate analyses were carried out by using SPSS 16.0.1 (SPSS Inc., Chicago, IL). Differences between groups were assessed with one-way analysis of variance (ANOVA) for normally distributed variables and with Tukey’s HSD test or the Kruskal–Wallis test for multiple comparisons for nonparametric variables. Statistical significance was set at p < 0.01. For the comparisons between the samples grown at the two latitudes, t tests (or Mann–Whitney tests for nonparametric variables) at a confidence interval of 99% were considered as being statistically different.

Multivariate analyses were performed by using the SIMCA15 software package (Umetrics, Umeå, Sweden). The data sets were scaled (unit variance (UV) or Pareto) prior to multivariate analysis by principal component analysis (PCA) or partial-least-squares discriminant analysis (PLS-DA). PCA is an unsupervised technique that reduces the dimensionality of the data set but retains the maximum amount of variability.[29] PLS-DA is a supervised method that focuses on class separation. The variable-influence-on-projection (VIP) values indicate the major compounds contributing to the separation of each sample in the PLS-DA scores plots. The VIP value is a weighted sum of squares of the PLS-DA weights that takes the explained Y variance in each dimension into account.[30] The PLS-DA models were validated with permutation tests.

Results and Discussion

HS-SPME-GC-MS Analyses of Volatile Profiles

HS-SPME conditions were optimized to achieve optimum analytical performance. In this regard, sample amount (0.5–4 g), pre-equilibrium time (5–20 min), extraction time (20–50 min), extraction temperature (35–60 °C), and desorption time (1–3 min) were assessed (data not shown) as in a previous work.[31] Optimum HS-SPME conditions were selected on the basis of the total area of detected volatiles, leading to a 2 g sample amount, a pre-equilibrium time of 10 min, an extraction time of 30 min, an extraction temperature of 45 °C, and a desorption time of 3 min.

The volatile compositions of the berries of the blackcurrant cultivars were determined by sampling the compounds on a 2 cm CAR/PDMS/DVB fiber followed by GC-MS analysis. The chromatographic profiles obtained from the berries of all three blackcurrant cultivars picked in 2017 in Piikkiö (S) and Apukka (N) are shown as an example in Supplementary Figure 1. In total, 41 compounds were detected and quantified in the samples. A list of the detected compounds and the basis for the identification are given in Table . The relative proportions of the 41 detected compounds in the berries of the three cultivars grown in the southern and northern locations for all the study years are listed in the Supplementary Table 3.

Initial inspection of the volatile headspace composition revealed terpenoids clearly dominating the chromatographic profile. Monoterpenoids were the most abundant compounds. Nonoxygenated monoterpenes accounted for 19 compounds, and the oxygenated ones for 15 compounds, although the relative abundance of the latter group was much lower than that of the former. The so-called oxygenated monoterpenes included several volatiles not previously detected in blackcurrant samples, such as campholenal; p-cymen-9-ol; cumaldehyde; and two degradation products of the α-pinene-degradation pathway, namely, pinocarvone and myrtenol. This quantitative difference was significantly reinforced by the higher distribution of the hydrophobic monoterpene hydrocarbons in the gas phase compared with that of the oxygenated counterparts. The only sesquiterpenes found in the headspace, existing in each of the blackcurrant samples analyzed, were α- and β-caryophyllene. This does not, however, exclude the commonly known presence of other sesquiterpenes in blackcurrant berries.

Regarding the nonterpenoid compounds, four esters, two aldehydes (hexanal and nonanal), and one alkane (undecane) were detected. The compositional differences among the samples highlighted the different abundances of volatile compounds rather than the presence of different compounds. These results are in agreement with another study, in which frozen blackcurrant berries were analyzed, and it was stated that proportions of terpenes are not significantly affected by freezing at −20 °C from picking until analysis.[14] It has been reported that terpenes are the most representative group of compounds in the volatile profile of blackcurrant berries.[16] Terpenoids are reported to be reliable indicators of the fruit freshness, maturity, and botanical and geographical origin, as well as quality and authenticity.[32] On the other hand, a recently published study by Jung et al.[12] reported a high abundance of C6-compounds and esters in blackcurrant samples grown in southern Germany and Austria when samples were freshly analyzed, which decrease in favor of terpenoids upon storage at −20 °C for three months.[12] This might explain the low number of aldehydes detected in our samples, as can be seen in Table . However, it needs to be taken into consideration that the presence and abundance of these compounds may also be significantly dependent on the cultivar, the growth site, and the stage of ripeness. It is important to notice that the storage of several years may have significantly affected the composition of the volatiles. However, all the berries of same age were treated the same way, which makes the statistical comparison relevant.

Comparison of the Volatile Profiles of the ‘Mortti’, ‘Ola’, and ‘Melalahti’ Cultivars

The whole data set of profiles obtained was submitted to principal-component analysis (PCA) to explore possible compositional differences among the three Finnish cultivars under study. The data set was mean-centered, Pareto-scaled, and standardized with the standard deviation. The PCA model showed excellent goodness-of-fit (R2X(cum) = 0.90) and predictive ability (Q2(cum) = 0.85). The scores plot of PC1 and PC2 in Figure A shows a clear separation between samples of the ‘Melalahti’ cultivar and those of ‘Mortti’ and ‘Ola’, the latter two being grouped together in the plot. A similar phenomenon was already described by Zheng et al. when analyzing the phenolic compounds, acids, and sugars of the same cultivars.[7] That previous work pointed out that the phenolic composition did not differ significantly between the cultivars ‘Mortti’ and ‘Ola’, whereas ‘Melalahti’ presented significantly lower contents of phenolics. The loading plot (Figure B) indicates that the cultivars ‘Mortti’ and ‘Ola’ were richer in volatiles compared with the cultivar ‘Melalahti’. In addition, multiple comparisons were carried out by means of ANOVA and Tukey’s HSD test or by the Kruskal–Wallis test when the variables did not show a normal distribution. The obtained results revealed that most of the compounds showed statistically different abundances (p < 0.01) between ‘Melalahti’ and ‘Mortti’, being in most cases higher for ‘Mortti’, with the only exceptions of ethyl butanoate, α-thujene, and γ-terpinene. In contrast, the abundances of a few compounds were not found to be statistically different (p > 0.01): hexanal and nonanal (i.e., autoxidation products of linoleic acid and oleic acid), undecane, ethyl isovalerate, eucalyptol, verbenene, and α-terpinene. Similar results were observed when comparing ‘Melalahti’ and ‘Ola’, with the only addition of methyl 2-methylbutanoate to the group of compounds not significantly differing (p > 0.01) in abundance between the cultivars. On the other hand, no statistically significant differences were found when comparing the ‘Ola’ and ‘Mortti’ cultivars.

Figure 1

PCA of blackcurrant samples. (A) Scores plot of ‘Melalahti’ (blue squares), ‘Mortti’ (red circles), and ‘Ola’ (black triangles); 1, 2, 3, and 4: samples collected from field blocks 1, 2, 3, and 4, respectively; S: southern Finland (Piikkiö); N: northern Finland (Apukka); 10, 11, 12, 13, 14, 16, and 17: samples collected in 2010, 2011, 2012, 2013, 2014, 2016, and 2017, respectively. (B) Loadings plot. Compounds are coded according to Table .

Another trend observed in the PCA scores plot (Figure A) was the influence of the storage time on the volatile composition. In this regard, the samples of 2017, although analyzed after frozen storage, presented higher contents of volatiles compared with the samples collected in the previous years. The impact of freezing on the volatile composition of blackcurrant was shown by Jung et al. to be especially remarkable during the first 3 months of storage, whereas the composition was close to constant from that point onward.[12]Figure depicts the total volatile contents in regard to storage time (i.e., total volatiles, Figure A; hydrocarbons, Figure B; and oxygenated monoterpenes, Figure C). This effect is more apparent in ‘Mortti’ and ‘Ola’ than in ‘Melalahti’. In addition, berries grown in the North present this effect to a higher extent. Compared with monoterpene hydrocarbons, the relative changes in oxygenated monoterpenes were less extreme and more random, evidently because of their lower volatility and lower permeability through the cuticular membrane. This gradual decrease of volatiles during storage is one of the sources of deviation when calculating the effects of weather conditions, which makes the differences between samples smaller. The present results and those released by Zheng et al.[7] show the compositional similarities of ‘Ola’ and ‘Mortti’. Both of them have the ‘Wellington XXX’ background.

Figure 2

Volatile contents in respect to storage time. (A) Total volatiles, (B) nonoxygenated monoterpenes, and (C) oxygenated monoterpenes for ‘Melalahti’ (squares), ‘Mortti’ (circles), and ‘Ola’ (triangles) grown in the North (red) and South (blue).

The weights of individual berries were not measured in this research. In the earlier studies, however, the berry weights of the studied cultivars have been shown to be rather alike: Lehmushovi[24] reported the berry weight of ‘Ola’ to be only slightly lower than that of the standard cultivar ‘Öjebyn’, whereas Mattila et al.[33] did not find differences in the average berry weights of the cultivars ‘Öjebyn’, ‘Mortti’, and ‘Melalahti’.

Regarding the comparison of southern and northern samples, it was not feasible to draw any conclusions from the PCA plot, as samples were not separated on this basis in the scatter plot. For this purpose, a supervised multivariate technique, such as PLS-DA, was of utmost importance.

Effect of Growth Latitude on Volatile Composition

The qualitative composition of volatiles was found to be the same in berries from the northern (Apukka) and southern (Piikkiö) orchards. A possible explanation for this fact is that the biosynthesis pathways of the volatiles are primarily determined by the genotype, whereas the quantities of these compounds show a certain dependency on the environmental factors intimately linked with the growth location.

PLS-DA was applied to classify samples between different growth latitudes. Three separate models, one for each cultivar, were created to investigate which volatile compounds were responsible for the compositional differences resulting from growth in the southern and northern locations. The PLS-DA scores plot (t[3] vs t[2]) showed an excellent discrimination between the berries grown in the northern and southern locations for the ‘Mortti’ (Figure A) and ‘Ola’ (Figure C) cultivars. For the ‘Mortti’ cultivars, the model parameters were R2X(cum) = 0.98, R2Y(cum) = 0.91, and Q2(cum) = 0.72, whereas for ‘Ola’, the corresponding parameters were R2X(cum) = 0.97, R2Y(cum) = 0.86, and Q2(cum) = 0.75. In both cases, the obtained values of R2X(cum) and R2Y(cum) represented excellent goodness-of-fit, and the Q2(cum) values represented high predictive ability. The model was validated with 20 permutations, resulting in R2Y-intercepts of 0.21 and 0.31 and Q2Y-intercepts of −0.57 and −0.59 for ‘Mortti’ and ‘Ola’, respectively. The intercept values of the permutation plots are shown in Supplementary Figure 2. According to Eriksson et al., R2Y-intercepts <0.3–0.4 and Q2Y-intercepts <0.05 prove model validity.[30] In contrast, the discrimination between northern and southern ‘Melalahti’ samples was not as good, with R2X(cum), R2Y(cum), and Q2(cum) values of 0.54, 0.37, and 0.28, respectively (Supplementary Figure 3). Hence, it can be stated that composition in ‘Melalahti’ cultivars was, on average, less affected by the growth location for the years under study. These findings are in accordance with those of Zheng et al., who described little association of phenolic compounds in ‘Melalahti’ with the growth latitudes.[7] Regardless of the poor fitting in the PLS-DA model for ‘Melalahti’, when performing univariate tests, significant differences were found for some terpenoids (i.e., α-pinene, myrcene, α-phellandrene, limonene, cis-β-ocimene, trans-β-ocimene, γ-terpinene, borneol, and campholenal) for which the contents, in all cases, were higher for the samples grown in the northern location, with the only exception of campholenal, which was found in a higher content in those grown in the South. The loading plot (Figure B) and variable importance in the projection (VIP) showed that the most important compounds in the PLS-DA model for ‘Mortti’ were ethyl isovalerate, methyl benzoate, verbenene, β-pinene, eucalyptol, and β-caryophyllene in the northern samples and α-terpinene, limonene, and terpinolene in the samples from the southern location. Similarly, the most important variables in the loading plot for ‘Ola’ were ethyl isovalerate, methyl benzoate, α-pinene, verbenene, β-pinene, eucalyptol, and β-caryophyllene for the samples grown in the North and α-terpinene and terpinolene for those grown in the South (Figure D). The similarity of the PLS-DA models for ‘Mortti’ and ‘Ola’ is in accordance with the results previously obtained in the PCA analysis, where both cultivars were grouped together, indicating the similar behavior of these cultivars.

Figure 3

PLS-DA of ‘Mortti’ and ‘Ola’ cultivars showing (A) ‘Mortti’ scores plot, (B) ‘Mortti’ loadings plot, (C) ‘Ola’ scores plot, and (D) ‘Ola’ loadings plot for samples grown in Apukka (N, red circles) and Piikkiö (S, black triangles).

Figure shows the contents of the most abundant compounds, representing altogether over 94% of the total volatile profile obtained from the headspace. The plotted samples include the samples of all three cultivars harvested from southern and northern Finland in 2017, the last year under study. The total content of quantified volatiles in the headspace ranged from 550 μg·kg–1 fresh weight in the southern samples of ‘Ola’ and ‘Mortti’ to 1000 μg·kg–1 in the northern samples, whereas for ‘Melalahti’ the values were between 6- and 4-fold lower, being 86 and 250 μg·kg–1, respectively. In addition, it can easily be observed that in all cases, the contents of volatiles found in the samples from the North were higher than those in the corresponding samples from the South as previously anticipated in the PLS-DA analysis of northern and southern samples of the same cultivar. Regarding the individual compounds, limonene was the most abundant compound in all samples (25–375 μg·kg–1) followed by δ-3-carene in the ‘Ola’ and ‘Mortti’ cultivars, whereas for ‘Melalahti’, the second most abundant compound was γ-terpinene (10–24 μg·kg–1). Δ-3-Carene was the least abundant of the quantified compounds in ‘Melalahti’ with a content below 1 μg·kg–1. The plots for the ‘Mortti’ and ‘Ola’ cultivars for 2017 show a high similarity in regard to not only the qualitative composition but also the quantitative results. In contrast, ‘Melalahti’ showed a higher abundance of α-terpinene, eucalyptol, and γ-terpinene.

Figure 4

Composition of 2017 samples expressed as means (μg·kg–1) of fresh weight. Error bars indicate standard deviations (n = 3).

Effect of Meteorological Variables on Volatile Composition

With the aim of assessing the response of blackcurrant berries to weather conditions, a PLS-DA model was constructed using the ‘Mortti’ and ‘Ola’ cultivars, which already showed similar behavior and differentiation upon change of growth location. The variables included in the model together with the abbreviations used are shown in Table . Before constructing the model, the data were submitted to UV scaling. The PLS-DA model yielded R2X(cum), R2Y(cum), and Q2(cum) values of 0.77, 0.96, and 0.93, respectively, representing good values in regard to the model’s goodness-of-fit and predictive ability. The model was validated with 20 permutations, giving a R2Y-intercept of 0.18 and a Q2Y-intercept of −0.59, as shown in Supplementary Figure 3. In addition to the PLS-DA model shown, a PCA model constructed using the same variables and samples can be found in Supplementary Figure 4. This model reinforces the results discussed in the current section.

The PLS-DA biplot (Figure ), showing simultaneously the scores and the correlation coefficients, presents clear separation between the samples from the North and the samples from the South when meteorological variables were added to the data set. In this regard, the variables that most influenced the separation between northern and southern cultivars were the ones associated with temperature, especially those of the last month before harvest. As shown in Figure B, as an expansion of the circled area in Figure A, these weather variables included the following variables during the last month before harvest: the highest daily average temperature, the lowest daily average temperature, the highest temperature, and the temperature sum. Also, the average temperature in the last week before harvest is among the important temperature variables separating the samples of the North from those of the South. Radiation, especially the total radiation from the last month and the last week before harvest, also played an important role in the discrimination between the southern and northern cultivars of ‘Mortti’ and ‘Ola’. These meteorological variables were positively associated with the first component, which is the main one responsible for the separation between the northern and southern samples. Compared with the conditions in the northern location, radiation and temperature are generally higher in the southern location during the growth season and during the month and week before harvest. Hence, low temperature and radiation values during the last month before harvest could be linked with a higher abundance of volatile compounds in these blackcurrant cultivars. The effects of radiation on the volatiles in fruits are still to be further studied in detail, as a report from Xu et al. revealed that preharvest radiation of strawberry with UV-C had no significant impact on the volatile composition, whereas the contents of sugars and ascorbic acid were increased.[20] On the other hand, Severo et al. stated that UV-C promoted increases in total polyphenolic and volatile organic contents, mostly in proanthocyanidins, anthocyanins, and esters in external tissues.[34] The difference in light quality between the South and the North may also have played a role in the difference in the abundance of volatiles observed in this study, although we did not investigate the impact of these factors in detail. Our previous research with the same blackcurrant cultivars used in the present study showed a positive impact from the temperature and radiation variables in the southern location, resulting in higher contents of phenolic compounds (dephinidin-3-O-glucoside, delphinidin-3-O-rutenoside, and myricetin-3-O-glucoside),[22] sugars, acids, and ascorbic acid.[7] Meteorological variables related with precipitation had a less remarkable effect on the separation of the northern and southern cultivars. In this regard, humidity from the start of the growth season until the day of harvest together with the percentage of days with high humidity were higher for the southern cultivars, whereas the humidity during the last month and week before harvest were higher for the northern cultivars. In previous research with the same cultivars and the same test fields, we found that the contents of sugars and acids were negatively associated with low humidity variables, whereas the content of vitamin C was positively correlated with these variables.[35] However, in Nordic environments, water-related variables are not expected to be the limiting factor, as the values between the southern and northern locations do not differ to a large extent compared with the radiation- and temperature-related variables, which are highly affected by the change of latitude.

Figure 5

(A) PLS-DA model biplot of ‘Mortti’ and ‘Ola’ samples grown in Apukka (N, red circles) and Piikkiö (S, black triangles). (B) Expanded areas circled in Figure A. The compounds are coded according to Table . The weather variable abbreviations are represented according to Table .

Studies focused on grape samples have reported that precipitation and humidity variables might have a certain effect on carotenoid and terpenoid biosynthesis when the plants are exposed to moderate water stress.[36] Moreover, it has been reported by several authors that different abiotic stress factors have an impact on the production of volatiles in plants,[37−39] although the exact signaling mechanisms for the regulation of volatile emissions by environmental or physiological factors still await further examination.[40] In addition, existing reports on fruits state that the effects of environmental conditions vary among compounds,[18] thus making it difficult to draw conclusions on how the environment affects volatile compounds. These findings, together with the results of the current research, indicate that the regulatory roles of environmental variables may vary depending on the biosynthetic and metabolic pathways in plants.

As already pointed out in the previous section, volatile compounds affected the negative side of the first component: higher contents of volatiles were found in the cultivars from the northern location. Moreover, they also presented a certain scattering along the second component, meaning that the volatile composition may also vary depending on the year of harvest, which implies other variables, in addition to those related to weather, were involved.

This study demonstrated for the first time that northern latitudes may increase the contents of volatile compounds in blackcurrant berries. The fact that this study was carried out in an 8 year time period adds robustness to the obtained results as it is evident that the volatile composition of blackcurrants can suffer important year-to-year variations. According to the reported results, this variation would be more related to the abundances of volatiles, rather than their qualitative composition, for the cultivars and locations reported here, as compounds present in the samples remained constant year to year. The meteorological variables studied included a selection of the most remarkable among them. Here, we were able to link the most decisive weather variables in the differentiation of the blackcurrant cultivars. In this regard, temperature and radiation, which are constantly lower in the northern location, played a key role. In this work, we focused on the variations of the volatiles in a long time period. However, the relationship between both Nordic locations and the aroma compositions of the blackcurrant cultivars remained outside of the scope of this study. For this purpose, further studies are needed to investigate the significance and the influence on the sensory properties of these berries.