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. 2026 Mar 31;109(4):542–555. doi: 10.1093/jaoacint/qsag025

Iridoid Glycosides as Key Discriminators of Cranberry (Vaccinium macrocarpon) Cultivars Revealed by UHPLC–HRAM-MSn Metabolomics

Ping Geng 1, Jianghao Sun 2, Pei Chen 3,✉
PMCID: PMC13458945  PMID: 41920922

Abstract

Background

Cranberry (Vaccinium macrocarpon) contains diverse hydrophilic phytochemicals, but their compositional profiles and cultivar-specific variation have not been comprehensively characterized.

Objective

The objective of this study was to profile hydrophilic metabolites in cranberry cultivars and identify constituents that differentiate among cultivars.

Methods

Water-eluted fractions of Sephadex® LH-20–separated cranberry extracts from six cultivars (Crimson Queen, Demoranville, Haines, Mullica Queen, Stevens, and Welker) were analyzed using ultra-HPLC–high-resolution accurate-mass tandem mass spectrometry (UHPLC–HRAM-MSn). Metabolites were annotated based on accurate mass and MS2 fragmentation data and comparison with literature reports. Sparse partial least-squares discriminant analysis (sPLS-DA) was applied to assess classification performance and identify discriminative features.

Results

Thirty-nine putative hydrophilic metabolites were identified, including hydroxycinnamic acid hexose derivatives, anthocyanins, hydroxycinnamoylquinic acids, and a distinct group of iridoid glycosides. Vaccinoside and several coumaroyl-substituted monotropein derivatives were structurally characterized in cranberry for the first time. sPLS-DA identified iridoid glycosides as key discriminators, with higher abundance observed in the Welker cultivar.

Conclusion

Cranberry cultivars contain previously unreported iridoid glycosides that contribute to chemotaxonomic differentiation. These results expand current knowledge of cranberry hydrophilic phytochemistry and support the relevance of iridoid glycosides as cultivar-informative metabolites.

Highlights

This study characterizes the highly polar, water-eluted fraction of six cranberry cultivars using UHPLC-HRAM-MSn analysis, resulting in the putative identification of 39 compounds, including a prominent series of iridoid glycosides.

The fruit of cranberry (Vaccinium macrocarpon), a commercially significant berry crop native to North America, has attracted increasing research interest due to its diverse phytochemical profile, especially its bioactive secondary metabolites that contribute to its health benefits, such as antioxidant, anti-inflammatory, and antimicrobial properties (1–4). While proanthocyanidins (PACs) (5–8) and anthocyanins (9–12) have dominated cranberry secondary metabolites research, water-soluble metabolites including phenolic acids, flavonol glycosides, triterpenoids, and hydroxycinnamic acid conjugates have gained increasing attention for their roles in flavor, bioavailability, and health benefits (13–15).

Considerable research has been devoted to understanding how genetic factors (cultivar) and environmental factors (such as growing region) influence the composition of cranberry phytochemicals (16–18). In our recent study (19), six cranberry cultivars harvested from two major production states, Wisconsin and New Jersey, were investigated using a combination of fuzzy chromatography mass spectrometry (FCMS) and ultra-HPLC coupled to high-resolution accurate-mass tandem mass spectrometry (UHPLC–HRAM-MSn). The results demonstrated that both cultivar and geographic origin significantly affect metabolite variability. However, for flavonol glycosides and iridoid derivatives, the cultivar factor accounted for a greater proportion of variance (29.0%) than the growing state (22.3%), suggesting that these compounds may be more tightly regulated by genetic rather than environmental factors.

This insight prompted a follow-up investigation that focuses exclusively on the cultivar variation in secondary metabolite profiles. By removing the geographic variable, confounding effects can be reduced, and the resolution of cultivar-specific chemical differences can be increased. Sephadex® LH-20 size-exclusion chromatography was applied to selectively fractionate cranberry extracts, followed by UHPLC–HRAM-MSn analysis of the water-eluted fraction. High-dimensional metabolomics datasets often contain many variables that are weakly informative, highly correlated, or dominated by analytical noise. Under these conditions, traditional partial least-squares discriminant analysis (PLS-DA) may produce models that are difficult to interpret because all variables are retained regardless of their relevance. Unlike traditional PLS-DA, sparse PLS-DA (sPLS-DA) incorporates variable selection during model construction, enhancing interpretability by focusing on the most discriminative metabolites while reducing noise from non-informative features (20–22). Among the key findings, iridoid compounds—particularly coumaroyl monotropein and its derivatives—emerged as prominent discriminators across cranberry cultivars, underscoring their potential utility as cultivar-specific metabolic markers.

Compared with the previously mentioned classes of compounds, iridoid glycosides have received relatively limited attention in both analytical and functional studies. To date, only a few iridoid derivative monotropeins, 6,7-dihydromonotropein, 10-trans-coumaroyl-6,7-dihydromonotropein, and 10-cis-coumaroyl-6,7-dihydromonotropein, have been reported in cranberry fruits (23, 24), and some more iridoids have been reported from genus Vaccinium (25–29). Although iridoid glycosides remain underexplored in cranberry compared to flavonoids and organic acids, accumulating evidence from other plant systems has demonstrated their potent biological effects, including anti-inflammatory (30), neuroprotective (31), hepatoprotective (32), and anticancer properties (33). These findings highlight their potential value as bioactive dietary phytochemicals.

This study aims to provide a more comprehensive analysis of water-soluble compounds in cranberries—particularly the less-studied iridoids and hydroxycinnamic acid derivatives—and to characterize cultivar-specific differences in polar metabolites, which may offer useful context for future applications such as food authentication, functional food development, and cultivar selection.

Experimental

Reagents

Optima LC/MS grade acetonitrile, HPLC grade methanol, and formic acid were purchased from Fisher Scientific. (Waltham, MA, USA). HPLC water was prepared from distilled water using a Milli-Q system (Millipore Laboratories, Bedford, MA, USA). Sephadex LH-20, analytical grade methanol, and acetone were purchased from Sigma (St. Louis, MO, USA).

Cranberry Fruit Materials Preparation, Extraction, and Fractionation

Fully matured cranberry (Vaccinium macrocarpon) fruits of six cultivars were collected in New Jersey and Wisconsin (Table 1). Immediately after harvest, the fruit was frozen and shipped to the laboratory overnight. Upon arrival, each laboratory sample was frozen, lyophilized, and ground into a fine powder, and the resulting material was passed through a 20-mesh sieve to ensure uniform particle size. The extraction adopted the optimized parameters proposed by Klavins et al. (34). Some modifications were made to improve compatibility with MS analysis. Briefly, a 100.0 mg aliquot of each test portion was mixed with 5.0 mL methanol–formic acid (99 + 1, v/v) and sonicated at 16 kHz with 250 W power for 20 min (Advanced Sonic Processing Systems, Oxford, CT, USA), then centrifuged at 13 000g for 10 min in a Contifuge 28RS centrifuge (Heraeus, South Plainfield, NJ, USA) at 4°C. The supernatants were filtered through a 25 mm (0.20 μm) PTFE syringe filter (Thermo Scientific™) and concentrated to dryness with a miVac Quattro concentrator (Genevac Inc, Gardiner, New York, USA).

Table 1.

Cranberry of six cultivars from New Jersey and Wisconsin.

Samples Location (Grower) Harvest date Cultivara State
CQ_NJ_1 J.J. White September 18, 2019 CQ New Jersey
CQ_NJ_2 Lee September 17, 2019 CQ New Jersey
CQ_NJ_3 Lee October 21, 2019 CQ New Jersey
CQ_NJ_4 T. Budd September 17, 2019 CQ New Jersey
CQ_NJ_5 Rutgers September 17, 2019 CQ New Jersey
DM_NJ_1 J.J. White September 18, 2019 DM New Jersey
DM_NJ_2 Lee September 17, 2019 DM New Jersey
DM_NJ_3 Lee October 21, 2019 DM New Jersey
DM_NJ_4 T. Budd September 17, 2019 DM New Jersey
DM_NJ_5 Rutgers September 17, 2019 DM New Jersey
HA_NJ_1 J.J. White September 18, 2019 HA New Jersey
HA_NJ_2 Lee September 17, 2019 HA New Jersey
HA_NJ_3 Lee October 21, 2019 HA New Jersey
HA_NJ_4 T. Budd September 17, 2019 HA New Jersey
HA_NJ_5 Rutgers September 17, 2019 HA New Jersey
MQ_NJ_1 J.J. White October 25, 2019 MQ New Jersey
MQ_NJ_2 Lee October 21, 2019 MQ New Jersey
MQ_NJ_3 Rutgers October 7, 2019 MQ New Jersey
ST_NJ_1 J.J. White October 25, 2019 ST New Jersey
ST_NJ_2 Lee October 21, 2019 ST New Jersey
ST_NJ_3 Rutgers October 7, 2019 ST New Jersey
WE_NJ_1 J.J. White September 18, 2019 WE New Jersey
CQ_WI_1 Elm Lake September 16, 2019 CQ Wisconsin
CQ_WI_2 Steve Gebhardt September 19, 2019 CQ Wisconsin
DM_WI_1 Elm Lake September 16, 2019 DM Wisconsin
DM_WI_2 Randy Gebhardt September 19, 2019 DM Wisconsin
HA_WI_1 Elm Lake September 16, 2019 HA Wisconsin
HA_WI_2 Randy Gebhardt September 19, 2019 HA Wisconsin
HA_WI_3 Steve Gebhardt September 19, 2019 HA Wisconsin
MQ_WI_1 Elm Lake October 7, 2019 MQ Wisconsin
MQ_WI_2 Nick Gebhardt October 18, 2019 MQ Wisconsin
ST_WI_1 Elm Lake October 7, 2019 ST Wisconsin
ST_WI_2 Nick Gebhardt October 18, 2019 ST Wisconsin
WE_WI_1 Elm Lake September 16, 2019 WE Wisconsin
WE_WI_2 Randy Gebhardt September 19, 2019 WE Wisconsin
WE_WI_3 Steve Gebhardt September 19, 2019 WE Wisconsin
Sum 36
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a

Abbreviations: Q = Crimson Queen; DM = Demoranville; HA = Haines; MQ = Mullica Queen; ST = Stevens; WE = Welker.

The dried residue was reconstituted with 0.5 mL water before loading onto Sephadex LH-20 columns. Sephadex LH-20 was hydrated overnight in water before the column was manually packed. The column was washed with 5 mL water to remove sugars, followed by sequential elution with 20 mL water, 20 mL 70% (v/v) methanol in water, and 10 mL 70% (v/v) acetone in water to obtain fractions A, B, and C, respectively (Figure 1). This study primarily focused on fraction A (the water eluate).

Figure 1.

Flowchart illustrating cranberry fruit sample processing and Sephadex LH‑20 fractionation. The dried residue is reconstituted with water, loaded onto a hydrated Sephadex LH‑20 column, and sequentially eluted with water, 70% methanol, and 70% acetone to produce fractions A, B, and C. Fraction A, the water eluate, is the focus of this study.

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Flowchart of cranberry fruit sample processing and fractionation. (For interpretation of the references to color in this legend, the reader is referred to the Web version of this article).

The eluate residue after evaporation of eluent was dissolved in 1.0 mL of 70% (v/v) methanol in water. The solution was filtered with a 13 mm (0.20 μm) PTFE syringe filter (Thermo Scientific) and stored at −80°C until analysis. The injection volume for all test solutions was 2.0 μL.

UHPLC–HRAM-MSn Conditions

The UHPLC–HRAM-MSn system consisted of an Orbitrap ID-X Tribrid MS coupled with a Vanquish™ UHPLC system (Thermo Fisher Scientific, San Jose, CA). ESI detection was operated in negative ion mode: sheath gas, 45 (arbitrary units); auxiliary gas, 13 (arbitrary units); spray voltage, 2.5 kV; capillary temperature, 300°C; and vaporizer temperature, 275°C. The scan event cycle used a FT full scan (mass range 150–1300 m/z) at a resolving power of 120 000 and a data-dependent collision-induced dissociation (CID) MS2-4 event acquired by the Orbitrap at a resolving power of 30 000 triggered by the most intense ion from each previous scan event. The MS2 activation parameter used an isolation width of 1.5 amu, collision energy at 35%, and activation time of 10 ms. Default settings were adopted for the remaining parameters.

The LC separation was carried out on a Accucore™ Vanquish C18+ UHPLC column (150 mm × 2.1 mm, 1.5 µm; Thermo Fisher Scientific) with a flow rate of 0.2 mL/min. The mobile phase consisted of a combination of A (0.1% formic acid in water) and B (0.1% formic acid in acetonitrile). A gradient elution program was employed as follows: 5% B at 0 min, a linear increase to 25% B at 35.0 min, to 95% B at 40.0 min, and re-equilibrated with 5% B for 8 min between injections. The column temperature was set at 60°C. UV–Vis spectra were recorded from 190 to 600 nm.

Data Processing

All collected raw data were imported into Progenesis QI software (Waters, Milford, MA, USA) for initial data processing. The processing procedure consisted of automatic peak alignment, peak picking, isotope and adduct peak grouping, peak integration, and blank subtraction. Throughout processing, the mass tolerance was no more than 5 ppm, and the retention time range was set between 1.2 and 30 min. The normalized peak areas, across all data files, were subsequently exported for chemometric analysis with Metaboanalyst online (35). The features containing zero or missing values were imputed using the default setting of replacing each value with one-fifth of the minimum positive value for that variable. Data quality filters were applied sequentially: the repeatability filter removed features that could not be measured reliably based on QC samples; the variance filter excluded features with little or no variation across samples; and the abundance filter removed features with very low signal intensity. The remaining data were normalized using probabilistic quotient normalization (PQN) with a pooled QC group as the reference. Square-root transformation was applied to reduce heteroscedasticity, followed by mean-centering to improve comparability across samples. sPLS-DA was conducted using the “Multivariate Statistical Analysis” module. The number of components and the number of variables selected per component were optimized using MetaboAnalyst’s built-in cross-validation procedure.

Results and Discussion

Metabolomic Profiling of Cranberry Extract Water Eluate

Fractionation of cranberry extracts using Sephadex LH-20 yielded three eluates: water, 70% (v/v) methanol in water, and 70% (v/v) acetone in water. The water-eluted fractions were analyzed by UHPLC–HRAM-MSn in negative ion mode, and 39 compounds were putatively annotated based on accurate mass measurements, retention time, diagnostic MSn fragmentation patterns, and comparison with previously reported data. (Table  2). Representative base peak chromatograms (BPCs) of the six cultivars are shown in Figure 2. These hydrophilic constituents comprised small molecule organic acids, hydroxycinnamic acid derivatives, anthocyanins, and a notable set of iridoid glycosides. This selective enrichment facilitated the investigation of compounds that are often masked yet biologically relevant in unfractionated extracts, by reducing interference from highly abundant PACs and flavonol glycosides.

Table 2.

Putative identification of hydrophilic metabolites in water-eluted LH-20 fractions from six cranberry cultivars.

Peak No. Rt (min) ESI (−) measured (m/z) Ion type Formula (neutral form) Error (ppm) Putative identification MSna Refences
1 1.49 341.1089 [M-H]− C12H22O11 −0.102 Dihexoside MS2[341.1]: 179.1(100), 161.0(34), 143.0(45), 113.0(53), 101.0(27)
2 1.52 191.0561 [M-H]− C7H12O6 −0.059 Quinic acid MS2[191.1]: 173.0(85), 171.0(32), 155.0(9), 127.0(100), 111.0(71), 109.0(25) (40, 41)
3 1.56 191.0197 [M-H]− C6H8O7 −0.135 Isocitric acid MS2[191.0]: 173.0(13), 147.0(5), 127.0(6), 111.0(100) (42, 43)
4 1.56 133.0142 [M-H]− C4H6O5 −0.350 Malic acid MS2[1331.0]: 115.0(100) (41)
5 1.95 191.0197 [M-H]− C6H8O7 −0.135 Citric acid MS2[191.0]: 173.0(11), 147.0(10), 129.0(3), 111.0(100) (42, 43)
6 4.72 325.0928 [M-H]− C15H18O8 −0.279 p-Coumaric acid hexoside MS2[325.1]: 163.0(100); MS3[325.1—>163.0]: 119.0(100) (39, 44)
371.0982 [M+FA-H]− —b — — MS2[371.1]: 325.1(78), 163.0(100); MS3[371.1—>163.0]: 119.0(100) —
7 4.89 341.0877 [M-H]− C15H18O9 −0.309 Caffeoyl hexose ester (should be α-1-caffeoylhexose according to the reference) MS2[341.1]: 281.1(5), 251.1(2), 203.0(8), 179.0(100), 161.0(41), 135.0(8); MS3[341.1—>179.0]: 135.0(100) (38, 45, 46)
8 6.34 341.0877 [M-H]− C15H18O9 −0.309 Caffeic acid hexoside MS2[341.1]: 179.0(100), 135.0(10); MS3[341.1—>179.0]: 135.0(100) (38, 45–47)
9 6.82 353.0878 [M-H]− C16H18O9 −0.005 Caffeoylquinic acid MS2[353.1]: 191.1(100); MS3[353.1—>191.1]: 173.0(79), 171.0(32), 127.0(100), 111.0(38), 109.0(27); MS4[353.1—>191.1—>127.0]: 109.0(100) (25, 29, 46, 48)
10 7.25 325.0928 [M-H]− C15H18O8 −0.279 p-coumaroylhexose MS2[325.1]: 265.1(17), 187.0(39), 163.0(78), 145.0(100); MS3[325.1—>145.0]: 117.0(100) (39, 44)
11 7.60 447.0932 [M+-2H]− C21H21O11 (M+) −0.189 Cyanidin-3-hexoside MS2[447.130]: 327.1(13), 299.1(19), 285.0(100), 284.0(71); MS3[447.130—>285.030]: 255.0(65), 241.0(100), 217.0(35), 175.0(27), 163.0(30), 147.0(42), 125.0(29) (49–52)
465.1038 [M+-2H+H2O]− — — MS2[465.130]: 339.1(10), 285.0(100), 241.0(12); MS3[465.130—>285.030]: 243.0(27), 241.0(100), 217.0(26), 199.0(13), 149.0(16) —
12 8.32 385.1140 [M-H]− C17H22O10 −0.052 Sinapoyl glucose MS2[385.1]: 223.1(100); MS3[385.1—>223.1]: 208.0(100), 179.1(36), 164.0(25); MS4[385.1—>223.1—>208.0]: 193.0(9), 149.0(100), 149(37) (53)
13 8.51 325.0928 [M-H]− C15H18O8 −0.279 p-Coumaroylhexose MS2[325.1]: 265.1(17), 187.0(40), 163.0(79), 145.0(100); MS3[325.1—>145.0]: 117.0(100) (39, 44)
14 9.06 283.0823 [M-H]− C13H16O7 −0.092 Benzoic acid hexoside MS2[283.1]: 121.0(100) (54, 55)
329.0878 [M+FA-H]− — −0.016 — MS2[329.1]: 283.1(100), 207.1(13), 121.0(31); MS3[329.1—>283.1]: 121.0(100) —
15 9.53 417.0826 [M+-2H]− C20H19O10 (M+) −0.287 Cyanidin-O-pentoside MS2[417.130]: 299.1(11), 285.0(65), 284.0(100); MS3[417.130—>284.030]: 256.0(74), 255.0(100), 241.0(50), 240.0(43), 147.0(49); MS4[417.130—>284.030—>255.030]: 227.0(100) (56, 57)
435.0932 [M+-2H+H2O]− −0.194 — MS2[435.130]: 285.0(100); MS3[435.130—>285.030]: 241.1(100), 217.1(29), 199.0(11), 149.0(21) —
16 9.58 355.1034 [M-H]− C16H20O9 −0.156 Feruloyl hexose MS2[355.1]: 217.0(51), 193.0(100), 175.0(47); MS3[355.1—>193.0]: 178.0(18), 149.1(31), 134.0(100) (46, 58, 59)
17 10.13 337.0928 [M-H]− C16H18O8 −0.269 Coumaroylquinic acid MS2[337.1]: 191.1(100), 163.0(5); MS3[337.1—>191.1]: 173.0(98), 171.0(34), 127.0(100), 111.0(38), 109.0(25); MS4[337.1—>191.1—>127.0]: 109.0(100) (29, 60)
18 10.69 385.1140 [M-H]− C17H22O10 −0.052 Sinapoyl glucose MS2[385.1]: 325.1(5), 295.1(4), 265.1(10), 247.1(45), 223.1(100), 205.0(57), 190.0(5); MS3[385.1—>223.1]: 208.0(30), 179.1(20), 164.0(100); MS4[385.1—>223.1—>164.0]: 149.0(100) (61, 62)
19 11.01 461.1083 [M+-2H]− C22H23O11 (M+) −0.082 Peonidin-galactoside MS2[461.1]: 299.1(100), 298.0(51); MS3[461.1—>299.1]: 284.0(92), 283.0(100), 255.1(29), 240.0(17), 147.0(12); MS4[461.1—>299.1—>283.0]: 255.0(100), 239.0(28), 227.0(10) (51, 63)
479.1194 [M+-2H+H2O]− — — MS2[479.1]: 461.1(5), 355.1(30), 329.1(21), 316.1(5), 299.1(100); MS3[479.1—>299.1]: 285.0(10), 284.0(100), 149.0(6); MS4[479.130—>299.1—>284.0]: 284.0(100), 256.0(22), 255.0(16), 240.0(23) —
20 11.82 553.1560 [M-H]− C25H30O14 −0.504 Coumaroyl-hydroxyl-dihydromonotropein MS2[553.2]: 389.1(100); MS3[553.2—>389.1]: 209.0(100); MS4[553.2—>389.1—>209.0]: 165.1(100) (28, 64)
21 12.06 553.1560 [M-H]− C25H30O14 −0.504 Coumaroyl-hydroxyl-dihydromonotropein MS2[553.2]: 507.2(26), 389.1(100); MS3[553.2—>389.1]: 227.1(8), 209.0(100) (28, 64)
22 12.14 461.1083 [M+-2H]− C22H23O11 (M+) −0.082 Peonidin-glucoside MS2[461.1]: 299.1(100); MS3[461.1—>299.1]: 285.0(15), 284.0(100), 283.0(28), 147.0(15); MS4[479.130—>299.1—>284.0]: 256.0(38), 255.0(100), 240.0(13) (51, 63)
479.1194 [M+-2H+H2O]− — — MS2[479.1]: 355.1(54), 317.1(58), 299.1(100); MS3[479.1—>299.1]: 284.0(100) —
23 13.02 431.0983 [M+-2H]− C21H21O10 (M+) −0.162 Peonidin-pentoside MS2[431.1]: 299.1(100); MS3[431.1—>299.1]: 284.0(100), 283.0(54), 147.0(14); MS4[431.1—>299.1—>284.0]: 256.0(27), 255.0(100), 239.0(11), 227.0(14) (51, 65)
449.1089 [M+-2H+H2O]− −0.077 — MS2[449.1]: 431.1(6), 325.1(91), 317.1(22), 299.1(100); MS3[449.1—>299.1]: 284.0(100), 285.0(14), 149.0(34) —
24 13.03 553.1560 [M-H]− C25H30O14 −0.504 Coumaroyl-hydroxyl-dihydromonotropein MS2[553.2]: 509.2(5), 389.1(100), 347.1(6); MS3[553.2—>389.1]: 227.1(69), 209.0(100) (28, 64)
25 13.44 553.1560 [M-H]− C25H30O14 −0.504 Coumaroyl-hydroxyl-dihydromonotropein MS2[553.2]: 509.2(6), 389.1(100), 347.1(5); MS3[553.2—>389.1]: 227.1(52), 209.0(100), 147.0(65) (28, 64)
26 13.53 337.0928 [M-H]− C16H18O8 −0.269 Coumaroylquinic acid MS2[337.1]: 191.1(100), 163.0(6); MS3[337.1—>191.1]: 173.0(65), 171.0(32), 127.0(100), 111.0(36), 109.0(24); MS4[353.1—>191.1—>127.0]: 109.0(100) (29, 60)
27 14.88 353.0878 [M-H]− C16H18O9 −0.005 Coujugates of psilalic acid and hexose MS2[353.1]: 325.1(38), 187.0(40), 163.0(100), 145.0(83), 119.1(11); MS3[353.1—>163.0]: 119.1(100) (38, 66, 67)
28 18.32 355.1034 [M-H]− C16H20O9 −0.156 Ferulic acid 4-hexoside (4 is the only possible position) MS2[355.1]: 309.1(100), 207.0(20), 147.0(30); MS3[355.1—>309.1]: 147.0(100) (59, 68)
29 18.40 535.1456 [M-H]− C25H28O13 −0.213 Coumaroyl monotropein MS2[535.1]: 491.2(20), 373.1(64), 371.1(100), 329.1(67), 311.1(17), 267.1(10), 191.0(41), 165.1(14), 163.0(38); MS3[535.1—>371.1]: 327.1(16), 191.0(100), 165.1(94), 147.0(84), 121.0(15); MS4[535.1—>371.1—>191.0]: 147.0(100) (29)
30 19.32 535.1456 [M-H]− C25H28O13 −0.213 Coumaroyl monotropein MS2[535.1]: 491.2(19), 373.1(57), 371.1(100), 329.1(60), 311.1(16), 267.1(8), 191.0(37), 165.1(14), 163.0(37); MS3[535.1—>371.1]: 209.0(15), 191.0(100), 165.1(84), 147.0(76), 121.0(14); MS4[535.1—>371.1—>191.0]: 147.0(100) (29)
581.1511 [M+FA-H]− — −0.093 — MS2[581.2]: 535.1(100); MS3[581.2—>535.1]: 373.1(49), 371.1(100), 329.1(54), 191.0(38), 173.5(19), 163.0(26) —
1071.2988 [2M-H]− — 0.089 — MS2[1071.3]: 535.1(100) —
31 20.00 537.1613 [M-H]− C25H30O13 −0.119 Coumaroyl dihydromonotropein MS2[537.2]: 493.2(26), 375.1(26), 373.1(100), 331.1(54), 313.1(11), 163.0(25); MS3[537.2—>373.1]: 330.1(14), 211.1(43), 193.0(100), 167.1(53), 165.1(14), 149.1(59), 123.0(47), 119.0(20) (23)
32 20.87 537.1613 [M-H]− C25H30O13 −0.119 Coumaroyl dihydromonotropein MS2[537.2]: 493.2(26), 375.1(24), 373.1(100), 331.1(55), 313.1(11), 163.0(24); MS3[537.2—>373.1]: 211.1(43), 193.1(100), 167.1(44), 149.1(54), 123.0(42), 119.0(20) (23)
583.1668 [M+FA-H]− — −0.043 — MS2[583.2]: 537.2(100); MS3[583.2—>537.2]: 493.2(22), 375.1(23), 373.1(100), 331.1(46), 163.0(24); MS4[583.2—>537.2—>373.1]: 193.1(95), 149.1(100) —
573.1387 [M+HCl-H]− — — — — —
33 21.56 565.1562 [M-H]− C26H30O14 −0.137 Feruloyl monotropein MS2[565.2]: 521.2(13), 403.1(100), 371.1(55), 193.0(42); MS3[565.2—>403.1]: 359.1(100), 193.0(98) (69)
34 21.92 567.1715 [M-H]− C26H32O14 −0.756 Feruloyl dihydromonotropein MS2[567.2]: 523.2(26), 405.1(81), 373.1(100), 361.1(24); MS3[567.2—>373.1]: 193.1(100) —
35 23.10 563.1403 [M-H]− C26H28O14 −0.584 Decarbonylating coumaroyl monotropein isomer MS2[563.1]: 535.1(100); MS3[563.1—>535.1]: 491.2(19), 373.1(52), 371.1(100), 329.1(55), 311.1(14), 267.1(7), 191.0(44), 165.1(15), 163.0(33); MS4[563.1—>535.1—>371.1]: 327.1(23), 191.0(100), 165.1(90), 147.0(87), 121.0(10) —
36 23.16 567.1715 [M-H]− C26H32O14 −0.756 Feruloyl dihydromonotropein MS2[567.2]: 523.2(22), 405.1(77), 373.1(100), 361.1(25); MS3[567.2—>373.1]: 211.1(46), 193.1(100), 149.1(43), 123.0(41), 119.0(14) —
37 23.87 563.1403 [M-H]− C26H28O14 −0.584 Decarbonylating coumaroyl monotropein isomer MS2[563.1]: 535.1(100); MS3[563.1—>535.1]: 491.2(18), 373.1(57), 371.1(100), 329.1(57), 311.1(14), 267.1(7), 191.0(43), 165.1(13), 163.0(36); MS4[563.1—>535.1—>371.1]: 327.1(143), 191.0(100), 165.1(96), 147.0(88), 121.0(15) —
38 24.46 565.1562 [M-H]− C26H30O14 −0.137 Decarbonylating coumaroyl dihydromonotropein isomer MS2[565.2]: 537.2(100); MS3[565.2—>537.2]: 493.2(39), 375.1(21), 373.1(100), 331.1(47), 163.0(23); MS4[565.2—>537.2—>373.1]: 193.0(100) —
39 25.26 565.1562 [M-H]− C26H30O14 −0.137 Decarbonylating coumaroyl dihydromonotropein isomer MS2[565.2]: 537.2(100); MS3[565.2—>537.2]: 493.2(23), 375.1(20), 373.1(100), 331.1(50), 313.1(8), 163.0(22); MS4[565.2—>537.2—>373.1]: 193.0(100), 167.1(51), 149.1(53), 123.0(49) —
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a

Numbers in parentheses indicate relative ion abundance. The most intense fragment ion (base peak) in each MS2-4 spectrum is shown in bold.

b

Empty cells in the adduct-ion row have been replaced with an em dash (—) to avoid repetition.

Figure 2.

Base peak chromatograms of the water‑eluted fraction of cranberry extracts from six cultivars—Crimson Queen, Demoranville, Haines, Mullica Queen, Stevens, and Welker. Each chromatogram displays numbered peaks representing major discriminative metabolites. Peak labels are color‑coded by compound class: small organic acids, hydroxycinnamic acid hexose derivatives, anthocyanins, hydroxycinnamoylquinic acids, and iridoid glycosides. The figure highlights differences in hydrophilic metabolite profiles among cultivars following Sephadex LH‑20 fractionation and UHPLC‑HRAM‑MSⁿ analysis.

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Base peak chromatograms (BPCs) of water eluate of cranberry extract fractionated by Sephadex LH-20. Six cultivars (CQ, DM, HA, MQ, ST, and WE) were compared, with numbered peaks indicating major discriminative metabolites detected. Peak labels are color-coded by putative compound class: black for small molecule organic acid, green for hydroxycinnamic acid hexose derivatives, blue for anthocyanins, plum for hydroxycinnamoylquinic acids, and red for iridoids. Abbreviations: CQ = Crimson Queen; DM = Demoranville; HA = Haines; MQ = Mullica Queen; ST = Stevens; WE = Welker.

Putative Identification of Iridoid Glycosides

Iridoids are a subclass of terpenes characterized by a cyclopentanopyran framework. In the genus Vaccinium, iridoids have been reported primarily as iridoid glycosides, with some occurring as hydroxycinnamoyl-substituted derivatives (23, 24, 27–29). In the present study, all 15 compounds annotated as iridoids in Table  2 and Figure  2 were identified as hydroxycinnamoylated iridoid glycosides.

Peaks 29 and 30 shared the same deprotonated molecular ion [M-H]− at m/z 535.1456, corresponding to the empirical formula C25H27O13. Their similar fragmentation patterns suggest they are isomers, although slight differences in the relative abundances of certain product ions were observed (Table 2). The fragment ion at m/z 491.1560 corresponded to a loss of 43.9898 (CO2), indicating the presence of carboxylic acid in the structure. In the MS2 spectrum (Figure 3), ions at m/z 373.0929 and 371.0984 were observed, arising from the direct loss of 162.0528 Da (hexosyl group) and 164.0473 Da (coumaroyl acid), respectively, from the deprotonated molecule. These observations indicate that the hexosyl and coumaroyl groups are individually bonded directly to the iridoid backbone. The ion at m/z 329.1032 could be generated either by losing CO2 from ion at m/z 373 or by losing a hexosyl moiety from m/z at 491 (Figures 3 and 4). The concurrent or sequential loss of sugar moiety and CO2 is characteristic for C-4-carboxylated iridoids (36).

Figure 3.

High‑resolution MS² and MSⁿ spectra of the ion at m/z 535.1456 for peaks 29 and 30, showing similar fragmentation patterns consistent with isomeric iridoid glycosides. Key product ions include losses corresponding to CO₂, a hexosyl group, and a coumaroyl group, with additional fragments reflecting sequential or concurrent neutral losses characteristic of C‑4‑carboxylated iridoids.

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High-resolution multiple-stage mass spectra of ions at m/z 535.1456.

Figure 4.

Speculative fragmentation pathway of vaccinoside (10‑p‑trans‑coumaroyl‑monotropein), illustrating the proposed sequence of neutral losses and key fragment ions derived from the deprotonated molecule at m/z 535.1456. The diagram shows the formation of major product ions, including those arising from losses of CO₂, hexosyl and coumaroyl groups, and subsequent water loss, consistent with characteristic fragmentation behavior of C‑4‑carboxylated iridoid glycosides.

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Speculative fragmentation pathway of vaccinoside (10-p-trans-coumaroyl-monotropein).

Subsequently, further loss of H2O from the ion at m/z 329.1032 yield a fragment ion at m/z 311.0926. The fragment at m/z 191.0350 can be attributed to the loss of a hexosyl group and H2O from the ion at m/z 371.0984. The fragment ion at m/z 163.0402 was generated directly from the molecular ion and corresponded to coumaric acid. The fragmentation pathway of these ions, together with those observed in the MS3 and MS4 mass spectra, are illustrated in Figure 4. Based on high-resolution MSn data and by referencing the fragmentation patterns reported in the literatures (23, 25, 27, 29), compounds 29 and 30 were putatively identified as coumaroyl monotropein, iridoid glycosides acylated with coumaric acid. More detailed structural features—such as the exact site of acylation and the cis/trans configuration—could not be determined by MS alone.

Peaks 31 and 32 exhibited fragmentation patterns identical to those of compounds 29 and 30, but showed a 2 Da shift to higher mass in their deprotonated molecular ions, observed at m/z 537.1614, corresponding to the empirical formula C25H29O13. The MSn spectra and proposed fragmentation pathway are presented in Supplemental Figures S1 and S2. Neutral losses of 43.9898 Da (CO2, m/z 493.1714) and 162.0528 Da (hexosyl, m/z 375.1085) indicated the presence of a carboxylic acid group and a hexose moiety, respectively. The fragment ion at m/z 163.0399 (calculated 163.0401) and the neutral loss of 164.0473 Da (m/z 373.1140) support the presence of a coumaroyl unit. Additional fragmentation products closely resembled those of coumaroyl monotropein, although the aglycone unit exhibits a 2 Da mass increment. Based on these observations, compounds 31 and 32 are putatively assigned as coumaroyl-dihydromonotropein, previously identified in cranberries (23).

The peaks 20, 21, 24, and 25 were minor peaks with low intensities. Their MSn spectra are shown in Supplemental Figure S3. All four exhibited the same deprotonated molecular ion [M-H]- at m/z 553.1560, consistent with the empirical formula C25H29O14 (calculated 553.1563 Da). In the MS2 spectra, these compounds showed a common neutral loss of CO2 (43.9898 Da) from ion [M-H]-, yielding an ion at m/z 509.1664, and a loss of coumaric acid (164.0473 Da), producing the base peak at m/z 389.1089. The fragment ion at m/z 163.0399 (calculated 163.0401 Da) was assigned to the coumaroyl moiety.

In the MS3 spectra for the 553.2—>389.1 transition, a major ion at m/z 209.0455 was observed as the base peak for all four compounds, arising from sequential neutral losses of a hexosyl moiety (−162.0528 Da) followed by H2O (−18.0106 Da). Evidence for a hexose substituent was supported by characteristic neutral losses of 162 and 180 Da. Although the fragment corresponding to a 180 Da neutral loss dominated the spectra for all four compounds, the 162 Da neutral-loss fragment at m/z 227.0561 showed substantial variability in relative abundance (Table 2 and Supplemental Figure S3).

Compared with 31 and 32, compounds 20, 21, 24, and 25 each contain one additional oxygen atom, consistent with an extra hydroxyl substituent. According to previous reports, a hydroxyl group at C-8 and/or C-10 on the iridoid skeleton facilitates hydrogen-bond-assisted cleavage of the glycosidic bond to form the Y0 ion by losing 162 Da, whereas, the absence of hydroxyl at these positions precludes such hydrogen bonding and favors Z-type cleavage with a 180 Da neutral loss (36, 37; Figure 5). In this case, the fragment ion at m/z 227.0561 (162.0528 Da neutral loss from m/z 389.1089) served as a diagnostic indicator of hydroxyl substitution at C-8/C-10. Its absence in compound 20 suggests no hydroxyls at these positions, whereas compounds 21, 24, and 25 displayed this ion at 8, 69, and 52% relative intensity, respectively, consistent with C-8/C-10 hydroxylation but with possible stereochemical variation. Based on these data, compounds 20, 21, 24, and 25 were tentatively assigned as coumaroyl-hydroxyl-dihydromonotropein. Similarly, peak 33 was assigned as feruloyl monotropein, and peaks 34 and 36 were characterized as isomers of feruloyl dihydromonotropein.

Figure 5.

Diagram illustrating ion nomenclature for iridoid glycosides using monotropein as an example. The figure shows the positions of key cleavages that generate Y‑ and Z‑type fragment ions, highlighting how hydroxyl substitution at C‑8 or C‑10 facilitates hydrogen‑bond‑assisted glycosidic bond cleavage to form the Y₀ ion, while its absence favors Z‑type fragmentation.

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Ion nomenclature used for iridoid glycosides (illustrated on monotropein).

The deprotonated molecular ion [M-H]− of compounds 35 and 37 exhibited an immediate neutral loss of CO (27.9949 Da), producing a base-peak ion [M-H-CO]- at m/z 535.1459, This ion was identical to the [M-H]− ion of coumaroyl monotropein (compounds 29 and 30), and subsequent fragmentation followed the same pattern observed for 29 and 30. Pending confirmation of the CO origin, we designated them as decarbonylating coumaroyl monotropein isomer. Similarly, compounds 38 and 39 underwent an MS/MS cascade identical to coumaroyl dihydromonotropein (compounds 31 and 32) after a neutral loss of CO; accordingly, they were assigned as decarbonylating coumaroyl dihydromonotropein isomer.

Although complete structural elucidation could not be achieved by MS alone, compounds 20, 21, 24, 25, and 34–39 are, to the best of our knowledge, reported here for the first time in the genus Vaccinium.

The role of iridoid glycosides in cultivar separation was further supported by the heatmap clustering (Figure 7), which grouped samples and metabolites based on abundance patterns. WE showed a unique accumulation pattern of iridoid-related features, suggesting that these compounds could serve as chemotaxonomic markers.

Figure 7.

3D loadings plot and heatmap from the sPLS‑DA model showing metabolites that drive cultivar discrimination. Panel A displays variables with high contributions along Loadings 1–3, highlighting iridoid features enriched in the Welker cultivar and unidentified features enriched in Crimson Queen. Panel B shows a heatmap of selected variables ranked by absolute loading values, illustrating cultivar‑specific abundance patterns across the six cranberry cultivars.

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(A) 3D loadings plot of the sPLS-DA model (Loadings 1–3). Variables with high contributions in Loading 1 were labeled. The distribution highlights the key metabolites driving cultivar discrimination. (B) Heatmap showing the variables selected by the sPLS-DA model for Component 1. The variables are ranked by the absolute values of their loadings, with iridoid features (m/z 553.16_11.8, 553.16_13.44, 553.16_12.06, 565.16_21.56) enriched in WE and unidentified features (m/z 579.18_15.16, 607.18_15.12, 623.17_15.10) enriched in CQ. The accompanying heatmap displays relative abundance across six cranberry cultivars, confirming cultivar-specific metabolic signatures. Abbreviations: CQ = Crimson Queen; DM = Demoranville; HA = Haines; MQ = Mullica Queen; ST = Stevens; WE = Welker.

Hydroxycinnamic Acid Sugar Derivatives

Hydroxycinnamic acids (HCAs) can form covalent linkages with glycosyl residues via either esterification or etherification. Both conjugation types were detected in the cranberry samples analyzed. According to the previous studies, hydroxycinnamoyl sugar ester and hydroxycinnamic acid glycoside can be distinguished based on their UV and MS characteristics. Clifford et al reported that caffeic acid-4-β-D-glucoside and caffeic acid-3-β-D-glucoside did not exhibit sugar ring fission fragmentation, in contrast to caffeoyl ester (38). Määttä and coworkers provided supporting UV evidence: conjugated phenolic forms that display bathochromic shifts in UV-visible absorption spectra compared to their respective aglycons (shifts to longer wavelength) indicate esterification with sugars, whereas hypsochromic shifts to shorter wavelengths suggest glycosidation (39). According to Mane and co-workers, caffeic acid-4-O-β-D-hexoside isomers are characterized by the absence of a clear absorption band at λmax 320 nm (26). In the current study, as the response of some compounds were too low to obtain reliable UV spectra, MS fragmentation behavior was used as the main criteria to assign esters and glycosides of HCA and UV spectra as a supporting evidence. A total of 10 hydroxycinnamic acid-hexose conjugates were identified in this study, corresponding to the green-labeled peaks in Figure 2 and assigned as compounds 6, 7, 8, 10, 12, 13, 16, 18, 27 and 28 in Table 2.

sPLS-DA of Water-Eluted Metabolites

To further enhance interpretability and feature selection, we applied sPLS-DA to model the LC–MS dataset of the water-eluted LH-20 fractions. In sPLS-DA, the L1-penalization is applied to shrink the coefficients of weakly informative variables toward zero, effectively selecting only the most relevant features and reducing noise from redundant signals. This built-in feature-selection step makes the model more interpretable for high-dimensional LC–MS datasets (20, 21). This refinement allows the model to highlight a concise subset of metabolites that contribute most strongly to class separation, thereby facilitating biological interpretation and biomarker discovery.

The 3D scores plot of the sPLS-DA model (Figure 6) illustrates the distribution of samples from six cranberry cultivars (CQ, Crimson Queen; DM, Demoranville; HA, Haines; MQ, Mullica Queen; ST, Stevens; and WE, Welker) across components 1–3, which explained 16.9, 8.4, and 9.5% of the variance, respectively. Samples from the same cultivar clustered closely within or close to their respective confidence ellipsoids, while clear separation was observed among cultivars, reflecting distinct metabolites captured by the sparse model. The pooled QC samples clustered tightly at the center of the plot, confirming analytical reproducibility and stability across the dataset. This separation reflects underlying differences in metabolite abundance derived from biosynthetic pathways or cultivar-specific responses to environmental conditions. To identify the compounds driving this separation, the highest absolute loadings were visualized in Figure 7.

Figure 6.

Three‑dimensional sPLS‑DA scores plot showing samples from six cranberry cultivars forming distinct clusters along Components 1–3, with pooled QC samples clustering tightly at the center. The figure illustrates clear group separation based on selected discriminative metabolites and demonstrates strong analytical reproducibility.

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3D scores plot of the sPLS-DA model (Components 1–3). Samples from six cranberry cultivars (CQ, DM, HA, MQ, ST, and WE) form distinct clusters, while pooled QC samples cluster centrally, indicating analytical reproducibility. Group separation reflects the discriminative power of the sparse model based on a reduced set of selected metabolites.

The 3D loadings plot of the sPLS-DA model (Figure 7A) illustrates the spatial distribution of discriminative features across the first three latent components. Features with strong positive or negative contributions along each axis represent the most influential variables in driving cultivar separation on the corresponding latent dimension. Clusters of features positioned in similar regions of the loading space suggest correlated metabolites or structurally related compounds that contribute jointly to discrimination. Conversely, features located at opposite extremes of an axis highlight opposing features that distinguish one cultivar from another.

Notably, several of the top-ranked discriminative features were annotated as iridoid glycosides, like coumaroyl-substituted iridoid derivatives (Table 2). These compounds were predominantly enriched in the WE cultivar, contributing strongly to its separation from others. For example, ion features (m/z_Rt) 553.16_11.8, 553.16_13.44, 553.16_12.06, and 565.16_21.56, identified as iridoid compounds 20, 25, 21, and 33, respectively, grouped together along Loading 1 (Figure 7A), showed the highest or second-highest abundance in WE and the lowest levels in CQ (Figure 7B). These compounds contributed strongly to the positive side of Component 1, indicating that iridoid accumulation is a key driver of the distinct clustering of WE. In contrast, features 579.18_15.16, 607.18_15.12, and 623.17_15.10, which could not be confidently identified despite extensive annotation efforts, were most abundant in CQ and lowest in WE. Their strong negative loadings suggest that these unknown metabolites are equally important in distinguishing CQ from the other cultivars.

These distribution patterns confirm that iridoid glycosides are the principal discriminators singled out by the sparse algorithm, aligning with the MS-based structural evidence presented in Section of Putative identification of iridoid glycosides. They provide chemotaxonomic markers useful for cultivar authentication and lay the groundwork for exploring genotype-specific biosynthetic regulation.

Conclusions

This study characterized the highly polar, water-soluble metabolites of six cranberry cultivars, identifying 39 annotated compounds, including a diverse set of iridoid glycosides and hydroxycinnamic acid derivatives. The observed cultivar-specific differences in iridoid glycosides and other polar metabolites highlight their chemotaxonomic potential and provide preliminary insights that may inform future work in areas such as food authentication and cultivar selection.

Supplementary Material

qsag025_Supplementary_Data
qsag025_supplementary_data.zip (2.6MB, zip)

Acknowledgments

The authors declare no conflict of interest.

Contributor Information

Ping Geng, U.S. Department of Agriculture, Methods and Application of Food Composition Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, Beltsville, MD 20705, United States.

Jianghao Sun, U.S. Department of Agriculture, Methods and Application of Food Composition Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, Beltsville, MD 20705, United States.

Pei Chen, U.S. Department of Agriculture, Methods and Application of Food Composition Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, Beltsville, MD 20705, United States.

Author Contributions

Ping Geng (Data curation [Equal], Formal analysis [Equal], Methodology [Equal], Writing—original draft [Equal]), Jianghao Sun (Data curation [Equal], Investigation [Equal], Writing—review & editing [Equal]), and Pei Chen (Conceptualization [Lead], Funding acquisition [Lead], Investigation [Equal], Methodology [Equal], Project administration [Lead], Supervision [Lead], Writing—review & editing [Lead])

Supplemental Information

Supplemental information is available on the J. AOAC Int. website.

Funding

This research is supported by the Agricultural Research Service of the U.S. Department of Agriculture and an Interagency Agreement with the Office of Dietary Supplements of the National Institutes of Health (AOD19006-001-00001).

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