Skip to main content
Data in Brief logoLink to Data in Brief
. 2019 Jul 26;25:104268. doi: 10.1016/j.dib.2019.104268

Analytical dataset on volatile compounds of cocoa bean shells from different cultivars and geographical origins

Letricia Barbosa-Pereira a,, Olga Rojo-Poveda a,b, Ilario Ferrocino a, Manuela Giordano a, Giuseppe Zeppa a
PMCID: PMC6700496  PMID: 31453285

Abstract

This data article describes the analysis of volatile organic compounds (VOCs) in 44 samples of cocoa bean shells (CBS) obtained from cocoa beans of diverse cultivars and collected in different geographical origins. The volatile compounds were extracted by headspace solid-phase microextraction (HS-SPME) method and then analyzed by gas chromatography coupled to a quadrupole mass spectrometry GC-qMS. The retention times, identification and semi-quantification of 101 VOCs are reported. Data collected on the volatile profile of CBS samples using E-nose analysis are also available. Additional data related to physicochemical characteristics and color analysis for CBS samples are reported. Further interpretation and discussion on these datasets can be found in the related article entitled “Assessment of volatile fingerprint by HS-SPME/GC-qMS and E-nose for the classification of cocoa bean shells using chemometrics” (Barbosa-Pereira et al., 2019).

Keywords: Cocoa bean shell (CBS), Cocoa by-product, Volatile compounds, HS-SMPE/GC-qMS, E-nose

Abbreviations: CBS, cocoa bean shell; HS-SPME/GC-qMS, headspace solid-phase micro-extraction coupled with gas chromatography-quadrupole mass spectrometry; VOC, volatile organic compound; E-nose, electronic nose; DVB/CAR/PDMS, divinylbenzene/carboxen/ polydimethylsiloxane; ISTD, internal standard


Specifications table

Subject area Chemistry
More specific subject area Food Chemistry and Technology
Type of data Table (Microsoft Excel Worksheet)
How data was acquired HS-SPME/GC-qMS: Autosampler for HS-SPME (SPME COMBI PAL System, CTC Analytics AG, Zwingen, Switzerland) – Gas chromatography (GC-2010, Shimadzu Corporation, Kyoto, Japan) coupled with quadrupole mass spectrometer (QP-2010 Plus, Shimadzu Corporation, Kyoto, Japan)
E-nose: Portable electronic nose system PEN3 (Airsense Analytics GmbH., Schwerin, Germany)
Data format Raw, analyzed and formatted
Experimental factors Roasting of cocoa beans to obtain the cocoa bean shell (CBS); physical separation of CBSs; grinding of CBSs into a powder with a 250 μm mesh size
Experimental features Semi-quantification (μg 5-nonanol Eq. kg−1 of CBS) of the identified volatile compounds in CBS powders from different geographical origins and cultivars.
E-nose sensor responses for CBS powders from different geographical origins and cultivars.
Data source location GC-qMS and E-nose datasets were obtained at Food Technology Laboratory at the Department of Agriculture, Forestry and Food Sciences (DISAFA), University of Turin, Grugliasco, Italy
Cocoa beans samples from American countries: Brazil, Colombia, Dominican Republic, Ecuador, Jamaica, Mexico, Peru, Venezuela.
Cocoa beans samples from African countries: Cameroon, Congo, Ghana, Ivory Coast, Madagascar, São Tomé, Sierra Leone, Tanzania, Togo, Uganda.
The cocoa bean samples were kindly supplied by Silvio Bessone S.r.l., ICAM S.p.A., Ferrero International S.A., Guido Gobino S.r.l., Pastiglie Leone S.r.l., and Venchi S.p.A.
Data accessibility Data are presented in this article and in a Microsoft Excel Worksheet, which is available as supplementary data.
Related research article Barbosa-Pereira, L., Rojo-Poveda, O., Ferrocino, I., Giordano, M., & Zeppa, G. (2019). Assessment of volatile fingerprint by HS-SPME/GC-qMS and E-nose for the classification of cocoa bean shells using chemometrics. Food Research International, 123, 684–696. https://doi.org/10.1016/j.foodres.2019.05.041
Value of the data
  • These are the first data on the contents of VOCs in CBSs from different origins and cultivars determined by HS-SPME/GC-qMS

  • The dataset allows the selection of CBSs with specific flavor characteristics according to the food application

  • The present data contribute to the chemical characterization and add-value of this cocoa by-product as food ingredient

  • The data can be used for reference of volatiles quantification and allow other researchers to extend the statistical analysis

  • The datasets from both techniques GC-qMS and E-nose may be useful for developing rapid detection methods for CBS origin and cultivar authentication

1. Data

The dataset collected for 44 CBS samples from different cultivars and geographical origins is presented in four segments of data: A) Samples information regarding the cultivar and geographical origin is labeled in Table 1; B) Physicochemical characterization of CBS samples is described in Table 2; C) The experimental retention index, names and contents of the volatile organic compounds (VOCs) identified among the CBSs determined by HS-SPME/GC-qMS are described indetail in Table S1 (Microsoft Excel Worksheet in supplementary material) and the total contents of each categorie of volatile compounds are summarized in Table 3; and D) The dataset obtained from E-nose sensors for CBS samples is described in Table 4.

Table 1.

Cocoa beans from different cultivars and origin used to obtain the CBSs.

Sample code Cultivar Country of origin
BRA Trinitario Brazil
CAM1 Forastero Cameroon
CAM2 Trinitario Cameroon
COL1 Forastero Colombia
COL2 Trinitario Colombia
CON1 Forastero Congo
CON2 Forastero Congo
DOR1 Trinitario Dominican Republic
DOR2 Forastero Dominican Republic
DOR3 Trinitario Dominican Republic
DOR4 Trinitario Dominican Republic
ECU1 Forastero Ecuador
ECU2 Trinitario Ecuador
ECU3 Forastero Ecuador
ECU4 Nacional Ecuador
ECU5 Nacional Ecuador
ECU6 Forastero Ecuador
ECU7 Criollo Ecuador
GHA Forastero Ghana
IVC Forastero Ivory Coast
JAM Trinitario Jamaica
MAD Forastero Madagascar
MEX Trinitario Mexico
PER1 Forastero Peru
PER2 Trinitario Peru
SAT1 Forastero São Tomé
SAT2 Forastero São Tomé
SAT3 Forastero São Tomé
SLE Forastero Sierra Leone
TAN Forastero Tanzania
TOG1 Forastero Togo
TOG2 Forastero Togo
UGA1 Forastero Uganda
UGA2 Forastero Uganda
VEN1 Trinitario Venezuela
VEN2 Trinitario Venezuela
VEN3 Trinitario Venezuela
VEN4 Trinitario Venezuela
VEN5 Criollo Venezuela
VEN6 Trinitario Venezuela
VEN7 Criollo Venezuela
VEN8 Criollo Venezuela
VEN9 Criollo Venezuela
VEN10 Criollo Venezuela

Table 2.

Moisture, pH, titratable acidity, and CIELab values of the CBSs powders obtained from cocoa beans from different origins and cultivars.

Sample Moisturea pH Titratable Acidityb L* a* b*
BRA 6.97 ± 0.38 4.06 ± 0.02 0.69 ± 0.00 40.15 ± 1.19 11.46 ± 0.61 22.98 ± 1.98
CAM1 5.46 ± 0.68 5.32 ± 0.01 0.22 ± 0.00 41.63 ± 0.87 11.96 ± 0.13 21.21 ± 0.45
CAM2 9.15 ± 0.27 6.31 ± 0.02 0.11 ± 0.00 36.23 ± 1.36 12.32 ± 0.15 21.01 ± 0.54
COL1 7.48 ± 0.25 4.83 ± 0.44 0.55 ± 0.24 42.88 ± 2.20 12.21 ± 0.34 21.78 ± 1.31
COL2 7.80 ± 0.75 5.42 ± 0.14 0.43 ± 0.05 36.94 ± 0.91 14.92 ± 1.01 26.24 ± 4.93
CON1 7.52 ± 0.30 4.90 ± 0.01 0.38 ± 0.00 43.72 ± 0.94 12.48 ± 0.17 24.58 ± 0.53
CON2 7.25 ± 0.34 5.20 ± 0.07 0.33 ± 0.03 40.22 ± 0.64 12.70 ± 0.23 23.28 ± 1.73
DOR1 6.44 ± 0.12 5.51 ± 0.03 0.47 ± 0.00 40.86 ± 1.42 14.03 ± 0.69 23.47 ± 0.77
DOR2 7.19 ± 0.48 5.58 ± 0.01 0.31 ± 0.05 45.91 ± 0.78 11.63 ± 0.71 22.16 ± 0.98
DOR3 7.85 ± 0.18 4.61 ± 0.01 0.42 ± 0.00 44.84 ± 0.83 12.15 ± 0.17 23.33 ± 0.47
DOR4 7.47 ± 0.27 5.42 ± 0.03 0.37 ± 0.01 32.66 ± 2.37 16.02 ± 1.18 32.20 ± 2.47
ECU1 8.45 ± 0.37 5.05 ± 0.18 0.32 ± 0.02 41.65 ± 1.21 13.61 ± 1.59 23.94 ± 1.30
ECU2 6.89 ± 0.41 4.97 ± 0.04 0.29 ± 0.01 45.81 ± 1.46 10.95 ± 0.77 21.29 ± 1.87
ECU3 6.74 ± 0.96 5.71 ± 0.01 0.34 ± 0.01 42.22 ± 2.01 14.62 ± 0.79 25.29 ± 0.62
ECU4 6.05 ± 0.14 5.71 ± 0.01 0.18 ± 0.00 35.98 ± 5.83 15.13 ± 0.67 30.74 ± 2.83
ECU5 7.12 ± 0.19 5.68 ± 0.01 0.23 ± 0.00 39.48 ± 9.47 14.19 ± 1.60 28.73 ± 4.87
ECU6 8.13 ± 0.38 4.96 ± 0.03 0.38 ± 0.00 47.01 ± 1.62 12.85 ± 0.71 26.22 ± 2.59
ECU7 6.46 ± 0.35 6.34 ± 0.01 0.18 ± 0.00 35.58 ± 0.93 12.89 ± 0.57 18.09 ± 0.89
GHA 9.18 ± 0.33 5.40 ± 0.08 0.18 ± 0.02 40.44 ± 3.39 12.54 ± 1.19 23.63 ± 1.18
IND 7.01 ± 0.18 5.49 ± 0.01 0.20 ± 0.01 42.33 ± 0.22 11.11 ± 0.35 20.72 ± 0.64
IVC 7.39 ± 1.44 5.46 ± 0.11 0.18 ± 0.01 35.45 ± 4.45 11.38 ± 0.29 19.45 ± 2.38
JAM 7.19 ± 0.35 6.32 ± 0.02 0.27 ± 0.00 35.68 ± 0.68 13.05 ± 0.23 21.06 ± 1.65
MAD 6.97 ± 0.36 4.96 ± 0.01 0.53 ± 0.01 43.12 ± 0.78 14.25 ± 0.06 23.92 ± 0.82
MEX 6.02 ± 1.46 5.24 ± 0.09 0.64 ± 0.03 36.34 ± 1.13 15.19 ± 0.55 23.88 ± 2.12
PER1 7.80 ± 1.03 5.57 ± 0.46 0.36 ± 0.23 36.90 ± 5.93 14.41 ± 2.43 24.72 ± 2.78
PER2 6.37 ± 0.67 5.17 ± 0.27 0.64 ± 0.15 39.61 ± 1.88 14.81 ± 0.52 25.29 ± 3.36
SAT1 6.95 ± 0.88 4.97 ± 0.03 0.40 ± 0.01 39.15 ± 1.70 15.92 ± 0.73 28.96 ± 5.04
SAT2 7.99 ± 0.39 5.32 ± 0.03 0.43 ± 0.01 42.36 ± 1.21 13.29 ± 0.41 24.15 ± 0.84
SAT3 6.87 ± 0.40 5.78 ± 0.05 0.35 ± 0.01 38.79 ± 0.92 13.49 ± 0.31 21.76 ± 1.15
SLE 7.12 ± 0.51 4.14 ± 0.02 0.70 ± 0.00 42.30 ± 2.24 11.03 ± 0.26 23.17 ± 0.61
TAN 6.70 ± 0.71 4.68 ± 0.11 0.42 ± 0.01 42.84 ± 0.80 11.93 ± 0.75 23.84 ± 1.61
TOG1 6.67 ± 0.90 4.33 ± 0.05 0.60 ± 0.01 41.48 ± 2.05 12.10 ± 0.76 24.58 ± 0.70
TOG2 7.68 ± 0.24 4.40 ± 0.19 0.28 ± 0.11 41.31 ± 2.21 12.56 ± 0.49 26.00 ± 1.41
UGA1 9.22 ± 0.79 5.19 ± 0.31 0.23 ± 0.09 43.63 ± 1.18 10.89 ± 0.54 22.25 ± 1.10
UGA2 7.92 ± 0.65 5.42 ± 0.04 0.16 ± 0.01 44.47 ± 1.32 11.01 ± 0.64 22.91 ± 1.11
VEN1 6.01 ± 0.81 4.82 ± 0.10 0.32 ± 0.01 46.28 ± 1.73 11.44 ± 0.65 23.08 ± 1.00
VEN2 6.23 ± 1.01 4.58 ± 0.09 0.48 ± 0.01 36.63 ± 0.44 14.34 ± 0.81 30.32 ± 1.63
VEN3 7.43 ± 0.88 6.02 ± 0.14 0.21 ± 0.02 37.89 ± 0.40 14.71 ± 0.21 26.46 ± 0.77
VEN4 6.30 ± 1.44 5.01 ± 0.31 0.36 ± 0.02 37.81 ± 1.22 13.85 ± 0.39 28.72 ± 1.56
VEN5 7.34 ± 0.80 5.94 ± 0.04 0.22 ± 0.03 35.36 ± 1.06 12.26 ± 0.36 20.83 ± 2.23
VEN6 7.44 ± 1.27 5.26 ± 0.02 0.39 ± 0.02 39.29 ± 2.82 14.29 ± 0.98 26.71 ± 4.44
VEN7 5.46 ± 0.45 5.95 ± 0.01 0.25 ± 0.00 37.97 ± 0.50 13.64 ± 0.28 19.86 ± 0.41
VEN8 5.63 ± 0.11 6.02 ± 0.01 0.27 ± 0.00 36.63 ± 1.02 13.56 ± 0.22 18.48 ± 0.54
VEN9 5.68 ± 0.11 5.49 ± 0.01 0.32 ± 0.00 40.78 ± 0.91 12.95 ± 0.13 21.35 ± 0.26
VEN10 5.74 ± 0.12 4.65 ± 0.02 0.55 ± 0.00 40.83 ± 0.87 12.37 ± 0.22 20.69 ± 0.41
a

Moisture expressed as % wt/wt.

b

Titratable acidity (% acetic acid wt/wt) =((N*V*Eqwt)/(wt*1000)) *100.

Table 3.

Total contents of the different categories of volatile compounds determined in CBS powders from different geographical origins and cultivars.

Sample Concentrationa (μg kg−1 of CBS)
∑ Aldehydes ∑ Ketones ∑ Sulfur compounds ∑ Esters ∑ Hydrocarbons ∑ Furan derivates ∑ Pyrazines ∑ Alcohols ∑ Pyrroles ∑ Terpenes/Isoprenoids ∑ Acids ∑ Lactones ∑ Others ∑ Total
BRA 2621.6 411.0 18.6 106.1 324.0 2225.5 405.4 348.5 467.5 349.3 462.9 99.2 25.8 7865.3
CAM1 2151.5 574.5 39.6 83.5 268.3 299.0 746.1 383.6 112.8 77.9 210.5 58.9 20.5 5026.7
CAM2 2353.0 995.8 23.1 90.9 569.1 279.6 499.0 696.8 143.6 68.5 100.4 133.5 60.2 6013.3
COL1 3088.4 638.0 68.3 282.6 119.0 418.5 865.1 682.2 167.3 126.2 1351.6 79.2 52.0 7604.7
COL2 4164.0 705.4 227.7 207.7 348.4 162.1 2240.4 619.5 112.6 111.9 490.5 32.9 116.4 9268.4
CON1 2948.6 763.4 82.4 297.8 205.8 436.2 1140.8 903.4 185.1 136.7 1189.4 87.2 27.8 7849.5
CON2 3773.8 665.6 76.5 199.4 290.5 534.8 1125.7 428.5 157.6 109.6 526.8 68.2 30.7 7907.8
DOR1 5123.9 546.1 577.4 158.1 242.5 193.9 2671.0 477.5 133.7 139.7 818.2 12.6 47.9 11013.4
DOR2 3820.2 605.5 181.4 178.3 244.0 304.0 1902.3 424.9 114.4 166.5 543.3 33.8 36.0 8478.2
DOR3 1894.5 597.4 11.9 359.7 298.2 677.7 505.5 759.2 111.5 270.2 795.3 49.5 18.2 5938.1
DOR4 4544.6 695.8 365.1 188.3 309.8 137.6 3272.7 824.6 214.3 120.6 1807.6 227.3 47.1 12279.3
ECU1 2244.0 564.8 37.6 199.1 261.3 345.0 689.3 514.1 142.5 93.4 871.6 33.3 20.6 5851.0
ECU2 2520.8 624.0 21.0 167.0 376.5 474.5 685.4 869.6 165.1 103.8 442.0 65.5 29.4 6023.6
ECU3 2871.3 431.1 184.3 176.2 137.7 196.6 1070.3 329.9 70.5 87.3 359.1 5.4 21.5 5959.7
ECU4 3883.3 990.1 113.4 312.5 251.7 164.4 1377.1 1908.8 200.6 107.3 1692.7 201.7 32.5 9675.7
ECU5 2772.7 1181.0 50.3 288.7 191.9 129.9 2784.3 1068.1 86.6 134.2 1255.1 30.8 33.8 9287.9
ECU6 2596.9 746.0 44.0 582.2 248.7 117.6 2112.0 779.9 92.1 52.1 1012.7 49.6 16.2 8018.6
ECU7 4405.9 1362.2 103.4 524.9 10.9 116.2 1916.0 2310.2 62.5 228.0 2124.0 4.4 37.6 11244.4
GHA 2020.7 832.8 29.3 112.2 413.7 233.7 940.0 667.3 116.2 221.8 1159.9 452.8 28.4 6909.8
IVC 2542.1 1076.1 31.3 73.4 1108.5 404.1 1263.5 876.3 235.3 124.1 1154.3 565.7 29.5 8956.4
JAM 4244.8 818.7 159.1 148.1 176.4 135.8 1697.8 1000.3 91.1 186.0 327.2 18.4 151.1 8503.0
MAD 4398.3 628.4 371.2 478.6 292.4 166.0 5140.7 277.1 198.8 66.5 1895.2 20.3 82.8 14087.7
MEX 4425.8 797.5 280.1 586.7 584.1 157.8 4353.5 319.2 79.3 48.8 1299.0 14.2 88.8 13034.6
PER1 4112.6 1044.0 197.8 1036.6 128.7 193.4 3031.1 1309.3 180.8 87.8 2155.1 214.8 34.1 13726.0
PER2 4951.5 979.8 281.1 830.8 319.8 227.0 4515.2 1084.9 183.6 74.4 1257.7 241.2 41.4 14988.4
SAT1 3163.1 343.8 150.7 127.9 200.2 213.0 1009.9 135.8 175.5 76.1 508.2 6.4 77.5 6188.1
SAT2 3669.2 514.0 158.6 312.9 145.7 153.7 1851.9 384.0 61.1 70.6 747.8 12.7 87.9 8170.2
SAT3 5122.6 846.7 284.3 195.8 370.9 174.3 2801.4 364.2 71.0 65.6 436.0 14.1 116.1 10863.0
SLE 1698.4 461.5 26.3 236.9 349.1 2004.0 198.3 474.0 157.0 133.7 1148.1 74.2 14.9 6976.5
TAN 3335.2 642.2 97.4 868.4 391.6 920.7 1131.5 934.5 277.2 154.8 1494.7 100.1 32.5 10380.7
TOG1 1712.2 291.7 29.3 316.6 318.2 2081.0 309.4 428.6 203.9 222.4 1404.0 367.8 16.9 7701.9
TOG2 1444.8 375.3 24.7 434.8 218.8 1918.9 272.5 439.5 182.0 183.7 1121.5 572.7 20.8 7210.2
UGA1 2666.4 747.9 33.6 385.6 635.9 399.8 1359.1 1032.2 161.7 119.4 1404.7 32.7 27.9 9007.0
UGA2 2849.8 826.1 28.5 201.8 227.7 354.2 792.6 880.5 142.4 114.8 421.1 13.5 26.5 6879.5
VEN1 1731.1 373.8 40.0 255.6 162.9 486.3 1176.0 266.6 112.0 65.7 832.5 25.4 20.8 5548.7
VEN2 2657.9 275.9 166.9 259.4 282.6 449.3 838.7 296.3 267.9 92.7 826.6 46.8 18.1 6479.0
VEN3 2621.5 421.0 43.3 146.1 142.8 101.1 665.2 302.6 48.7 43.4 338.6 6.7 29.6 4910.2
VEN4 1690.7 419.0 58.9 205.9 161.5 330.0 1138.0 514.6 118.4 124.0 503.6 11.8 16.5 5292.9
VEN5 4536.5 1115.8 115.4 308.1 536.7 294.3 3167.8 1046.2 114.0 174.5 329.7 49.9 55.3 11844.2
VEN6 2309.5 467.9 87.2 230.6 89.4 98.8 1702.0 492.5 67.3 102.3 484.1 11.2 20.5 6163.3
VEN7 4260.7 726.7 186.3 356.7 20.3 153.2 3918.3 1355.8 119.0 91.8 1654.9 10.3 40.6 12894.7
VEN8 4843.8 715.6 146.2 604.7 16.7 112.1 3731.4 891.2 84.6 47.0 1354.1 3.3 52.6 12603.4
VEN9 2977.5 593.9 132.5 760.0 21.0 487.9 5285.7 895.2 101.9 66.7 2100.8 29.2 26.8 13479.0
a

The total amounts of each category of volatile compounds semi-quantified as 5-nonanol equivalents (μg kg−1of CBS). Data are presented as the sum (∑) of the means of the different molecules (n = 6) for each category.

Table 4.

Average of E-nose sensor responses G/G0 (area under the curve; G and G0 stand for the conductance of the MOS connected with the sample and clean gas, respectively), expressed as resistivity (Ohm), for CBS powders from different geographical origins and cultivars.

Sample E-nose Sensors
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10
BRA 21.67 ± 2.71 7066.91 ± 1413.30 25.19 ± 3.39 92.05 ± 1.00 28.58 ± 4.00 1640.40 ± 280.07 2553.81 ± 476.52 662.21 ± 154.87 1335.16 ± 217.20 101.07 ± 2.77
CAM1 24.32 ± 0.54 6471.08 ± 88.20 28.25 ± 0.42 89.67 ± 0.46 31.76 ± 0.37 1319.07 ± 16.36 1820.72 ± 68.53 481.43 ± 3.10 968.75 ± 47.29 98.44 ± 0.79
CAM2 19.44 ± 0.95 7476.53 ± 298.55 22.38 ± 0.72 93.80 ± 0.77 25.06 ± 0.59 1845.28 ± 72.22 2545.55 ± 249.02 764.69 ± 127.89 1291.37 ± 135.09 104.36 ± 0.74
COL1 21.82 ± 3.63 7363.27 ± 2195.01 25.39 ± 4.30 89.32 ± 0.98 28.52 ± 5.11 1586.49 ± 310.26 2746.75 ± 698.52 642.11 ± 166.67 1446.12 ± 309.88 97.98 ± 1.68
COL2 21.56 ± 2.58 7984.51 ± 1821.07 25.27 ± 3.04 90.98 ± 0.76 28.77 ± 3.38 1671.50 ± 269.72 2684.13 ± 479.19 634.39 ± 98.26 1423.07 ± 177.29 105.14 ± 3.46
CON1 19.13 ± 0.19 9443.36 ± 142.67 21.85 ± 0.17 89.55 ± 0.15 24.02 ± 0.12 1783.55 ± 5.68 3202.33 ± 74.91 698.94 ± 4.27 1655.81 ± 39.61 95.44 ± 0.29
CON2 18.68 ± 0.32 7708.13 ± 391.41 22.23 ± 0.74 92.90 ± 3.38 24.37 ± 0.96 2028.79 ± 338.99 2747.88 ± 198.67 788.78 ± 110.15 1375.48 ± 99.26 102.99 ± 8.56
DOR1 16.56 ± 2.26 11829.35 ± 2456.92 19.06 ± 2.66 92.44 ± 1.10 20.69 ± 3.62 2214.38 ± 211.63 3502.73 ± 442.91 924.91 ± 174.36 1687.11 ± 144.54 104.98 ± 2.07
DOR2 17.31 ± 2.33 10941.95 ± 2082.60 19.88 ± 2.78 93.02 ± 1.15 21.94 ± 3.82 2103.86 ± 257.59 3287.51 ± 478.67 892.91 ± 218.62 1601.99 ± 193.46 107.83 ± 1.92
DOR3 18.92 ± 0.09 8003.06 ± 28.39 21.80 ± 0.10 89.65 ± 0.19 23.68 ± 0.12 1790.95 ± 7.24 2610.91 ± 9.35 689.22 ± 4.38 1352.05 ± 12.65 93.67 ± 0.46
DOR4 18.70 ± 0.30 9687.95 ± 151.09 21.39 ± 0.28 89.04 ± 0.17 22.98 ± 0.32 1688.86 ± 15.24 2735.65 ± 138.98 723.46 ± 39.29 1474.48 ± 93.49 94.81 ± 0.42
ECU1 19.25 ± 0.66 8914.25 ± 235.90 22.29 ± 0.66 92.37 ± 0.95 25.19 ± 1.02 1896.56 ± 51.70 2971.79 ± 263.95 770.30 ± 119.21 1484.32 ± 125.55 108.43 ± 4.02
ECU2 21.48 ± 2.38 7361.28 ± 1716.64 25.02 ± 2.93 92.16 ± 1.60 28.38 ± 3.22 1627.18 ± 224.41 2601.30 ± 687.30 674.54 ± 171.02 1328.67 ± 327.70 104.41 ± 5.57
ECU3 19.20 ± 0.17 9074.20 ± 990.59 21.98 ± 0.27 92.43 ± 0.32 24.84 ± 0.49 1861.96 ± 33.03 3179.05 ± 173.82 745.17 ± 86.90 1625.42 ± 92.66 102.99 ± 0.65
ECU4 19.31 ± 0.35 9609.19 ± 149.24 21.66 ± 0.36 90.40 ± 0.06 22.98 ± 0.35 1601.76 ± 9.61 2313.98 ± 33.13 682.87 ± 3.95 1278.82 ± 26.81 93.15 ± 0.33
ECU5 19.67 ± 0.24 9341.42 ± 548.90 22.11 ± 0.22 89.85 ± 0.48 23.72 ± 0.18 1560.93 ± 21.72 2513.20 ± 60.96 699.18 ± 40.12 1434.21 ± 79.14 93.54 ± 0.42
ECU6 21.94 ± 5.00 8693.98 ± 1899.47 25.73 ± 5.29 90.12 ± 1.78 29.65 ± 6.17 1681.91 ± 457.92 3156.50 ± 804.72 673.04 ± 249.26 1631.93 ± 327.72 105.49 ± 4.77
ECU7 55.71 ± 3.48 1514.15 ± 270.69 57.24 ± 2.92 89.38 ± 0.15 55.23 ± 2.88 574.67 ± 47.85 1646.82 ± 105.21 242.67 ± 12.20 620.72 ± 11.39 90.93 ± 0.14
GHA 18.93 ± 0.36 8274.79 ± 644.82 21.89 ± 0.35 91.25 ± 0.60 24.46 ± 0.64 1890.14 ± 35.03 2797.61 ± 134.52 750.80 ± 85.80 1437.51 ± 99.64 102.10 ± 1.30
IND 25.30 ± 0.38 4874.39 ± 190.58 29.38 ± 0.17 89.00 ± 0.18 33.11 ± 0.16 1267.68 ± 21.82 1860.06 ± 176.65 490.07 ± 58.38 1011.30 ± 94.65 95.67 ± 1.32
IVC 17.69 ± 1.56 10417.91 ± 1928.62 20.13 ± 2.10 92.39 ± 1.03 21.78 ± 3.07 1984.29 ± 149.63 3021.47 ± 257.57 806.68 ± 112.71 1463.84 ± 52.88 103.01 ± 1.00
JAM 21.28 ± 2.66 8305.77 ± 890.69 24.61 ± 3.32 90.71 ± 1.13 27.63 ± 3.90 1657.18 ± 277.15 2551.96 ± 390.84 625.90 ± 106.84 1318.46 ± 165.16 101.70 ± 1.82
MAD 13.60 ± 0.84 14944.91 ± 211.24 15.86 ± 0.72 96.09 ± 2.33 16.73 ± 0.61 2685.12 ± 243.43 4464.91 ± 292.33 1188.71 ± 126.59 2072.15 ± 209.74 110.69 ± 2.45
MEX 21.34 ± 2.60 8754.26 ± 1136.11 25.38 ± 2.72 88.62 ± 1.49 29.06 ± 2.95 1768.73 ± 244.89 3489.61 ± 318.57 711.20 ± 127.84 1833.66 ± 136.42 103.28 ± 4.36
PER1 18.77 ± 0.74 9253.63 ± 1623.86 21.81 ± 0.70 90.92 ± 1.92 24.29 ± 0.89 1880.98 ± 57.96 3181.73 ± 472.45 747.68 ± 78.93 1615.90 ± 224.08 99.39 ± 1.56
PER1 20.64 ± 1.96 7902.55 ± 518.35 24.35 ± 2.86 89.04 ± 1.60 27.48 ± 4.08 1660.89 ± 130.46 3012.20 ± 370.89 648.06 ± 62.76 1603.76 ± 215.80 97.88 ± 2.80
SAT1 19.27 ± 0.64 10015.39 ± 226.20 22.80 ± 0.67 92.18 ± 0.53 25.83 ± 0.67 1935.85 ± 74.23 3239.05 ± 294.67 739.29 ± 96.39 1618.85 ± 135.67 107.17 ± 4.15
SAT2 17.86 ± 0.31 8806.54 ± 316.45 21.02 ± 0.21 89.04 ± 0.21 23.17 ± 0.18 1891.34 ± 58.48 3252.55 ± 346.96 756.60 ± 81.73 1685.34 ± 174.02 95.69 ± 0.61
SAT3 21.71 ± 3.10 7747.48 ± 2194.60 25.27 ± 3.67 90.38 ± 2.17 28.59 ± 3.98 1629.68 ± 315.13 2614.14 ± 612.93 616.95 ± 146.59 1367.30 ± 262.75 104.55 ± 7.31
SLE 19.64 ± 0.41 8540.30 ± 429.64 22.36 ± 0.36 89.93 ± 0.12 24.78 ± 0.41 1710.06 ± 9.25 3138.62 ± 263.82 725.62 ± 94.95 1671.07 ± 158.77 93.93 ± 0.38
TAN 21.13 ± 3.55 7226.23 ± 1776.82 24.68 ± 4.17 89.28 ± 0.24 27.58 ± 5.02 1579.49 ± 237.78 2577.40 ± 619.43 631.37 ± 130.42 1381.09 ± 342.89 96.31 ± 0.57
TOG1 18.42 ± 0.25 9096.71 ± 226.91 21.47 ± 0.19 90.55 ± 0.32 23.92 ± 0.24 1949.76 ± 22.78 3190.16 ± 163.26 759.22 ± 68.24 1616.83 ± 85.04 100.49 ± 0.46
TOG2 18.04 ± 0.38 9144.50 ± 648.34 21.21 ± 0.40 90.01 ± 0.38 23.57 ± 0.57 1913.86 ± 42.76 3269.53 ± 256.43 754.92 ± 72.44 1690.98 ± 143.82 96.44 ± 0.62
UGA1 18.67 ± 0.59 8883.83 ± 605.31 21.60 ± 0.53 90.86 ± 0.23 23.98 ± 0.65 1910.96 ± 51.92 2972.22 ± 200.97 750.44 ± 74.80 1489.62 ± 96.00 100.91 ± 0.10
UGA2 18.81 ± 0.40 7748.48 ± 512.48 21.64 ± 0.39 91.36 ± 0.84 23.57 ± 0.61 1741.24 ± 33.06 2514.97 ± 110.86 696.66 ± 56.16 1331.73 ± 92.41 94.93 ± 0.70
VEN1 18.49 ± 0.29 9322.05 ± 407.03 21.54 ± 0.40 92.34 ± 0.21 24.50 ± 0.71 2058.22 ± 45.29 3233.22 ± 257.10 808.82 ± 99.68 1629.05 ± 148.69 105.62 ± 0.72
VEN2 18.76 ± 1.04 12279.52 ± 340.46 22.22 ± 0.95 91.30 ± 0.68 25.54 ± 1.24 1943.82 ± 153.76 3815.66 ± 368.50 758.72 ± 112.61 1886.10 ± 163.19 107.07 ± 0.89
VEN3 19.30 ± 0.45 8369.49 ± 276.09 22.62 ± 0.37 91.98 ± 0.41 25.87 ± 0.43 1915.56 ± 59.49 2746.92 ± 218.96 754.34 ± 106.30 1400.97 ± 108.24 108.49 ± 0.97
VEN4 18.93 ± 0.36 9151.03 ± 178.30 22.15 ± 0.43 92.52 ± 0.27 25.34 ± 0.62 1964.55 ± 25.49 3058.53 ± 264.09 782.13 ± 116.28 1526.11 ± 132.82 109.71 ± 0.38
VEN5 21.03 ± 2.71 7408.06 ± 1578.26 24.48 ± 3.39 90.95 ± 1.43 27.63 ± 3.90 1710.84 ± 300.91 2658.24 ± 496.27 689.56 ± 168.85 1381.97 ± 217.53 101.56 ± 2.34
VEN6 23.26 ± 4.43 8058.15 ± 1845.73 26.77 ± 4.80 91.10 ± 1.07 30.30 ± 5.32 1561.91 ± 395.52 2547.69 ± 615.04 610.96 ± 145.44 1340.39 ± 228.82 105.14 ± 5.51
VEN7 57.71 ± 1.33 4055.88 ± 116.29 58.42 ± 1.25 88.99 ± 0.31 55.57 ± 1.39 582.72 ± 25.23 1746.05 ± 47.27 216.82 ± 7.81 727.72 ± 21.74 90.95 ± 0.04
VEN8 56.64 ± 2.34 2684.49 ± 119.20 57.39 ± 2.30 88.57 ± 0.06 54.95 ± 2.17 583.64 ± 40.85 1657.69 ± 78.70 226.60 ± 10.67 654.89 ± 19.80 91.00 ± 0.24
VEN9 55.58 ± 1.67 4277.87 ± 253.80 57.58 ± 1.79 88.54 ± 0.33 56.97 ± 4.62 577.43 ± 77.01 1997.31 ± 324.73 227.75 ± 14.88 756.53 ± 205.78 91.00 ± 0.09
VEN10 57.01 ± 0.86 3909.49 ± 255.19 57.15 ± 0.80 89.07 ± 0.17 54.64 ± 0.84 608.08 ± 16.61 1938.36 ± 36.70 226.49 ± 3.61 798.33 ± 19.07 91.35 ± 0.16

Data are presented as the mean (n = 6) ± standard deviation.

The data reported in Table 3, Table S1 and Table 4 were used for the assessment of volatile fingerprint and classification of CBSs from different cultivars and geographical origins using chemometrics reported by Barbosa-Pereira et al. (2019) [1].

2. Experimental design, materials and methods

2.1. Samples – CBS

Cocoa beans (n = 44) from different cultivars and countries across the world (Table 1) were purchased from several local chocolate enterprises. Cocoa bean shells were obtained from the cocoa beans after a standardized roasting process according to the procedure described by Barbosa-Pereira et al. (2019) [1] After separation from the respective cocoa beans, the CBS samples were ground into powders with a 250 μm mesh size and stored under a vacuum at −20 °C prior to further analysis. More detailed information related to the origin and description of samples was also reported by Barbosa-Pereira et al. (2019) (see Section 2.2. CBS Samples and Table 1 in Ref. [1]).

2.2. Physicochemical analysis

2.2.1. Moisture content

The moisture content of the CBS samples was determined by gravimetry, at 110 °C until constant weight, using a Gibertini Eurotherm electronic moisture balance (Gibertini Elettronica, Novate Milanese MI, Italy).

2.2.2. Determination of pH and titratable acidity

Titratable acidity (TA) and pH of the CBS were determined according to AOAC official method described by Nazaruddin, Seng, Hassan, & Said, 2006 [2]. Five grams of CBS powder were homogenised in 100 ml of boiled distilled water by stirring for 30 s and filtered through Whatman no. 4 filter under vacuum. An aliquot (25 mL) was used to measure pH using a pH meter Knick Portamess® 913 (Knick, Berlin, Germany). Then, the same aliquot was titrated with 0.1 mol L−1 NaOH standard solution to an endpoint pH of 8.2. All determinations were performed in triplicate. The results of titratable acidity (TA) were expressed as g of acetic acid equivalents/100 g of CBS.

2.2.3. Color analysis – CIELab

The color analysis of CBSs was performed in transmittance mode on a CM-5 spectrophotometer (Konica Minolta, Tokyo, Japan) as reported by Rojo-Poveda et al. (2019) [3]. L*, a*, and b* CIELab parameters were used to measure the color, where L* is a coefficient of lightness ranging from 0 (black) to 100 (white), a* indicates the colors red-purple (when positive a*) and bluish-green (when negative a*), and b* denotes the colors yellow (when positive b*) and blue (negative b*).

2.3. HS-SPME/GC-qMS analysis

The VOCs from the CBS samples were analysed using a headspace solid phase micro extraction (HS-SPME) coupled with gas chromatography/quadrupole mass spectrometry (GC-qMS) as described by Barbosa-Pereira el al. (2019) [1].

2.3.1. HS-SPME conditions

The extraction of VOCs was performed in a COMBI PAL System Autosampler for SPME (CTC Analytics AG, Zwingen, Switzerland) equipped with an HS-SPME unit. CBS powder (0.1 g) was placed in a 20 mL headspace vial in contact with 2 mL of sodium chloride (40% w/v) and 10 μL of internal standard (IS) 5-nonanol (10 μg mL−1) and equilibrated at 60 °C with stirring at 250 rpm for 10 min. After reach the equilibrium, a SPME fibre coated with divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) (df 50/30 μm, 1 cm) (Supelco, Bellefonte, PA, USA) was exposed to the headspace of the sample for another 30 min with continuous heating and agitation. After extraction, the fibre was desorbed at 260 °C for 2 min in the injection port of the GC system in splitless mode.

2.3.2. GC-qMS instrument and analytical conditions

GC-qMS analyses were performed on a Shimadzu GC-2010 gas chromatograph equipped with a Shimadzu QP-2010 Plus quadrupole mass spectrometer (Shimadzu Corporation, Kyoto, Japan). A 30 m × 0.25 mm, 0.25 μm thickness DB-WAXETR capillary column (J&W Scientific Inc., Folsom, CA, USA) was used to separate the VOCs using helium as carrier gas at 1 ml min−1flow rate. The oven time-temperature programme was as follows: initial temperature 40 °C held for 5 min, from 40 °C to 180 °C at the rate of 5 °C min−1, and then to 240 °C at the rate of 10 °C min−1, which was held for 5 min. The MS transfer line was set at 240 °C. The MS fragmentation was performed by electron impact ionization mode (70 eV), and the temperature of the ion source and quadrupole was 240 °C. The data were recorded in full-scan mode in the mass acquisition range of 30–450 m/z with 0.30 s scan time. Data were acquired and analysed by using GC-qMS Solution Workstation software (version 4.3) (GC-qMS Solution, Shimadzu Corporation, Kyoto, Japan).

2.3.3. Qualitative and quantitative analysis

The identification of the volatile organic compounds, focused on 101 molecules, was performed by comparing the EI-MS fragmentation pattern of each compound with those available on the National Institute of Standards and Technology (NIST05) mass-spectral library and on our home-based library as reported by Barbosa-Pereira et al. (2019) (see section 2.3.2 and Table 2 in Ref [1]). The semi-quantitative concentrations of the VOCs identified were calculated as the area of the volatile marker component divided by the response factor of the ISTD 5-nonanol and expressed as micrograms of 5-nonanol equivalents per kg of sample (μg 5-nonanol Eq. kg−1 of CBS). CBS sample were analysed in triplicate and the data of the sum of each class of compound are shown in Table 3, while the data for a single molecule are detailed in Table S1 (Microsoft Excel Worksheet in supplementary material).

2.4. E-nose analysis

E-nose data were recorded using a portable electronic nose system PEN3 (Airsense Analytics GmbH., Germany). The system consists of a sampling unit and the gas detection system composed of 10 Metal Oxide Semiconductor (MOS) sensors, which are differentially sensitive to each characteristic volatile compound or group of compounds [4]. The analyses were performed as described by Barbosa-Pereira et al. (2019) [1]. Briefly, CBS powders (2 g) were placed in a 20-mL glass vial and incubated at 30 °C for 30 min to reach the headspace equilibrium. After, the gas headspace was injected into the E-nose for 90 s at a constant flow rate of 400 mL min−1. The sensor signals were recorded at each second by the pattern recognition software (WinMuster, v.1.6, Airsense Analytics GmbH., Germany). Each CBS sample was independently analysed in triplicates and the average of sensor responses (area under the curve) is shown in Table 4.

Funding

The present work was supported by COVALFOOD “Valorisation of high added-value compounds from cocoa industry by-products as food ingredients and additives”. This project has received funding from the European Union's Seventh Framework programme for research and innovation under the Marie Skłodowska-Curie grant agreement No 609402 - 2020 researchers: Train to Move (T2M).

Authors contributions

Conceptualization, L.B.P, G.Z; Validation, M.G, G.Z; Investigation, O.R.P, L.B.P, I.F; Writing-original Draft Preparation, L.B.P; Review and Editing, O.R.P, I.F, M.G, G.Z; Supervision, L.B.P., M.G, G.Z; Project Administration, L.B.P, G.Z.

Acknowledgements

L. Barbosa-Pereira gratefully acknowledges the European Union's Seventh Framework programme for her Marie Skłodowska-Curie grant. O. Rojo-Poveda is grateful to the University of Turin for her predoctoral fellowship. The authors are grateful to Silvio Bessone S.r.l., ICAM S.p.A., Ferrero International S.A., Guido Gobino S.r.l., Pastiglie Leone S.r.l., and Venchi S.p.A. for supplying the cocoa bean samples.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.dib.2019.104268.

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

mmc1.xlsx (297.9KB, xlsx)

References

  • 1.Barbosa-Pereira L., Rojo-Poveda O., Ferrocino I., Giordano M., Zeppa G. Assessment of volatile fingerprint by HS-SPME/GC-qMS and E-nose for the classification of cocoa bean shells using chemometrics. Food Res. Int. 2019;123:684–696. doi: 10.1016/j.foodres.2019.05.041. [DOI] [PubMed] [Google Scholar]
  • 2.Nazaruddin R., Seng L.K., Hassan O., Said M. Effect of pulp preconditioning on the content of polyphenols in cocoa beans (Theobroma cacao) during fermentation. Ind. Crops Prod. 2006;24(1):87–94. [Google Scholar]
  • 3.Rojo-Poveda O., Barbosa-Pereira L., Mateus-Reguengo L., Bertolino M., Stévigny C., Zeppa G. Effects of particle size and extraction methods on cocoa bean shell functional beverage. Nutrients. 2019;11(4):867. doi: 10.3390/nu11040867. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Benedetti S., Buratti S., Spinardi A., Mannino S., Mignani I. Electronic nose as a non-destructive tool to characterise peach cultivars and to monitor their ripening stage during shelf-life. Postharvest Biol. Technol. 2008;47(2):181–188. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

mmc1.xlsx (297.9KB, xlsx)

Articles from Data in Brief are provided here courtesy of Elsevier

RESOURCES