Shahin Ahmadi*a,
Shahram Lotfi
b,
Hamideh Hamzehalic and
Parvin Kumar
d
aDepartment of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran. E-mail: ahmadi.chemometrics@gmail.com
bDepartment of Chemistry, Payame Noor University (PNU), 19395-4697 Tehran, Iran
cDepartment of Chemistry, Islamic Azad University, East Tehran Branch, Tehran, Iran
dDepartment of Chemistry, Kurukshetra University, Kurukshetra, Haryana 136119, India
First published on 19th January 2024
Worldwide, various types of pepper are used in food as an additive due to their unique pungency, aroma, taste, and color. This spice is valued for its pungency contributed by the alkaloid piperine and aroma attributed to volatile essential oils. The essential oils are composed of volatile organic compounds (VOCs) in different concentrations and ratios. In chromatography, the identification of compounds is done by comparing obtained peaks with a reference standard. However, there are cases where reference standards are either unavailable or the chemical information of VOCs is not documented in reference libraries. To overcome these limitations, theoretical methodologies are applied to estimate the retention indices (RIs) of new VOCs. The aim of the present work is to develop a reliable QSPR model for the RIs of 273 identified VOCs of different types of pepper. Experimental retention indices were measured using comprehensive two-dimensional gas chromatography coupled to quadrupole mass spectrometry (GC × GC/qMS) using a coupled BPX5 and BP20 column system. The inbuilt Monte Carlo algorithm of CORAL software is used to generate QSPR models using the hybrid optimal descriptor extracted from a combination of SMILES and HFG (hydrogen-filled graph). The whole dataset of 273 VOCs is used to make ten splits, each of which is further divided into four sets: active training, passive training, calibration, and validation. The balance of correlation method with four target functions i.e. TF0 (WIIC = WCII = 0), TF1 (WIIC = 0.5 & WCII = 0), TF2 (WIIC = 0 & WCII = 0.3) and TF3 (WIIC = 0.5 & WCII = 0.3) is used. The results of the statistical parameters of each target function are compared with each other. The simultaneous application of the index of ideality of correlation (IIC) and correlation intensity index (CII) improves the predictive potential of the model. The best model is judged on the basis of the numerical value of R2 of the validation set. The statistical result of the best model for the validation set of split 6 computed with TF3 (WIIC = 0.5 & WCII = 0.3) is R2 = 0.9308, CCC = 0.9588, IIC = 0.7704, CII = 0.9549, Q2 = 0.9281 and RMSE = 0.544. The promoters of increase/decrease for RI are also extracted using the best model (split 6). Moreover, the proposed model was used for an external validation set.
Numerous reports have been published for the identification and characterization of diverse VOCs of peppers.10–13 Gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS) techniques are generally employed for the quantitative determination of volatile compounds in peppers.14,15 Other techniques such as proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS), two-dimensional gas chromatography with flame ionization detection (GC × GC-FID), quadrupole mass spectrometry (GC × GC-qMS) and time-of-flight mass spectrometry (GC × GC/TOFMS) are also applied to identify VOCs.5,10,16
In chromatography, the chemical structure of compounds is identified by comparing obtained peaks with a reference standard. However, in some cases, reference standards may be unavailable or the chemical information about VOCs may not be registered in reference libraries. To reduce these limitations, theoretical techniques for estimating the retention index (RI) of new VOCs are employed. Hence, the quantitative structure–property/activity relationship (QSPR/QSAR) is employed to predict the retention index (RI).17 QSPR/QSAR is a significant theoretical technique used to establish mathematical models that predict the properties/activities or endpoints of compounds, which have been newly designed or are undeveloped.18,19
A literature survey revealed that CORAL (CORrelation And Logic software available at http://www.insilico.eu/coral) software can be implemented for the development of predictive QSPR/QSAR models. CORAL is freeware software designed to calculate one-variable QSPR/QSAR models between an endpoint and descriptors using the Monte Carlo algorithm. In this software, the optimal descriptor of correlation weight (DCW) is calculated using the SMILES (Simplified Molecular-Input Line-Entry System) notation of the molecular structure.20–22 According to a literature report, the index of ideality of correlation (IIC) and correlation intensity index (CII) are applied as new criteria for judging the predictive potential of the QSPR model. It is often mentioned in the literature that the numerical value of the coefficient of determination (R2) for the validation and calibration set is improved by the IIC, whereas the CII improves the numerical value of the coefficient of determination (R2) for all four sets: i.e. active training, passive training, calibration and validation.23–31
The objective of this study is to construct a predictive QSPR model using the Monte Carlo technique of the CORAL software for the retention index property of 273 VOCs recognized in peppers. Ten random splits are made and each split is divided into four subsets. The IIC and CII statistical parameters are employed to predict a better model. The balance of correlation method with four target functions, i.e. TF0 (WIIC = WCII = 0), TF1 (WIIC = 0.5 & WCII = 0), TF2 (WIIC = 0 & WCII = 0.3) and TF3 (WIIC = 0.5 & WCII = 0.3), is used to examine the robustness and accuracy of the constructed QSPR model.
(1) Data preparation involves converting the structure to SMILES and preparing the Total set file.
(2) The process of data splitting in CORAL software is carried out using random splitting. This can be done using the classical scheme or balance of correlation. In the balance of correlation the data is divided into four sets: active training, passive training, calibration, and validation sets. However, if the amount of data is small, the classical scheme is used and the data set includes training, calibration, and validation sets.
(3) The selection of descriptors is based on either SMILES or a graph, or a combination of both. The descriptors are chosen accordingly.
(4) The target function selection involves computing correlation weights using the Monte Carlo method and maximizing one of the target functions: namely TF0, TF1, TF2, or TF3. The formulas of these target functions are described in the corresponding section.
(5) Model building consists of two phases. In Phase 1, the preferable threshold and number of epochs are searched for using Monte Carlo optimization based on statistical results from the calibration set. In Phase II, the preferable model is constructed after optimization of the threshold and number of epochs.
(6) External validation is performed on the test sets after model building.
(7) Model interpretation is carried out in this step.
(8) New molecules can be designed based on the model interpretation.
The hybrid optimal descriptor of the correlation weights (DCW) is computed using the following equation:
DHybridCW(T*,N*) = DCWSMILES(T*,N*) + DCWHFG(T*,N*) | (1) |
The DCW of HFG and SMILES are calculated via mathematical eqn (2) and (3).
DCWSMILES(T*,N*) = ∑CW(Sk) + ∑CW(SSk) + ∑CW(SSSk) + CW(BOND) + CW(MFCs) | (2) |
DCWHFG(T*,N*) = ∑CW(EC0k) + ∑CW(EC1k) + ∑CW(pt2k) + ∑CW(VS2k) + ∑CW(nnk) + ∑CW(C5) + ∑(C6) | (3) |
In eqn (2), the structural attributes Sk, SSk, and SSSk are single SMILES symbols (e.g., Cl or S), two SMILES symbols and a combination of three SMILES symbols, respectively. The BOND code demonstrates the existence or absence of double (=), triple (#), or stereochemical bonds (@ or @@). Here, the molecular feature contributions (MFCs) are the total number of oxygen atoms (O), the number of double bonds (=), and the number of triple bonds (#). Therefore, in eqn (3), the attributes EC0 and EC1 are the number of neighbors of a vertex degree and Morgan's connectivity of first order; pt2k is the number of paths of length 2; VS2 is the valence shells of radius 2 in the HFG; the nn symbol implies nearest neighbors; C5 and C6 are descriptors that represent the five- and six-membered rings in the molecular structure, respectively. T is the threshold to separate SMILES attributes into noise or active. The active SMILES are applied to construct the model. The noise SMILES are not involved in constructing the model. T* and N* are the optimum threshold and number of epochs of the Monte Carlo optimization method. T* and N* provide the maximum statistical quality for the calibration set. The numerical values for CWs are acquired from the Monte Carlo optimization and the optimal descriptor is computed with the optimal CWs. Then the calculated CWs are employed to design a predictive model of the RI according to the following equation:
RI = C0 + C1 × DCW(T*,N*) | (4) |
The mathematical equations for each target function can be demonstrated follows:
TF0 = RATRN + RPTRN − |RATRN − RPTRN| × drweight | (5) |
TF1 = TF0 + IICCAL × weight for IIC (IICweight) | (6) |
TF2 = TF0 + CIICAL × weight for CII (CIIweight) | (7) |
TF3 = TF0 + IICCAL × IICweight + CIICAL × CIIweight | (8) |
![]() | (9) |
RCAL is the correlation coefficient between experimental values and calculated values of RI for the calibration set. The negative and positive mean absolute errors are indicated by −MAE and +MAE, which are computed as follows:
![]() | (10) |
![]() | (11) |
Δk = observedk − calculatedk | (12) |
The ‘k’ is the index (1, 2, …, N) and the observedk and calculatedk are related to the endpoint.
![]() | (13) |
R2 is the correlation coefficient for a set that contains n substances. Rk2 is the correlation coefficient for n − 1 substances of a set after removing the kth substance. Hence, if (Rk2 − R2) is greater than zero, the kth substance is an “oppositionist” for the correlation between experimental and predicted values of the set. A small sum of “protests” means a more “intensive” correlation.
![]() | (14) |
PATRN(Ak), PPTRN(Ak) and PCAL(Ak) are the probability of attributes in the active training set, passive training set, and calibration set, respectively; NATRN(Ak), NPTRN(Ak), and NCAL(Ak) are frequencies of attributes in the active training, passive training and calibration sets, respectively.
The SMILES-statistical defect (D) can be calculated as the sum of statistical defects of all attributes:
![]() | (15) |
NA is the number of active SMILES attributes for the given compounds.
In CORAL, a SMILES is an outlier if:
![]() | (16) |
D is an average of statistical defects for the dataset of the active training set.
![]() | ||
Fig. 1 Comparison of determination coefficients computed with TF0, TF1, TF2 and TF3 of all ten splits. |
The QSPR models for the ten splits formulated with TF3 for prediction of the RI of the VOCs are given below:
Split 1
RI = 73.8025(±7.0844) + 24.5924(±0.1499) × DCW(1,15) | (17) |
Split 2
RI = 121.5622(±6.8196) + 26.4093(±0.1507) × DCW(1,15) | (18) |
Split 3
RI = 315.0484(±6.5146) + 20.0374(±0.1270) × DCW(1,15) | (19) |
Split 4
RI = 133.8434(±9.0780) + 16.6644(±0.1272) × DCW(1,15) | (20) |
Split 5
RI = 120.8001(±5.6911) + 22.7349(±0.1122) × DCW(1,15) | (21) |
Split 6
RI = 265.7739(±6.3219) + 23.9520(±0.1477) × DCW(1,15) | (22) |
Split 7
RI = 107.8894(±7.4311) + 23.2382(±0.1417) × DCW(1,15) | (23) |
Split 8
RI = 40.7742(±6.0698) + 25.9964(±0.1255) × DCW(1,20) | (24) |
Split 9
RI = 25.2594(±6.5441) + 25.5860(±0.1587) × DCW(1,15) | (25) |
Split 10
RI = 110.5639(±6.0444) + 21.8635(±0.1077) × DCW(1,15) | (26) |
![]() | ||
Fig. 2 Experimental versus predicted retention indices of split 1 to 10 for VOCs detected in peppers by the Monte Carlo method based on target function TF3. |
The promoters for endpoint RI increase or decrease were computed from the best model (split 6) and are displayed in Table 1. Morgan extended connectivity of zero-order for hydrogen atom as 1 (ec0-h…1…), Morgan extended connectivity of first-order for hydrogen atom as 4 (ec1-h…4…), Morgan extended connectivity of first-order for carbon atom as 7 (ec1-c…7…), Morgan extended connectivity of zero-order for carbon atom as 3 (ec0-c…3…), Morgan extended connectivity of first-order for carbon atom as 4 (ec1-c…10…), the number of paths of length 2 which started from a hydrogen atom is equal to 3 (pt2-h…3…), the number of paths of length 2 which started from a carbon atom is equal to 5 (pt2-c…5…), the number of paths of length 2 which started from a hydrogen atom is equal to 2 (pt2-h…2…), two sp3 hybridized carbon joined by branching (c…(…c…), the presence of two consecutive aliphatic carbons (c…c…) etc. were some significant promoters of endpoint increase. The nearest neighbours code for carbon equal to 413 (nnc-c…413), the nearest neighbours code for carbon equal to 440 (nnc-c…440), a combination of the carbon atom, oxygen and branching (c…o…(…), and 28 as a sum of vertex degrees which take place at a topological distance of 2 relatively to carbon vertex (vs. 2-c…28) etc. were some significant promoters of endpoint decrease.
No. | Structural attributes | CWs Probe 1 | CWs Probe 2 | CWs Probe 3 | NSs | NSc | NSv | Defect [SAk] | Description |
---|---|---|---|---|---|---|---|---|---|
The promoters of RI increase | |||||||||
1 | EC0-H…1… | 0.0509 | 0.16064 | 0.34295 | 68 | 58 | 54 | 0 | Morgan extended connectivity of zero-order for hydrogen atom as 1 |
2 | EC1-H…4… | 0.18025 | 0.22099 | 0.18401 | 68 | 57 | 54 | 0 | Morgan extended connectivity of first-order for hydrogen atom as 4 |
3 | PT2-H…3… | 0.16527 | 0.2584 | 0.3257 | 68 | 57 | 54 | 0 | The number of paths of length 2 which started from a hydrogen atom is equal to 3 |
4 | C⋯C…… | 0.44726 | 0.01345 | 0.30905 | 65 | 55 | 50 | 0.0003 | The presence of two consecutive aliphatic carbons |
5 | EC1-C…7… | 0.31326 | 0.46929 | 0.21017 | 63 | 56 | 50 | 0 | Morgan extended connectivity of first-order for carbon atom as 7 |
6 | VS2–H…6… | 0.10836 | 0.05545 | 0.26739 | 63 | 56 | 50 | 0 | 6 as a sum of vertex degrees which take place at a topological distance of 2 relatively to hydrogen vertex |
7 | EC0-C…3… | 0.56731 | 0.20537 | 0.33853 | 62 | 47 | 46 | 0.0006 | Morgan extended connectivity of zero-order for carbon atom as 3 |
8 | EC1-C…10 | 0.22378 | 0.25831 | 0.04221 | 61 | 52 | 47 | 0.0002 | Morgan extended connectivity of first-order for carbon atom as 4 |
9 | NNC-C…422 | 0.20503 | 0.01758 | 0.05181 | 60 | 53 | 48 | 0.0001 | The nearest neighbours codes for carbon equal to 422 |
10 | C…(…C… | 0.29947 | 0.13954 | 0.33177 | 59 | 49 | 49 | 0.0004 | Two sp3 hybridized carbon joined by branching |
11 | =…… | 0.15273 | 0.45098 | 0.39027 | 56 | 43 | 44 | 0.0001 | Presence of double covalent bond |
12 | 1…… | 0.26393 | 0.42999 | 0.64276 | 53 | 46 | 42 | 0 | Presence of at least one ring |
13 | NNC-C…321 | 0.44352 | 0.72343 | 0.10398 | 53 | 39 | 38 | 0.0008 | The nearest neighbours codes for carbon equal to 321 |
14 | PT2-H…2… | 0.06983 | 0.57052 | 0.40344 | 53 | 42 | 38 | 0.0008 | The number of paths of length 2 which started from a hydrogen atom is equal to 2 |
15 | PT2-C…5… | 0.21029 | 0.22211 | 0.19676 | 52 | 42 | 39 | 0.0005 | The number of paths of length 2 which started from a carbon atom is equal to 5 |
![]() |
|||||||||
The promoters of RI decrease | |||||||||
1 | NNC-C…413 | −0.08801 | −0.07644 | −0.57505 | 67 | 57 | 52 | 0.0002 | The nearest neighbours codes for carbon equal to 413 |
2 | C…1…(… | −0.30101 | −0.02505 | −0.28091 | 26 | 27 | 20 | 0.0003 | Combination of aliphatic carbon, one ring and branching |
3 | NNC-C…440 | −0.3105 | −0.03567 | −0.80092 | 26 | 27 | 20 | 0.0003 | The nearest neighbours codes for carbon equal to 440 |
4 | C⋯O…(… | −0.39833 | −1.30355 | −0.7525 | 8 | 1 | 4 | 0.0036 | Combination of the carbon atom, oxygen and branching |
5 | O…(…(… | −0.15072 | −1.42791 | −2.29626 | 5 | 1 | 5 | 0.0019 | Oxygen atom with two branching |
6 | VS2–C…28 | −0.05713 | −0.5657 | −0.85747 | 5 | 8 | 4 | 0.0001 | 28 as a sum of vertex degrees which take place at a topological distance of 2 relatively to carbon vertex |
7 | 3⋯C…1… | −1.16603 | −0.21466 | −2.69557 | 1 | 6 | 1 | 0.0019 |
Table 2 displays a comparison of the statistical results of the present QSPR model with the reported QSPR model. The previously reported model was implemented with only one split, but in the present QSPR models, 10 splits were used to design 40 QSRR models employing four target functions (TF0, TF1, TF2 and TF3). Two significant criteria, the index of ideality correlation (IIC) and correlation intensity index (CII), are also addressed in this work, which were not studied in earlier work. In the present QSPR models, only one descriptor, DCW, was used to construct the QSPR models but in the previously reported model, four descriptors were applied. The numerical value of the determination coefficient (Rval2) of the QSPR model generated with TF3 for split 6 is 0.9308, which is much better than the value for the reported model. Thus, the presented QSPR models are more robust and predictable.
No. | Set | n | Descriptor generator | Regression method | R2 train | RMSD | IIC | CII | Ref. |
---|---|---|---|---|---|---|---|---|---|
1 | Training | 92 | Dragon and PaDEL | MLR | 0.879 | 72.1 | — | — | 32 |
Validation | 91 | 0.832 | 91.7 | — | — | ||||
Test | 90 | 0.915 | 55.4 | — | — | ||||
2 | ActivTRN | 68 | CORAL package | LR | 0.885 | 62.7 | 0.658 | 0.928 | Present work |
PassTRN | 58 | 0.900 | 70.8 | 0.677 | 0.943 | ||||
Calib | 54 | 0.904 | 51.6 | 0.951 | 0.944 | ||||
Valid | 90 | 0.931 | 54.4 | 0.770 | 0.955 |
No. | Name | CAS number | RIpredicted (this study) | RIpredicted (Rojas et al.) | Experimental RIliterature | Ref. | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
DB-5 column | HP-5MS stationary phase | HP-5 column | DB-Wax stationary phase | BPX5 column | RTX-Wax stationary phase | HP-20M column | DB-5MS stationary phase | BP20 column | ||||||
a Not available. | ||||||||||||||
1 | Myrcenol | 543-39-5 | 1164.719 | 1159.6 | NA | NAa | NA | NA | NA | NA | NA | NA | NA | 41 |
2 | (E,E)-α-Farnesene | 502-61-4 | 1483.631 | 1496.2 | 1508 | NA | NA | NA | NA | NA | NA | NA | NA | 15, 42 and 43 |
α-Farnesene | ─ | ─ | NA | NA | NA | NA | NA | NA | NA | NA | 41 and 44 | |||
3 | Elemicin | 487-11-6 | 1501.785 | 1565 | 1540 | NA | NA | NA | NA | NA | NA | NA | NA | 43 and 45 |
1554 | ||||||||||||||
1556 | ||||||||||||||
4 | Myristicin | 607-91-0 | 1391.435 | 1546.5 | 1520 | NA | NA | NA | NA | NA | NA | NA | NA | 43 |
5 | Apiole | 523-80-8 | 1541.468 | 1721.7 | 1679 | NA | NA | NA | NA | NA | NA | NA | NA | 43 |
1680 | ||||||||||||||
1685 | ||||||||||||||
6 | Dillapiole | 484-31-1 | 1565.236 | 1709.1 | 1622 | 1622 | NA | NA | NA | NA | NA | NA | NA | 14,43 |
1644 | ||||||||||||||
7 | Eugenol acetate | 93-28-7 | 1568.772 | 1577.7 | 1524 | NA | NA | NA | NA | NA | NA | NA | NA | 45 |
8 | Carvone oxide | 18![]() |
1239.442 | 1367.3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 41, 42 and 46–49 |
trans-Carvone oxide | 33![]() |
─ | ─ | NA | NA | NA | NA | NA | NA | NA | NA | NA | 41 | |
9 | α-Bulnesene | 3691-11-0 | 1484.066 | 1508.7 | 1493 | 1505 | NA | NA | NA | NA | NA | NA | NA | 41 |
1503 | ||||||||||||||
1505 | ||||||||||||||
δ-Guaiene | ─ | ─ | NA | NA | NA | NA | NA | NA | NA | NA | NA | 42 and 43 | ||
10 | Dihydrocarveol | 619-01-2 | 1164.505 | 1184.8 | 1192 | NA | 1195 | 1941 | NA | NA | NA | NA | NA | 41, 42, 44 and 46–49 |
1195 | ||||||||||||||
p-Menth-8-en-2-ol | ─ | ─ | NA | NA | NA | NA | NA | NA | NA | NA | NA | 42 | ||
trans-p-Menth-8-en-2-ol | ─ | ─ | NA | NA | NA | NA | NA | NA | NA | NA | NA | 47–49 | ||
Neo-dihydrocarveol | 18![]() |
─ | ─ | NA | NA | NA | NA | NA | NA | NA | NA | NA | 41 | |
11 | cis-β-Terpineol | 138-87-4 | 1196.492 | 1181 | 1159 | NA | NA | NA | NA | NA | NA | NA | NA | 41 |
12 | β-Bisabolol | 15![]() |
1647.933 | 1604.1 | NA | NA | 1668 | NA | NA | NA | 2021 | NA | NA | 41 and 42 |
13 | α-Bisabolol | 515-69-5 | 1644.237 | 1594.9 | 1683 | NA | NA | NA | NA | NA | NA | NA | NA | 14 and 44 |
1701 | ||||||||||||||
14 | Squalene | 111-02-4 | 2742.641 | 2726 | 2790 | NA | NA | NA | NA | NA | NA | NA | NA | 6 |
15 | δ-Terpinyl acetate | 93![]() |
1382.135 | 1356 | 1313 | NA | NA | NA | NA | NA | NA | NA | NA | 41 |
16 | 1,4-Cineole | 470-67-7 | 1139.433 | 1117.1 | 1016 | 1016 | NA | 1171 | NA | NA | NA | NA | NA | 41 |
17 | Cadina,1,4-dien-3-ol | 1608.026 | 1636.1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 41 | |
18 | trans-Piperitone oxide | 4713-38-6 | 1194.878 | 1303.8 | 1258 | NA | NA | NA | NA | NA | NA | NA | NA | 50 |
19 | 3-Buten-2-ol | 598-32-3 | 642.9435 | 758.6 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 41 |
20 | Vomifoliol | 23![]() |
1573.99 | 1735.6 | NA | 1796 | NA | 3167 | 1814 | NA | NA | NA | NA | 41 |
3175 | ||||||||||||||
21 | Neryl isovalerate | 3915-83-1 | 1578.39 | 1593 | NA | NA | NA | NA | NA | NA | NA | NA | 1872 | 43 |
22 | Retrofractamide B | 54![]() |
2393.783 | 2624 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 14 |
23 | Guineensine | 55![]() |
2568.907 | 2783.1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 14 |
24 | Caryophyllene alcohol | 472-97-9 | 1586.876 | 1548.9 | 1568 | 1560 1564 | NA | NA | NA | NA | NA | NA | NA | 42 |
Caryophyllenol | ─ | ─ | NA | NA | NA | NA | NA | NA | NA | NA | NA | 41, 44 and 46–49 | ||
25 | Clovene | 469-92-1 | 1440.777 | 1460 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 42 |
26 | Piperitol | 491-04-3 | 1170.929 | 1143.6 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 43 |
27 | Humulene oxide II | 19![]() |
1609.486 | 1589.6 | 1606 | NA | NA | NA | NA | NA | NA | NA | NA | 41 |
1607 | ||||||||||||||
28 | α-Cedrene | 469-61-4 | 1447.516 | 1451.9 | 1409 | 1409 | NA | NA | NA | 1562 | NA | NA | NA | 43 |
1410 | ||||||||||||||
29 | Hedycaryol | 21![]() |
1627.43 | 1612.5 | 1530 | NA | NA | NA | NA | NA | NA | NA | NA | 44 |
30 | Germacrene D-4-ol | 198![]() |
1594.602 | 1606.5 | 1511 | 1567 | NA | NA | NA | NA | NA | NA | NA | 43 |
1574 | ||||||||||||||
31 | α-Eudesmol | 473-16-5 | 1592.197 | 1595.8 | 1652 | NA | NA | NA | NA | NA | 2230 | NA | NA | 41 and 43 |
32 | Furanodiene | 19![]() |
1586.736 | 1626.1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 43 |
33 | cis-p-Menth-8-en-2-ol | 1180.423 | 1193.1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 47–49 | |
34 | Isopulegol | 89-79-2 | 1200.764 | 1141.3 | 1145 | 1146 | NA | 1879 | NA | NA | NA | NA | NA | 43 |
1879 | ||||||||||||||
35 | Menthol | 89-78-1 | 1190.85 | 1113.6 | 1173 | NA | NA | 1626 | NA | NA | 1646 | NA | NA | 41 |
2103 | ||||||||||||||
36 | cis-Sabinene hydrate | 15![]() |
1144.655 | 975 | 1069 | 1101 | NA | 1465 | NA | NA | NA | NA | NA | 14 and 43 |
1070 | ||||||||||||||
1097 | ||||||||||||||
37 | Cedrol | 77-53-2 | 1583.856 | 1544.5 | 1596 | NA | NA | NA | NA | NA | NA | NA | NA | 15, 41, 42 and 46–49 |
1604 | ||||||||||||||
38 | (Z)-Isosafrole | 17![]() |
1271.586 | 1391.8 | 1308 | NA | NA | NA | NA | NA | NA | NA | NA | 41 |
1336 | ||||||||||||||
39 | Nona-trans,cis-2,6-dienal | 557-48-2 | 1102.993 | 1130.7 | NA | NA | NA | 1597 1605 | NA | NA | NA | NA | NA | 51 |
40 | Howeveranediol | 25![]() |
761.5132 | 733.4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 6 |
41 | Ethyl-2-hexenol | 50![]() |
993.1246 | 1003.4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 41 |
42 | Hept-trans-3-en-2-one | 1119-44-4 | 940.3034 | 900.1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 51 |
43 | Nona-trans,trans-2,5- dien-4-one | 61![]() |
1101.811 | 1085.6 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 51 |
44 | Hex-trans-2-enal | 6728-26-3 | 838.5927 | 850.3 | 854 | NA | 857 | 1201 | NA | NA | 1209 | 848 | NA | 51 |
(E)-2-Hexenal | ─ | ─ | NA | NA | NA | NA | NA | NA | NA | NA | NA | 41 | ||
45 | 1-Hepten-3-ol | 4938-52-7 | 902.912 | 925.4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 41 |
46 | (E)-3-Octenol | 18![]() |
1063.265 | 1043.7 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 41 |
47 | 2-Heptanone | 110-43-0 | 900.1747 | 971.3 | 888 | NA | 882 | 1160 | 889 | NA | NA | NA | NA | 51 |
889 | ||||||||||||||
48 | Non-trans-2-en-4-one | 32![]() |
1115.155 | 1156.7 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 51 |
49 | Non-1-en-4-one | 61![]() |
1084.331 | 1154.4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 51 |
50 | α-Ethyl hexanoate | 123-66-0 | 1021.078 | 1118 | 996 | 1001 | 997 | 1224 | NA | NA | NA | NA | NA | 6 |
997 | 1229 | |||||||||||||
998 | 1244 | |||||||||||||
1001 | 1270 | |||||||||||||
51 | 1-Octen-3-ol | 3391-86-4 | 990.474 | 1013.3 | 942 | 975 | 977 | 980 | NA | NA | 1423 | NA | NA | 41 |
978 | 991 | 1438 | ||||||||||||
980 | 1465 | |||||||||||||
52 | 2,4-Decadienoic acid | 42![]() |
1235.616 | 1736.5 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 14 |
Piperidide | NA | NA | NA | NA | NA | NA | NA | NA | NA | |||||
53 | Pellitorin | 18![]() |
1519.16 | 1624.7 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 14 |
54 | Deca-trans,cis-2,4-dienal | 25![]() |
1189.996 | 1212.5 | 1291 | NA | NA | 1758 | NA | NA | NA | NA | NA | 51 |
1297 | ||||||||||||||
Deca-trans,trans-2,4-dienal | 2363-88-4 | ─ | ─ | NA | NA | NA | NA | NA | NA | NA | NA | NA | 51 | |
Deca-2,4-dienal | 25![]() |
─ | ─ | 1311 | NA | NA | 1820 1832 | NA | NA | NA | NA | NA | 51 | |
1314 | ||||||||||||||
1319 | ||||||||||||||
55 | (E)-2-Octenal | 2363-89-5 | 1010.999 | 1023.6 | NA | 1060 | NA | NA | NA | NA | NA | NA | NA | 41 |
56 | N-Isobutyl-(2E,4E,12E)-octadecatrienamide | 943![]() |
2222.115 | 2290 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 14 |
57 | N-Isobutyl-(2E,4E,14Z)-eicosatrienamide | — | 2397.239 | 2448.7 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 14 |
58 | Hexanal | 66-25-1 | 836.3585 | 921.5 | 784 | 805 | 800 | 1067 | NA | NA | 1075 | NA | NA | 16 and 51 |
797 | 803 | 1093 | ||||||||||||
799 | ||||||||||||||
800 | ||||||||||||||
819 | ||||||||||||||
59 | 2-Octanol | 25![]() |
1019.923 | 981.9 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 41 |
60 | Nonane | 111-84-2 | 964.4392 | 965.9 | 899 | 900 | NA | NA | NA | NA | 900 | NA | NA | 41 |
61 | (E)-2-Tridecenal | 7774-82-5 | 1448.809 | 1429.9 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 43 |
62 | Tetradecane | 629-59-4 | 1402.249 | 1372.2 | 1116 | NA | NA | 1399 | 1400 | NA | 1400 | NA | NA | 14 |
1399 | ||||||||||||||
63 | N-Isobutyl-(2E,4E)-octadecadienamide | 54![]() |
2219.656 | 2266 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 14 |
64 | Pentadecanal | 2765-11-9 | 1624.416 | 1660.3 | 1513 | 1711 | NA | NA | NA | NA | NA | NA | NA | 50 |
1687 | ||||||||||||||
1710 | ||||||||||||||
65 | Hexadecane | 544-76-3 | 1577.373 | 1531.4 | 1600 | NA | NA | NA | NA | NA | 1600 | NA | NA | 14 |
66 | Palmitic acid | 57-10-3 | 1755.653 | 1811.2 | 1984 | NA | NA | NA | NA | NA | 2860 | NA | NA | 14 |
Palmitic acid glyceride | ─ | ─ | NA | NA | NA | NA | NA | NA | NA | NA | NA | 6 | ||
67 | Heptadecane | 629-78-7 | 1664.935 | 1610.7 | 1700 | NA | NA | NA | NA | NA | 1700 | NA | NA | 14 |
68 | 1-Octadecene | 112-88-9 | 1759.766 | 1711.6 | 1793 | NA | NA | NA | 1774 | NA | NA | NA | NA | 14 |
1794 | ||||||||||||||
69 | Octadecane | 593-45-3 | 1752.497 | 1689.9 | 1800 | NA | NA | 1805 | NA | NA | 1800 | NA | NA | 14 |
70 | Nonadecane | 629-92-5 | 1840.059 | 1768.9 | 1900 | NA | NA | NA | NA | NA | 1900 | NA | NA | 14 |
71 | 1-Eicosene | 3452-07-1 | 1934.889 | 1869.5 | 1990 | NA | NA | NA | NA | NA | NA | NA | NA | 14 |
1994 | ||||||||||||||
72 | Eicosane | 112-95-8 | 1927.621 | 1847.8 | 2000 | NA | NA | NA | NA | NA | 2000 | NA | NA | 14 |
73 | Heneicosane | 629-94-7 | 2015.183 | 1926.7 | 2100 | NA | NA | NA | NA | NA | 2100 | NA | NA | 14 |
74 | Docosane | 629-97-0 | 2102.745 | 2005.4 | 2200 | NA | NA | NA | NA | NA | 2200 | NA | NA | 14 |
75 | Nonadecanol | 1454-84-8 | 2015.34 | 1901.6 | NA | 2156 | NA | NA | NA | NA | NA | NA | NA | 50 |
76 | Hexanol | 111-27-3 | 877.0338 | 846.4 | 867 | NA | 865 | 1351 | NA | NA | 1325 | 869 | NA | 41 |
884 | 1354 | |||||||||||||
1360 | ||||||||||||||
1379 | ||||||||||||||
1392 | ||||||||||||||
77 | Amyl alcohol | 71-41-0 | 789.4719 | 751.3 | 766 | NA | 766 | 1244 | NA | NA | NA | NA | NA | 41 |
768 | ||||||||||||||
78 | Benzenepropanoic acid, ethyl ester | 2021-28-5 | 1339.896 | 1411.3 | 1390 | NA | NA | 1897 | NA | NA | NA | NA | NA | 43 |
1905 | ||||||||||||||
79 | Methyl salicylate | 119-36-8 | 1160.501 | 1248.3 | 1190 | 1190 | NA | NA | NA | NA | NA | NA | NA | 51 |
1191 | ||||||||||||||
1206 | ||||||||||||||
80 | Guaiacol | 90-05-1 | 1001.29 | 1087.9 | 1086 | NA | NA | 1872 | NA | NA | NA | NA | NA | 41 |
1091 | 1875 | |||||||||||||
1883 | ||||||||||||||
81 | 2-Methoxy-3-isobutylpyrazine | 24![]() |
1055.625 | 1291 | 1135 | NA | NA | 1540 | NA | NA | NA | NA | NA | 51 |
1171 | ||||||||||||||
82 | Thymol | 89-83-8 | 1189.021 | 1203.6 | 1290 | 1290 | 1308 | NA | NA | NA | NA | NA | NA | 41 and 43 |
83 | 2-Methylnaphthalene | 91-57-6 | 1210.977 | 1379.3 | 1281 | NA | 1295 | NA | NA | NA | NA | NA | NA | 51 |
84 | 1-Methylnaphthalene | 90-12-0 | 1163.889 | 1355.6 | 1298 | NA | 1312 | NA | NA | NA | NA | NA | NA | 51 |
85 | Piperamide C 9:1 (8E) | 62![]() |
2232.683 | 2552.6 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 14 |
86 | 4,5-Dihydropiperettine | 583-34-6 | 2105.33 | 2419.8 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 14 |
87 | Dehydropipernonaline | 107![]() |
2279.967 | 2579.6 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 14 |
88 | Piperine | 94-62-2 | 1928.943 | 2235.5 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 14 |
89 | Piperanine | 23![]() |
1932.372 | 2211.5 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 14 |
90 | 1-Cinnamoyl piperidine | 5422-81-1 | 1597.08 | 1759.2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 14 |
91 | Piperolein B | 30![]() |
2301.005 | 2626.8 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 14 |
92 | Geranial | 5392-40-5 | 1182.447 | 1151.7 | 1240 | NA | NA | NA | NA | NA | NA | NA | NA | 41 |
Neral | 106-26-3 | ─ | ─ | 1235 | NA | NA | 1630 | NA | NA | 1658 | NA | NA | 41 | |
1240 | 1690 | |||||||||||||
1294 | 1695 | |||||||||||||
93 | Citronella | 106-23-0 | 1155.695 | 1216.9 | 1153 | NA | NA | 1425 | NA | NA | NA | NA | NA | 41, 42 and 46–49 |
1159 | 1485 | |||||||||||||
1161 | 1488 | |||||||||||||
94 | Phenylacetaldehyde | 122-78-1 | 1046.275 | 1112.2 | 1043 | NA | NA | 1609 | NA | NA | 1615 | NA | NA | 51 |
1049 | 1671 | |||||||||||||
95 | Furfural | 98-01-1 | 753.1899 | 858.1 | 830 | NA | NA | 1458 | 832 | NA | NA | NA | NA | 51 |
1474 | ||||||||||||||
1485 | ||||||||||||||
96 | Oxalic acid | 144-62-7 | 740.7731 | 852.3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 6 |
97 | α-Hydroxypropionic acid | 50-21-5 | 699.8166 | 862.4 | NA | NA | NA | NA | 1058 | NA | NA | NA | NA | 6 |
98 | Howeverenedioic acid | 110-16-7 | 888.9371 | 943.8 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 6 |
99 | Dihydrolimonen-10-al | 3269-90-7 | 1223.518 | 1236 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 41 |
100 | Gluconate anion | 608-59-3 | 1350.775 | 1570.7 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 6 |
101 | Erythritol | 149-32-6 | 943.4734 | 1041.8 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 6 |
102 | Glycerin | 56-81-5 | 758.5946 | 864.6 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 6 |
103 | Myrtenol | 515-00-4 | 1159.949 | 1118.5 | 1194 | NA | NA | NA | NA | NA | NA | NA | NA | 42, 44 and 47–49 |
1196 | ||||||||||||||
1202 | ||||||||||||||
1214 | ||||||||||||||
104 | cis-p-Menth-2-en-7-oI | 1185.016 | 1202.4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 41 | |
105 | Glucopyranose | 492-62-6 | 1317.003 | 1440.1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 6 |
106 | Geraniol | 106-24-1 | 1175.015 | 1171.8 | 1255 | NA | 1240 | 1788 | NA | NA | 1814 | NA | NA | 41 |
1276 | 1850 | |||||||||||||
1862 | ||||||||||||||
Nerol | 106-25-2 | ─ | ─ | 1228 | NA | NA | 1753 | NA | NA | 1770 | NA | NA | 41 and 50 | |
107 | (2E,6E)-Farnesol | 106-28-5 | 1606.869 | 1597.8 | 1706 | NA | NA | 2371 | NA | NA | NA | NA | NA | 43 |
1722 | ||||||||||||||
(E,E)-Farnesol | ─ | ─ | NA | NA | NA | NA | NA | NA | NA | NA | NA | 41 | ||
α-Farnesol | ─ | ─ | NA | NA | NA | NA | NA | NA | NA | NA | NA | 44 | ||
(Z,Z)-Farnesol | 4602-84-0 | ─ | ─ | 1689 1713 | 1713 | 1713 | NA | NA | NA | NA | NA | NA | 41 | |
1718 | ||||||||||||||
(2Z,6Z)-Farnesol | ─ | ─ | NA | NA | NA | NA | NA | NA | NA | NA | NA | 43 | ||
(2E,6Z)-Farnesol | 3879-60-5 | ─ | ─ | 1734 | 1742 | NA | NA | NA | NA | NA | NA | NA | 43 | |
1742 | 1743 | |||||||||||||
1748 | ||||||||||||||
(E,Z)-Farnesol | ─ | ─ | NA | NA | NA | NA | NA | NA | NA | NA | NA | 41 | ||
(Z,E)-Farnesol | 3790-71-4 | ─ | ─ | 1697 | NA | NA | NA | NA | NA | NA | NA | NA | 41 | |
108 | Phytol | 150-86-7 | 1941.848 | 1928.7 | 1949 | NA | NA | NA | NA | NA | 2571 | NA | NA | 43 |
109 | (E)-Cinnamyl alcohol | 4407-36-7 | 1155.272 | 1238.7 | 1305 | NA | NA | NA | NA | NA | NA | NA | NA | 41 |
110 | 2-Methyl cinnamyl alcohol | 1504-55-8 | 1212.211 | 1285.1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 41 |
111 | Isoamyl alcohol | 123-51-3 | 764.7509 | 679.6 | 734 | NA | 737 | 1169 | NA | NA | 1182 | NA | NA | 41 |
735 | 1206 | |||||||||||||
1230 | ||||||||||||||
112 | Hex-cis-3-enol | 928-96-1 | 880.2674 | 870.4 | 857 | NA | NA | 1378 | NA | NA | 1357 | 849 | NA | 51 |
1401 | ||||||||||||||
1407 | ||||||||||||||
3-Hexenol | 544-12-7 | ─ | ─ | NA | NA | NA | NA | NA | NA | NA | NA | NA | 41 | |
113 | Cumin alcohol | 536-60-7 | 1204.248 | 1262.4 | 1287 | NA | NA | 2099 | NA | NA | NA | NA | NA | 41 |
114 | Biphenyl | 92-52-4 | 1283.923 | 1486.7 | 1381 | NA | 1385 | NA | NA | NA | NA | NA | NA | 51 |
115 | Naphthalene | 91-20-3 | 1131.721 | 1316 | 1179 | NA | NA | NA | NA | NA | 1718 | NA | NA | 51 |
Fig. 3 shows a plot of the RI of the external set predicted by CORAL software versus the RI predicted by Rojas et al..32 There is good agreement between the external predictions by the two methods.
![]() | ||
Fig. 3 Plot of the RI of external set predicted by CORAL software versus the RI predicted by Rojas et al. |
Footnote |
† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d3ra07960k |
This journal is © The Royal Society of Chemistry 2024 |