**1. Introduction**

Phenols are widely present in the environment as building blocks for plants [1]. They are formed naturally from decomposition of leaves and wood as well as through human activity like water purification processes [2]. Alkylphenols are a family of organic compounds obtained by the alkylation of phenols. The term is usually reserved for major industrial compounds such as propylphenol, amylphenol, heptylphenol, octylphenol, nonylphenol, dodecylphenol, and other long-chain carbon compounds. Methylphenols and ethylpenols are also alkylphenols, but are more often referred to by their specific names, cresols and xylenols, respectively. The alkylated phenols have a good ability to be adsorbed on

solid materials and some are toxic to fish and other forms of aquatic environment. Very low concentrations of these molecules have unfavorable effects on the taste and odor of water and fish [3].

predictive power for an external test set. We accordingly propose quantitative models, using stepwise multiple linear regression (MLR) for 2D-QSRR analysis and the partial least squares (PLS) for 3D-QSRR model, and we try to interpret the retention property of the compounds relying on the multidimensional-QSRR

*2D- and 3D-QSRR Studies of Linear Retention Indices for Volatile Alkylated Phenols*

The reliability of the 2D-QSRR analysis is depending on the available data set, and the method of analysis and the validations. In the present analysis, a series of 29 selected alkylated phenols that have been evaluated for their linear retention indices was taken from literature, and as reported in the literature [14], high-resolution GC/O (HRGC/O) analyses were performed with a type 5160 gas chromatograph (Carlo Erba), and the analyses were accomplished using DB-1701, as demonstrated by Czerny et al. [14]. We considered to carry out the 2D-QSRR analysis: 24 molecules are selected to propose the quantitative model (training set) and 5 compounds that have been selected randomly and were not used in training set have served to test the performance of the proposed model (test set). **Table 1** shows the studied

**No. Compound LRI Log(LRI) No. Compound LRI Log(LRI)** 1a Phenol 1167 3.067 16 4-n-hexylphenol 1784 3.251 **Monoalkylated phenols** 17 4-n-heptylphenol 1886 3.276 2 2-Methylphenol 1244 3.095 18 4-n-octylphenol 1994 3.300

4 4-Methylphenol 1269 3.103 19 2,3-Dimethylphenol 1387 3.142 5 2-Ethylphenol 1330 3.124 20 2,4-Dimethylphenol 1344 3.128 6 3-Ethylphenol 1371 3.137 21 2,5-Dimethylphenol 1342 3.128 7 4-Ethylphenol 1369 3.136 22 2,6-Dimethylphenol 1300 3.114 8 2-n-propylphenol 1415 3.151 23 3,4-Dimethylphenol 1406 3.148 9<sup>a</sup> 3-n-propylphenol 1463 3.165 24 3,5-Dimethylphenol 1369 3.136

11<sup>a</sup> 2-Isopropylphenol 1414 3.150 25 2,3,5-Trimethylphenol 1483 3.171 12 3-Isopropylphenol 1418 3.152 26 2,3,6-Trimethylphenol 1440 3.158 13 4-Isopropylphenol 1419 3.152 27 2,4,5-Trimethylphenol 1472 3.168 14 4-n-Butylphenol 1571 3.196 28<sup>a</sup> 2,4,6-Trimethylphenol 1400 3.146 15 4-n-Pentylphenol 1678 3.225 29<sup>a</sup> 3,4,5-Trimethylphenol 1551 3.191

compounds and the experimental linear retention indices values (LRI).

3 3-Methylphenol 1270 3.104 **Dimethylated phenols**

10 4-n-propylphenol 1463 3.165 **Trimethylated phenols**

*Alkylated phenols used in this study and their experimental linear retention indices.*

analyses [13].

*2.1.1 Data set*

**2. Material and methods**

*DOI: http://dx.doi.org/10.5772/intechopen.89576*

**2.1 2D-QSRR study**

*LRI: linear retention indices.*

*a Test set.*

**157**

**Table 1.**

All phenolic compounds can be considered as important parameters of the organoleptic (color, flavor, and aroma) and nutritional qualities of food products. The phenolic compounds which participate in the vegetable aroma are relatively simple volatile compounds whose odors can be pleasant or unpleasant. Vanilla, for example, is the most popular aroma in the world, and its production is estimated at 1500 tons per year [4]. Approximately 250 compounds are responsible for vanilla aroma and among these are about 20 phenolic compounds, the most abundant of which are vanillin, p-hydroxybenzaldehyde, and vanillic acid [5]. The spices we use to enhance taste and flavor of food contain volatile compounds characterized by the presence of a methoxyl group. 4-vinyl guaiacol is responsible for the pleasant odors that occur during the manufacture and storage of citrus juices (orange and grapefruit in particular). This compound is formed from the degradation of ferulic acid, and the quality of the orange juice aroma is directly related to changes in free ferulic acid and 4-vinyl guaiacol contents [6]. These two compounds are also produced during the thermal degradation of lignin. With their derivatives (4-methyl guaiacol, 4-ethyl guaiacol, vanillin, vanillic acid, etc.), they are at the origin of the aroma developed by the smoking techniques used in meat and fish conservation [7].

Some alkylated phenols represent another group of compounds with a constantly weak odor. In addition, some individual odorants in this group have been described in several studies as having various sensory properties. Because of their obviously high odor potency, the odor thresholds of the alkylated phenols have been extensively evaluated.

The multidimensional quantitative structure-activity/property relationship (multidimensional-QSAR/QSPR) analysis is a computational method used to predict biological activities or chemical properties of existing or supposed chemical compounds. With incessant development, the multidimensional-QSAR/QSPR analyses have made notable achievement in diverse fields, such as toxicology and medicinal chemistry [8, 9]. Through the fast progress of computer science and theoretical study, it can quickly and accurately find molecular information (chemical descriptors) of compounds by computation. These chemical descriptors used in the construction of the QSAR/QSPR models can increase the interpretability and can predict the activity/property of new molecules [10].

The release of odorant molecules from a solid or liquid medium and their passage in the vapor phase is the first step before a possible perception due to the activation of the olfactory receptors present in the nasal cavity followed by a series of complex neurophysiological reactions, in order to code a particular smell, that's why in this study, a series of 29 volatile alkylated phenols, including monoalkylated phenols and di- and trimethylphenols, were subjected to a quantitative structure retention relationships (QSRR) studies, we have developed two- and threedimensional quantitative structure retention relationships (2D- and 3D-QSRR) for a series of 29 molecules odorants based on phenol. We construct 2D-QSRR model using 28 descriptors. The 3D-QSAR/QSPR models were constructed using the comparative molecular field analysis (CoMFA) [11] tools that collect and interpret complex data from series of bioactive molecules to construct computational models that correlate chemical properties with biological activity/propriety [12]. Through this approach, molecular features responsible for the retention property of the investigated compounds (alkylated phenols) were identified using the CoMFA contour plots. Furthermore, the statistical consistency of the developed models was evaluated on the basis of their correlation ability for the training set, as well as their
